Journal articles on the topic 'Advanced prognostic model'
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Uneno, Yu, Tadayuki Kou, Masashi Kanai, Michio Yamamoto, Peng Xue, Yukiko Mori, Yasushi Kudo, et al. "Prognostic model for survival in patients with advanced pancreatic cancer receiving palliative chemotherapy." Journal of Clinical Oncology 33, no. 3_suppl (January 20, 2015): 248. http://dx.doi.org/10.1200/jco.2015.33.3_suppl.248.
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248 Background: The prognosis of patients with advanced pancreatic cancer (APC) is extremely poor. Several clinical and laboratory factors have been known to be associated with prognosis of APC patients. However, there are few clinically available prognostic models predicting survival in APC patients receiving palliative chemotherapy. Methods: To construct a prognostic model to predict survival in APC patients receiving palliative chemotherapy, we analyzed the clinical data from 306 consecutive patients with pathologically confirmed APC who received palliative chemotherapy. We selected six independent prognostic factors which remained independent prognostic factors after multivariate analysis. Thereafter, we rounded the regression coefficient (β) for each independent prognostic factor derived from the Cox regression equation (HR = eβ) and developed a prognostic index (PI). Results: Developed prognostic index (PI) was as follows: PI = 2 (if performance status score 2–3) + 1 (if metastatic disease) + 1 (if initially unresectable disease) + 1 (if carcinoembryonic antigen level ≥5.0 ng/ml) + 1 (if carbohydrate antigen 19-9 level ≥1000 U/ml) + 2 (if neutrophil–lymphocyte ratio ≥5). The patients were classified into three prognostic groups: favorable (PI 0–1, n = 73), intermediate (PI 2–3, n = 145), and poor prognosis (PI 4–8, n = 88). The median overall survival for each prognostic group was 16.5, 12.3 and 6.2 months, respectively, and the 1-year survival rates were 67.3%, 51.3%, and 19.1%, respectively (P < 0.01). The c index of the model was 0.658. This model was well calibrated to predict 1-year survival, in which overestimation (2.4% and 0.2% in the favorable and poor prognosis groups, respectively) and underestimation (3.6% in the intermediate prognosis group) were observed. Conclusions: This prognostic model based on readily available clinical factors would help clinicians in estimating the overall survival in APC patients receiving palliative chemotherapy.
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Hum, Allyn, Yoko Kin Yoke Wong, Choon Meng Yee, Chung Seng Lee, Huei Yaw Wu, and Mervyn Yong Hwang Koh. "PROgnostic Model for Advanced Cancer (PRO-MAC)." BMJ Supportive & Palliative Care 10, no. 4 (April 4, 2019): e34-e34. http://dx.doi.org/10.1136/bmjspcare-2018-001702.
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ObjectiveTo develop and validate a simple prognostic tool for early prediction of survival of patients with advanced cancer in a tertiary care setting.DesignProspective cohort study with 2 years’ follow-up.SettingSingle tertiary teaching hospital in Singapore.ParticipantsThe study includes consecutive patients diagnosed with advanced cancer who were referred to a palliative care unit between 2013 and 2015 (N=840). Data were randomly split into training (n=560) and validation (n=280) sets.Results743 (88.5%) patients died with a mean follow-up of 97.0 days (SD 174.0). Cox regression modelling was used to build a prognostic model, cross-validating with six randomly split dataset pairs. Predictor variables for the model included functional status (Palliative Performance Scale, PPS V.2), symptoms (Edmonton Symptom Assessment System, ESASr), clinical assessment (eg, the number of organ systems with metastasis, serum albumin and total white cell count level) and patient demographics. The area under the receiver operating characteristic curve using the final averaged prognostic model was between 0.69 and 0.75. Our model classified patients into three prognostic groups, with a median survival of 79.0 days (IQR 175.0) for the low-risk group (0–1.5 points), 42.0 days (IQR 75.0) for the medium-risk group (2.0–5.5 points), and 15.0 days (IQR 28.0) for the high-risk group (6.0–10.5 points).ConclusionsPROgnostic Model for Advanced Cancer (PRO-MAC) takes into account patient and disease-related factors and identify high-risk patients with 90-day mortality. PPS V.2 and ESASr are important predictors. PRO-MAC will help physicians identify patients earlier for supportive care, facilitating multidisciplinary, shared decision-making.
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Liu, Lin, Karen Messer, John A. Baron, David A. Lieberman, Elizabeth T. Jacobs, Amanda J. Cross, Gwen Murphy, Maria Elena Martinez, and Samir Gupta. "A prognostic model for advanced colorectal neoplasia recurrence." Cancer Causes & Control 27, no. 10 (August 12, 2016): 1175–85. http://dx.doi.org/10.1007/s10552-016-0795-5.
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Kim, Jung Hoon, Sung Yong Oh, Jung Hun Kang, Myoung-Hee Kang, Chi-Young Jeong, and Jun Ho Ji. "The prognostic significance of the advanced lung cancer inflammation index(ALI) in patients with advanced biliary tract cancer: A retrospective study." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e16613-e16613. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16613.
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e16613 Background: Intrahepatic cholangiocarcinoma is the second most common primary cancer, but the prognosis is poor and aggressive therapies provide only limited benefit when it is in advanced stage. Many studies were conducted to find significant prognostic factors to predict clinical outcomes, but there is no definitive parameter or scoring model yet in this disease. This study is a retrospective analysis to test various kinds of prognostic scoring systems including the advanced lung cancer inflammation (ALI) index, a new prognostic model, for patients treated for advanced intrahepatic cholangiocarcinoma. Methods: We retrospectively searched medical records of patients who were diagnosed with advanced intrahepatic cholangiocarcinoma between January 2012 and December 2017 and actively treated at a single tertiary regional cancer center. Patients who received only supportive care, or had active infection concomitantly were excluded. 60 patients were identified, and we reviewed and analyzed their baseline characteristics and clinical outcomes. Various types of inflammation or nutritional scoring systems, neutrophil to lymphocyte ratio (NLR), Onodera’s prognostic nutritional index (OPNI), the advanced lung cancer inflammation (ALI) index were concurrently calculated and significance for patients’ survival was evaluated. Results: In 50 patients with metastatic stage and 10 patients with locally advanced disease, the median overall survival was 13.97 months (95% confidence interval (CI) 7.99 – 19.95). ALI of 28.5 was determined as the optimal cut-off value for prediction of 1-year survival by receiver operating characteristics (ROC) curve with an AUC value of 0.671 (sensitivity and specificity were 77.8% and 60.6%, respectively). 26 patients with ALI > 28.5 had significantly better overall survival compared with 34 patients with ALI≤28.5 (19.2 months vs. 8.73 months, p= .007). NLR above 2.8 only showed mildly significant difference for overall survival (OS), and the OPNI failed to predict prognosis of these patients. In multivariate analysis, there was no independent prognostic factor observed to have association with overall survival. Conclusions: The advanced lung cancer inflammation (ALI) index showed a potential to be a new prognostic model for advanced intrahepatic cholangiocarcinoma. Further cohort studies to expand population size may be worth.
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Pellegrini, Fabio, Massimiliano Copetti, Maria Pia Sormani, Francesca Bovis, Carl de Moor, Thomas PA Debray, and Bernd C. Kieseier. "Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling." Multiple Sclerosis Journal 26, no. 14 (November 5, 2019): 1828–36. http://dx.doi.org/10.1177/1352458519887343.
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Background: There is an unmet need for precise methods estimating disease prognosis in multiple sclerosis (MS). Objective: Using advanced statistical modeling, we assessed the prognostic value of various clinical measures for disability progression. Methods: Advanced models to assess baseline prognostic factors for disability progression over 2 years were applied to a pooled sample of patients from placebo arms in four different phase III clinical trials. least absolute shrinkage and selection operator (LASSO) and ridge regression, elastic nets, support vector machines, and unconditional and conditional random forests were applied to model time to clinical disability progression confirmed at 24 weeks. Sensitivity analyses for different definitions of a combined endpoint were carried out, and bootstrap was used to assess prediction model performance. Results: A total of 1582 patients were included, of which 434 (27.4%) had disability progression in a combined endpoint over 2 years. Overall model discrimination performance was relatively poor (all C-indices ⩽ 0.65) across all models and across different definitions of progression. Conclusion: Inconsistency of prognostic factor importance ranking confirmed the relatively poor prediction ability of baseline factors in modeling disease progression in MS. Our findings underline the importance to explore alternative predictors as well as alternative definitions of commonly used endpoints.
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Chen, Chen Hsiu, Su Ching Kuo, and Siew Tzuh Tang. "Current status of accurate prognostic awareness in advanced/terminally ill cancer patients: Systematic review and meta-regression analysis." Palliative Medicine 31, no. 5 (August 4, 2016): 406–18. http://dx.doi.org/10.1177/0269216316663976.
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Background: No systematic meta-analysis is available on the prevalence of cancer patients’ accurate prognostic awareness and differences in accurate prognostic awareness by publication year, region, assessment method, and service received. Aim: To examine the prevalence of advanced/terminal cancer patients’ accurate prognostic awareness and differences in accurate prognostic awareness by publication year, region, assessment method, and service received. Design: Systematic review and meta-analysis. Methods: MEDLINE, Embase, The Cochrane Library, CINAHL, and PsycINFO were systematically searched on accurate prognostic awareness in adult patients with advanced/terminal cancer (1990–2014). Pooled prevalences were calculated for accurate prognostic awareness by a random-effects model. Differences in weighted estimates of accurate prognostic awareness were compared by meta-regression. Results: In total, 34 articles were retrieved for systematic review and meta-analysis. At best, only about half of advanced/terminal cancer patients accurately understood their prognosis (49.1%; 95% confidence interval: 42.7%–55.5%; range: 5.4%–85.7%). Accurate prognostic awareness was independent of service received and publication year, but highest in Australia, followed by East Asia, North America, and southern Europe and the United Kingdom (67.7%, 60.7%, 52.8%, and 36.0%, respectively; p = 0.019). Accurate prognostic awareness was higher by clinician assessment than by patient report (63.2% vs 44.5%, p < 0.001). Conclusion: Less than half of advanced/terminal cancer patients accurately understood their prognosis, with significant variations by region and assessment method. Healthcare professionals should thoroughly assess advanced/terminal cancer patients’ preferences for prognostic information and engage them in prognostic discussion early in the cancer trajectory, thus facilitating their accurate prognostic awareness and the quality of end-of-life care decision-making.
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Graham, Jeffrey, Daniel Y. C. Heng, James Brugarolas, and Ulka Vaishampayan. "Personalized Management of Advanced Kidney Cancer." American Society of Clinical Oncology Educational Book, no. 38 (May 2018): 330–41. http://dx.doi.org/10.1200/edbk_201215.
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The treatment of renal cell carcinoma represents one of the great success stories in translational cancer research, with the development of novel therapies targeting key oncogenic pathways. These include drugs that target the VEGF and mTOR pathways, as well as novel immuno-oncology agents. Despite the therapeutic advancements, there is a paucity of well-validated prognostic and predictive biomarkers in advanced kidney cancer. With a number of highly effective therapies available across multiple lines, it will become increasingly important to develop a more tailored approach to treatment selection. Prognostic clinical models, such the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model, are routinely used for prognostication in clinical practice. The IMDC model has demonstrated a predictive capability in the context of these treatments including immune checkpoint inhibition. A number of promising molecular markers and gene expression signatures are being explored as prognostic and predictive biomarkers, but none are ready to be widely used for treatment selection. In this review, we will explore the current landscape of personalized care in metastatic renal cell carcinoma. This will include a focus on both prognostic and predictive factors as well as clinical applications of biology in kidney cancer.
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Redman, J. R., G. R. Petroni, P. E. Saigo, N. L. Geller, and T. B. Hakes. "Prognostic factors in advanced ovarian carcinoma." Journal of Clinical Oncology 4, no. 4 (April 1986): 515–23. http://dx.doi.org/10.1200/jco.1986.4.4.515.
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Nineteen factors were analyzed for prognostic significance in a series of 89 women with advanced (stage III or IV) ovarian carcinoma treated with chemotherapy after initial debulking surgery. Seventy-eight of these women received cyclophosphamide, Adriamycin (Adria Laboratories, Columbus, Ohio), and cisplatin (CAP) treatment, and 11 received cyclophosphamide initially with Adriamycin and cisplatin administered at the time of recurrence. Median survival and remission duration were 25 and 19 months, respectively. Using survival as an end point, significant prognostic factors in univariate analyses included the total residual mass after debulking (P = .0007), largest residual mass after debulking (P = .0008), and stage (P = .0098). Using remission duration as an end point, significant prognostic factors in univariate analyses included total residual mass after debulking (P = .007) and the largest residual mass after debulking (P = .0020). The prognostic variables were then considered as possible predictors of survival in a multivariate analysis using the Cox proportional hazards model resulting in the following expression: lambda i(t)/lambda o(t) = exp(0.5928 (log TRM - 1.8117) + 0.6450 (stage - 0.3827) + 0.6673 (C4 - 0.4198) - 0.8596 (CAP - 0.8642)), where lambda i(t)/lambda o(t) is the risk of dying for a particular patient compared with the average risk of the entire group; log TRM is the log of the volume of the total residual mass in cm3 plus 1.0; stage = 0 if stage III, 1 if stage IV; C4 = 0 if cytologic grade is 1, 2, or 3 and 1 if grade 4; CAP = 0 if treatment is cyclophosphamide and 1 if CAP. Median survival times of patients with relative risk greater than 1 and less than 1 are 43 and 19 months respectively. If this model is confirmed in a prospective study, then it could be used to assign risk and assess treatment options for similar patients at diagnosis.
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Schmidt, Rebecca J., Daniel L. Landry, Lewis Cohen, Alvin H. Moss, Cheryl Dalton, Brian H. Nathanson, and Michael J. Germain. "Derivation and validation of a prognostic model to predict mortality in patients with advanced chronic kidney disease." Nephrology Dialysis Transplantation 34, no. 9 (November 5, 2018): 1517–25. http://dx.doi.org/10.1093/ndt/gfy305.
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Abstract Background Guiding patients with advanced chronic kidney disease (CKD) through advance care planning about future treatment obliges an assessment of prognosis. A patient-specific integrated model to predict mortality could inform shared decision-making for patients with CKD. Methods Patients with Stages 4 and 5 CKD from Massachusetts (749) and West Virginia (437) were prospectively evaluated for clinical parameters, functional status [Karnofsky Performance Score (KPS)] and their provider’s response to the Surprise Question (SQ). A predictive model for 12-month mortality was derived with the Massachusetts cohort and then validated externally on the West Virginia cohort. Logistic regression was used to create the model, and the c-statistic and Hosmer–Lemeshow statistic were used to assess model discrimination and calibration, respectively. Results In the derivation cohort, the SQ, KPS and age were most predictive of 12-month mortality with odds ratios (ORs) [95% confidence interval (CI)] of 3.29 (1.87–5.78) for a ‘No’ response to the SQ, 2.09 (95% CI 1.19–3.66) for fair KPS and 1.41 (95% CI 1.15–1.74) per 10-year increase in age. The c-statistic for the 12-month mortality model for the derivation cohort was 0.80 (95% CI 0.75–0.84) and for the validation cohort was 0.74 (95% CI 0.66–0.83). Conclusions Our integrated prognostic model for 12-month mortality in patients with advanced CKD had good discrimination and calibration. This model provides prognostic information to aid nephrologists in identifying and counseling advanced CKD patients with poor prognosis who are facing the decision to initiate dialysis or pursue medical management without dialysis.
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Chen, Zhan-Hong, Jin-Xiang Lin, Qu Lin, Xing Li, Ying-Fen Hong, and Xiang-yuan Wu. "A new prognostic model based on total tumor volume to predict survival rate in locally advanced hepatocellular carcinoma patients." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e15622-e15622. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e15622.
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e15622 Background: Many HCC patients are diagnosed as locally advanced and loco-regional therapies improved the prognosis of these patients. Survival rates of these patients vary differently. We want to research on the prognostic factors and establish a new prognostic model based on total tumor volume(TTV) to predict the survival rate in locally advanced hepatocellular carcinoma patients who receive loco-regional therapy. Methods: 214 locally advanced HCC patients who received TACE or PEJ were retrospectively studied. Univariate and multivariate analyses were used to assess the variables. A new prognostic model based on TTV was developed with independent prognostic factors. The predictive value was evaluated using receiver operator characteristic curve (ROC) analyses. Results: The median survival is 23.4 months. Univariate and multivariate analyses showed that total tumor volume (TTV), child-pugh garde, protal vein thrombosis and antivirus therapy were independent factors of overall survival. A risk model was establsihed and it is consisted of 4 factors mentioned above. 214 patients were classified into 5 stages by the new staging system. We researched on the predictive value of the new prognostic system by comparing it with CLIP, BCLC and TNM. When predicting 1-year OS, AUC of the new prognostic system, CLIP, BCLC and TNM is 0.620, 0.735, 0.527 and 0.655, respectively; CLIP is best when predicting 1-year os. When predicting 2-year OS, AUC of the new prognostic system, CLIP, BCLC and TNM is 0.623, 0.671, 0.516 and 0.577, respectively;The new prognostic system is as good as CLIP. When predicting 3-year OS, AUC of the new prognostic system, CLIP, BCLC and TNM is 0.623, 0.645, 0.504 and 0.593, respectively; The new prognostic system is as good as CLIP and is better than BCLC(P < 0.05). Conclusions: Total tumor volume is independent prognotic factors of locally advanced hepatocellular carcinoma patients who received locoregional therapies.The new prognostic system based on TTV is as good as CLIP in predicting 2-year OS and 3-year OS. A new prognostic system based on TTV TTV(CM3) < 22.5(0) ≥22.5(1) Antivirus therapy No(0) Yes(1) Child pugh Grade A(0) B(1) Portal vein thrombosis No(0) Yes(1)
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Kang, Haeyoun, Min Chul Choi, Sewha Kim, Ju-Yeon Jeong, Ah-Young Kwon, Tae-Hoen Kim, Gwangil Kim, et al. "USP19 and RPL23 as Candidate Prognostic Markers for Advanced-Stage High-Grade Serous Ovarian Carcinoma." Cancers 13, no. 16 (August 6, 2021): 3976. http://dx.doi.org/10.3390/cancers13163976.
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Ovarian cancer is one of the leading causes of deaths among patients with gynecological malignancies worldwide. In order to identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. In order to predict the prognosis of patients with HGSC, we created machine learning-based models and identified USP19 and RPL23 as candidate prognostic markers. Specifically, patients with lower USP19 mRNA levels and those with higher RPL23 mRNA levels had worse prognoses. This model was then used to analyze the data of patients with HGSC hosted on The Cancer Genome Atlas; this analysis validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of USP19 and RPL23 determined using a machine-learning model could serve as prognostic markers for patients with HGSC receiving conventional therapy.
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Meng, Jianwen, Qun Huang, Yifeng Huang, Zida Yang, Kang Chen, Ana Wang, Kaimin Chang, and Liping Liang. "Predictive Value of Positron Emission Tomography/CT Imaging in Distant Metastasis in Early and Locally Advanced Lung Cancer." Journal of Medical Imaging and Health Informatics 11, no. 5 (May 1, 2021): 1372–77. http://dx.doi.org/10.1166/jmihi.2021.3381.
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The imaging features of advanced non-small cell lung cancer identified by imaging approaches are particularly critical for the clinical prognosis and treatment. Our study explores the predictive value of Positron Emission Tomography–Computed Tomography (PET/CT) imaging in distant metastasis in early and locally advanced lung cancer. From September 2017 to September 2019, 121 patients with PET images were enrolled. 80 patient’s data were used to simulate the cohort and the data on 41 patients were used to validate the cohort. Quantitative PET image features were assessed. A Cox model was used to predict the distant metastasis. A combination of imaging and histology type prognostic models was also evaluated. The best prognostic model includes two features, which can quantify intratumoral heterogeneity. In independent validation cohort, the consistency index of model was 0.71. This prognostic model allows high-risk and low-risk group with distant metastases (hazard ratio 4.8, P < 0.05). The ROC curve revealed a separation of the combined imaging and histology type among high-and low-risk group. The consistency index was also improved to 0.80. PET features related to distant metastases have been identified and these features may help develop appropriate treatment options for early and locally advanced lung cancer patients.
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Park, Hyung Soon, Ji Soo Park, Yun Ho Roh, Jieun Moon, Dong Sup Yoon, and Hei-Cheul Jeung. "Prognostic factors and scoring model for survival in advanced biliary tract cancer." Journal of Clinical Oncology 35, no. 4_suppl (February 1, 2017): 264. http://dx.doi.org/10.1200/jco.2017.35.4_suppl.264.
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264 Background: Metastatic biliary tract cancer (BTC) has dismal prognosis. We herein presented multivariate analysis using routinely evaluated clinico-laboratory parameters at the time of initial diagnosis, to implement a scoring model that can effectively identify risk groups, and we finally validated the model using independent dataset. Methods: From September 2006 to February 2015, 482 patients with metastatic BTC were analyzed. Patients were randomly assigned (7:3) into investigational (n = 340) and validation dataset (n = 142). Continuous variables were dichotomized according to the normal range or the best cutoff values statistically determined by Contal and O’Quigley method. Multivariate analysis using Cox’s proportional hazard model was done to find independent prognostic factors, and scoring model were derived by summing the rounded χ2 scores for the factors emerged in the multivariate analysis. Results: Performance status (ECOG 3-4), hypoalbuminemia ( < 3.4 mg/dL), carcinoembryonic antigen (≥9 ng/mL), neutrophil-lymphocyte ratio (≥3.0), and carbohydrate antigen 19-9 (≥120 U/mL) were identified as independent factors for poor survival in investigational dataset. When assigning patients into three risk groups based on these factors, survival was 14.0, 7.3, and 2.3 months for the low, intermediate, and high-risk groups, respectively (P < 0.001). Harrell’s C-index and integrated AUC for scoring model were 0.682 and 0.653, respectively. In validation dataset, prognosis was also well-divided according to the risk groups (median OS, 16.7, 7.5 and 1.9 months, respectively, P < 0.001). Chemotherapy gave a survival benefit in low and intermediate-risk group (11.4 vs. 4.8 months; P< 0.001), but not in high-risk group (median OS, 4.3 vs. 1.1 months; P = 0.105). Conclusions: We propose a set of prognostic criteria for metastatic BTC, which can help accurate patient risk stratification and aid in treatment selection.
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Chow, Edward, Mohamed Abdolell, Tony Panzarella, Kristin Harris, Andrea Bezjak, Padraig Warde, and Ian Tannock. "Predictive Model for Survival in Patients With Advanced Cancer." Journal of Clinical Oncology 26, no. 36 (December 20, 2008): 5863–69. http://dx.doi.org/10.1200/jco.2008.17.1363.
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Purpose To derive and validate a simple predictive model for survival of patients with metastatic cancer attending a palliative radiotherapy clinic. Patients and Methods We described previously a model predicting survival of patients referred for palliative radiotherapy using six prognostic factors: primary cancer site, site of metastases, Karnofsky performance score (KPS), and the fatigue, appetite, and shortness of breath subscales from the Edmonton Symptom Assessment Scale. Here we simplified the model to include only three factors: primary cancer site, site of metastases, and KPS. Each factor was assigned a value proportional to its prognostic weight, and the weighted scores for each patient were summed to obtain a survival prediction score (SPS). Patients were also grouped according to their number of risk factors (NRF): nonbreast cancer, metastases other than bone, and KPS ≤ 60. The three- and six- variable models were evaluated for their ability to predict survival in patients referred during a different time period and of those referred to a different cancer center. Results A training set of 395 patients, a temporal validation set of 445 patients, and an external validation set of 467 patients were used. The ability of the three- and six-variable models to separate patients into three prognostic groups and to predict their survival was similar using both SPS and NRF methods in the training, temporal, and external validation data sets. There was no statistically significant difference in the performance of the models. Conclusion The three-variable NRF model is preferred because of its relative simplicity.
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Kumbhaj, Prashant, Vishesh Gumdal, Ankur Punia, Deepak Yadlapalli, Rakesh Taran, and Prakash Chitalkar. "Prognostic model in locally advanced and metastatic gall bladder cancer." Annals of Oncology 28 (June 2017): iii59. http://dx.doi.org/10.1093/annonc/mdx261.154.
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Mandrekar, Sumithra J., Steven E. Schild, Shauna L. Hillman, Katie L. Allen, Randolph S. Marks, James A. Mailliard, James E. Krook, et al. "A prognostic model for advanced stage nonsmall cell lung cancer." Cancer 107, no. 4 (2006): 781–92. http://dx.doi.org/10.1002/cncr.22049.
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Stone, Patrick, Anastasia Kalpakidou, Chris Todd, Jane Griffiths, Vaughan Keeley, Karen Spencer, Peter Buckle, Dori-Anne Finlay, Victoria Vickerstaff, and Rumana Z. Omar. "Prognostic models of survival in patients with advanced incurable cancer: the PiPS2 observational study." Health Technology Assessment 25, no. 28 (May 2021): 1–118. http://dx.doi.org/10.3310/hta25280.
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Background The Prognosis in Palliative care Study (PiPS) prognostic survival models predict survival in patients with incurable cancer. PiPS-A (Prognosis in Palliative care Study – All), which involved clinical observations only, and PiPS-B (Prognosis in Palliative care Study – Blood), which additionally required blood test results, consist of 14- and 56-day models that combine to create survival risk categories: ‘days’, ‘weeks’ and ‘months+’. Objectives The primary objectives were to compare PIPS-B risk categories against agreed multiprofessional estimates of survival and to validate PiPS-A and PiPS-B. The secondary objectives were to validate other prognostic models, to assess the acceptability of the models to patients, carers and health-care professionals and to identify barriers to and facilitators of clinical use. Design This was a national, multicentre, prospective, observational, cohort study with a nested qualitative substudy using interviews with patients, carers and health-care professionals. Setting Community, hospital and hospice palliative care services across England and Wales. Participants For the validation study, the participants were adults with incurable cancer, with or without capacity to consent, who had been recently referred to palliative care services and had sufficient English language. For the qualitative substudy, a subset of participants in the validation study took part, along with informal carers, patients who declined to participate in the main study and health-care professionals. Main outcome measures For the validation study, the primary outcomes were survival, clinical prediction of survival and PiPS-B risk category predictions. The secondary outcomes were predictions of PiPS-A and other prognostic models. For the qualitative substudy, the main outcomes were participants’ views about prognostication and the use of prognostic models. Results For the validation study, 1833 participants were recruited. PiPS-B risk categories were as accurate as agreed multiprofessional estimates of survival (61%; p = 0.851). Discrimination of the PiPS-B 14-day model (c-statistic 0.837, 95% confidence interval 0.810 to 0.863) and the PiPS-B 56-day model (c-statistic 0.810, 95% confidence interval 0.788 to 0.832) was excellent. The PiPS-B 14-day model showed some overfitting (calibration in the large –0.202, 95% confidence interval –0.364 to –0.039; calibration slope 0.840, 95% confidence interval 0.730 to 0.950). The PiPS-B 56-day model was well-calibrated (calibration in the large 0.152, 95% confidence interval 0.030 to 0.273; calibration slope 0.914, 95% confidence interval 0.808 to 1.02). PiPS-A risk categories were less accurate than agreed multiprofessional estimates of survival (p < 0.001). The PiPS-A 14-day model (c-statistic 0.825, 95% confidence interval 0.803 to 0.848; calibration in the large –0.037, 95% confidence interval –0.168 to 0.095; calibration slope 0.981, 95% confidence interval 0.872 to 1.09) and the PiPS-A 56-day model (c-statistic 0.776, 95% confidence interval 0.755 to 0.797; calibration in the large 0.109, 95% confidence interval 0.002 to 0.215; calibration slope 0.946, 95% confidence interval 0.842 to 1.05) had excellent or reasonably good discrimination and calibration. Other prognostic models were also validated. Where comparisons were possible, the other prognostic models performed less well than PiPS-B. For the qualitative substudy, 32 health-care professionals, 29 patients and 20 carers were interviewed. The majority of patients and carers expressed a desire for prognostic information and said that PiPS could be helpful. Health-care professionals said that PiPS was user friendly and may be helpful for decision-making and care-planning. The need for a blood test for PiPS-B was considered a limitation. Limitations The results may not be generalisable to other populations. Conclusions PiPS-B risk categories are as accurate as agreed multiprofessional estimates of survival. PiPS-A categories are less accurate. Patients, carers and health-care professionals regard PiPS as potentially helpful in clinical practice. Future work A study to evaluate the impact of introducing PiPS into routine clinical practice is needed. Trial registration Current Controlled Trials ISRCTN13688211. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 28. See the NIHR Journals Library website for further project information.
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Vernerey, Dewi, Pascal Hammel, Sophie Paget-Bailly, Florence Huguet, Jean Luc Van Laethem, David Goldstein, Bengt Glimelius, et al. "Prognosis model for overall survival in locally advanced unresecable pancreatic carcinoma: An ancillary study of the LAP 07 trial." Journal of Clinical Oncology 33, no. 3_suppl (January 20, 2015): 235. http://dx.doi.org/10.1200/jco.2015.33.3_suppl.235.
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235 Background: The management of locally advanced pancreatic cancer (LAPC) patients remains controversial and complex. Better discrimination for Overall Survival (OS) is needed to improve therapeutic decisions. We address this issue, with the largest Phase III cohort of LAPC, by establishing the first prognosis model for OS with the full spectrum of parameters currently available at diagnosis. Methods: We enrolled 442 LAPC patients recruited in LAP07, an international multicenter randomized phase III trial (NCT00634725). 30 baseline variables among demographic, cancer history, clinical, biological and radiological parameters were evaluated in univariate and multivariate analyses as prognostic factors for OS. The predictive value of the final model was evaluated with Harrell’s C index. This analysis was repeated 1,000 times with the use of bootstrap sample to derive 95%CI for the C. A prognostic score and nomogramm were then developed based on the identified prognostic factors in the final model. Results: Independent prognostic factors identified in multivariate analysis (n=370) for OS were: Age (HR= 1.01; 95%CI 1.00 - 1.03; p=0.0418), Pain (HR= 1.36 ; 95%CI 1.08 - 1.71; p=0.0094), Albumin (HR= 0.96; 95%CI 0.94 - 0.98; p=0.0001), and RECIST size (HR= 1.01; 95%CI 1.00 - 1.02; p=0.0033), Harrell’s C-statistic for the final model was 0.60 (95% bootstrap CI 0.56 0.63). A prognostic score between 0 and 4 was then calculated for each patient, based on the previous model. Three risk-groups for death could be identified: lower risk (n=17; median OS time = 18.8 months; group of reference); intermediate risk (n=166 ;median OS time = 13.4 months; HR=1.7); higher risk (n=187 ; median OS time = 11.8 months; HR=2.1); p = 0.0101 by the global log rank test. A score and nomogramm were also developped with the addition of CA19.9 information. Conclusions: Our results highlighted 4 OS’s independent pronostic factors among a broad spectrum of parameters at time of diagnosis. We identified 3 groups with different OS’s prognosis profile. The determination of this simple prognostic score allows risk stratification that may help guiding clinical management of patients and the design for future clinical trials.
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Tu, Xiao, Na Luo, Yiqi Lv, Bijuan Wang, and Yayu Li. "Prognostic evaluation model of diabetic nephropathy patients." Annals of Palliative Medicine 10, no. 6 (June 2021): 6867–72. http://dx.doi.org/10.21037/apm-21-1454.
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Kim, Sung Gon, Bang Wool Eom, Hongman Yoon, Young-Woo Kim, and Keun Won Ryu. "Prognostic Value of Preoperative Systemic Inflammatory Parameters in Advanced Gastric Cancer." Journal of Clinical Medicine 11, no. 18 (September 9, 2022): 5318. http://dx.doi.org/10.3390/jcm11185318.
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Background: The predictive value of various systemic inflammatory parameters has been reported. However, it is still unclear which inflammatory parameters are the best predictors of prognosis in advanced gastric cancer and what are their mechanisms of action. The aim of this study was to evaluate the association between preoperative systemic inflammatory parameters and overall survival (OS) in patients with advanced gastric cancer. Methods: This retrospective study included 489 patients with stage II/III advanced gastric cancer treated at the National Cancer Center, Republic of Korea, between January 2012 and December 2015. We divided the patients into survivors and non-survivors and compared their clinicopathological characteristics. Univariate and multivariate analyses using the Cox proportional hazards model were performed to evaluate the prognostic value of inflammatory parameters. Results: The absolute lymphocyte count was significantly higher in survivors (2.07 ± 0.62 × 103/µL vs. 1.88 ± 0.63 × 103/µL, p = 0.001). The neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) were marginally lower in survivors. Survival analysis revealed that the NLR and PLR were independent prognostic factors for OS. Survival was significantly different depending on NLR and PLR in the same pathologic stages. Conclusions: NLR and PLR were independent prognostic factors for OS in patients with advanced gastric cancer. Regarding single inflammatory parameters, an elevated lymphocyte count was the only factor associated with a favorable prognosis. These results suggest that the enhanced immune function of patients affects their prognosis more than the increased systemic inflammatory response.
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Kunisaki, Chikara, Masazumi Takahashi, Hidetaka Ono, Takashi Oshima, Shoichi Fujii, Takashi Kosaka, Hirochika Makino, Hirotoshi Akiyama, and Itaru Endo. "Use of inflammation-based prognostic score to predict survival in patients with advanced gastric cancer receiving biweekly docetaxel and S-1 combination chemotherapy." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): e14624-e14624. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e14624.
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e14624 Background: The Glasgow Prognostic Score (GPS), an inflammation-based prognostic score composed of C-reactive protein (CRP) and albumin measurements, has been reported to be a prognostic factor in patients with various cancers. This study was conducted to determine the prognostic value of GPS for patients with advanced cancer. Methods: The GPS was classified according to a previous study. A total of 83 advanced gastric cancer patients receiving bi-weekly docetaxel/S1 treatment (DS) were included. Correlation of clinicopathological factors and the GPS was assessed. To identify the impact of GPS as prognostic factors for disease-specific survival (DSS) and progression-free survival (PFS), univariate and multivariate analyses were performed. Results: Of these 83 patients, unresectable tumors were observed in 78 patients and recurrent tumors were detected in 5 patients. Of these, 13 patients underwent surgery and 12 patients underwent gastrectomy. There were significant correlations between the GPS and the neutrophil to lymphocyte ratio (NLR). Univariate analysis revealed that the GPS, ECOG-PS and gastrectomy after DS treatment significantly affected prognosis. The Cox proportional regression hazard model showed that the GPS, age and gastrectomy independently influenced DSS, and that the GPS and gastrectomy also influenced PFS. The Cox proportional regression hazard model restricted patients without gastrectomy showed that the GPS and age independently influenced DSS, and that the GPS influenced PFS. Conclusions: The GPS may be an useful prognostic factor for advanced gastric cancer patients receiving uniform first-line treatment (DS). The impact of the GPS should be confirmed in a well-designed prospective trial in many patients.
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Jerry Teng, Chieh-Lin, Tran-Der Tan, Yun-Yi Pan, Yu-Wen Lin, Pei-Wen Lien, Hsin-Chun Chou, Peng-Hsu Chen, and Fang-Ju Lin. "Prognostic Factors for Clinical Outcomes in Patients with Newly Diagnosed Advanced-stage Hodgkin Lymphoma: A Nationwide Retrospective Study." Cancer Control 29 (January 2022): 107327482211248. http://dx.doi.org/10.1177/10732748221124865.
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Introduction While Hodgkin lymphoma (HL) is mostly curable, outcomes for advanced-stage HL remain unsatisfactory. The International Prognostic Score and its modifications were developed to predict HL prognosis; however, more straightforward prognostic factors are needed. This study aimed to identify simpler prognostic factors for advanced-stage newly diagnosed HL (NDHL). Methods This retrospective study used the Taiwan National Health Insurance Research Database and the Taiwan Cancer Registry. Patients with advanced-stage NDHL receiving ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or ABVD-like regimens between 2009 and 2016 were enrolled. Cox proportional hazards models were used to identify prognostic factors for the time to next treatment (TTNT) and overall survival (OS). We used the time-dependent area under the receiver operating characteristic curve (AUROC) to evaluate model performance. Results The study included 459 patients with advanced-stage NDHL. A bimodal age distribution (peaks 20-44 and >65 years) was observed. Over a median follow-up of 4.7 years, the complete remission and OS rates were 52% and 76%, respectively. Age ≥60 years (adjusted hazard ratio [aHR]: 1.73, 95% confidence interval [CI]: 1.23-2.43), extranodal involvement (1.40, 1.05-1.87), B symptoms (1.53, 1.13-2.06), and Charlson Comorbidity Index (CCI) ≥1 (1.49, 1.08-2.06) were significantly associated with a shorter TTNT. The time-dependent AUROC was .65. With a time-dependent AUROC of .81, age ≥60 years (4.55, 2.90-7.15) and CCI ≥1 (1.86, 1.18-2.91) were risk factors for worse OS. Conclusion Older age and more comorbidities were risk factors for an inferior OS in advanced-stage NDHL, while older age, extranodal involvement, B-symptoms, and higher CCI were significantly associated with disease relapse.
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Wang, Tao, Rong Lu, Sunny Lai, Joan H. Schiller, Fang Liz Zhou, Bo Ci, Stacy Wang, et al. "Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer." Cancer Informatics 18 (January 2019): 117693511983754. http://dx.doi.org/10.1177/1176935119837547.
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Importance: Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials. Objective: The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort. Design: 1817 patients with advanced NSCLC from the control arms of 4 Phase III randomized clinical trials were included in this study. Data from 524 NSCLC patients from one of these trials were used to validate a previously published nomogram and then used to develop an updated nomogram. Patients from the other 3 trials were used as independent validation cohorts of the new nomogram. The prognostic performances were comprehensively evaluated using hazard ratios, integrated area under the curve (AUC), concordance index, and calibration plots. Setting: General community. Main outcome: A nomogram model was developed to predict overall survival in NSCLC patients. Results: We demonstrated the prognostic power of the previously published model in an independent cohort. The updated prognostic model contains the following variables: sex, histology, performance status, liver metastasis, hemoglobin level, white blood cell counts, peritoneal metastasis, skin metastasis, and lymphocyte percentage. This model was validated using various evaluation criteria on the 3 independent cohorts with heterogeneous NSCLC populations. In the SUN1087 patient cohort, the continuous risk score output by the nomogram achieved an integrated area under the receiver operating characteristics (ROC) curve of 0.83, a log-rank P-value of 3.87e−11, and a concordance index of 0.717. In the SAVEONCO patient cohort, the integrated area under the ROC curve was 0.755, the log-rank P-value was 4.94e−6 and the concordance index was 0.678. In the VITAL patient cohort, the integrated area under the ROC curve was 0.723, the log-rank P-value was 1.36e−11, and the concordance index was 0.654. We implemented the proposed nomogram and several previously published prognostic models on an online Web server for easy user access. Conclusions: This nomogram model based on basic clinical features and routine lab testing predicts individual survival probabilities for advanced NSCLC and exhibits cross-study robustness.
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Gettelman, A., H. Morrison, S. Santos, P. Bogenschutz, and P. M. Caldwell. "Advanced Two-Moment Bulk Microphysics for Global Models. Part II: Global Model Solutions and Aerosol–Cloud Interactions*." Journal of Climate 28, no. 3 (February 1, 2015): 1288–307. http://dx.doi.org/10.1175/jcli-d-14-00103.1.
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Abstract A modified microphysics scheme is implemented in the Community Atmosphere Model, version 5 (CAM5). The new scheme features prognostic precipitation. The coupling between the microphysics and the rest of the model is modified to make it more flexible. Single-column tests show the new microphysics can simulate a constrained drizzling stratocumulus case. Substepping the cloud condensation (macrophysics) within a time step improves single-column results. Simulations of mixed-phase cases are strongly sensitive to ice nucleation. The new microphysics alters process rates in both single-column and global simulations, even at low (200 km) horizontal resolution. Thus, prognostic precipitation can be important, even in low-resolution simulations where advection of precipitation is not important. Accretion dominates as liquid water path increases in agreement with cloud-resolving model simulations and estimates from observations. The new microphysics with prognostic precipitation increases the ratio of accretion over autoconversion. The change in process rates appears to significantly reduce aerosol–cloud interactions and indirect radiative effects of anthropogenic aerosols by up to 33% (depending on substepping) to below 1 W m−2 of cooling between simulations with preindustrial (1850) and present-day (2000) aerosol emissions.
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Liu, Lili, Haoming Wan, Li Liu, Jie Wang, Yibo Tang, Shaoguo Cui, and Yongmei Li. "Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer." Diagnostics 13, no. 4 (February 16, 2023): 748. http://dx.doi.org/10.3390/diagnostics13040748.
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This study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a total of 185 patients with pathologically confirmed HGSOC. A total of 185 patients were randomly assigned in a 5:3:2 ratio to a training cohort (n = 92), validation cohort 1 (n = 56), and validation cohort 2 (n = 37). We built a new deep learning network from 3839 preoperative MRI images (T2-weighted images and diffusion-weighted images) to extract HGSOC prognostic indicators. Following that, a fusion model including clinical and deep learning features is developed to predict patients’ individual recurrence risk and 3-year recurrence likelihood. In the two validation cohorts, the consistency index of the fusion model was higher than both the deep learning model and the clinical feature model (0.752, 0.813 vs. 0.625, 0.600 vs. 0.505, 0.501). Among the three models, the fusion model had a higher AUC than either the deep learning model or the clinical model in validation cohorts 1 or 2 (AUC = was 0.986, 0.961 vs. 0.706, 0.676/0.506, 0.506). Using the DeLong method, the difference between them was statistically significant (p < 0.05). The Kaplan–Meier analysis distinguished two patient groups with high and low recurrence risk (p = 0.0008 and 0.0035, respectively). Deep learning may be a low-cost, non-invasive method for predicting risk for advanced HGSOC recurrence. Deep learning based on multi-sequence MRI serves as a prognostic biomarker for advanced HGSOC, which provides a preoperative model for predicting recurrence in HGSOC. Additionally, using the fusion model as a new prognostic analysis means that can use MRI data can be used without the need to follow-up the prognostic biomarker.
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Huo, Teh-Ia, Shu-Yein Ho, and Po-Hong Liu. "Selecting an optimal prognostic model for advanced hepatocellular carcinoma: Any new ideas?" Digestive and Liver Disease 53, no. 9 (September 2021): 1208–9. http://dx.doi.org/10.1016/j.dld.2021.04.025.
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Jang, Raymond W., Valerie B. Caraiscos, Nadia Swami, Subrata Banerjee, Ernie Mak, Ebru Kaya, Gary Rodin, et al. "Simple Prognostic Model for Patients With Advanced Cancer Based on Performance Status." Journal of Oncology Practice 10, no. 5 (September 2014): e335-e341. http://dx.doi.org/10.1200/jop.2014.001457.
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Bologna, Marco, Valentina Corino, Giuseppina Calareso, Chiara Tenconi, Salvatore Alfieri, Nicola Alessandro Iacovelli, Anna Cavallo, et al. "Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients." Cancers 12, no. 10 (October 13, 2020): 2958. http://dx.doi.org/10.3390/cancers12102958.
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Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.
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Roychowdhury, D. F., A. Hayden, and A. M. Liepa. "Health-Related Quality-of-Life Parameters as Independent Prognostic Factors in Advanced or Metastatic Bladder Cancer." Journal of Clinical Oncology 21, no. 4 (February 15, 2003): 673–78. http://dx.doi.org/10.1200/jco.2003.04.166.
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Purpose: This retrospective analysis examined prognostic significance of health-related quality-of-life (HRQoL) parameters combined with baseline clinical factors on outcomes (overall survival, time to progressive disease, and time to treatment failure) in bladder cancer. Patients and Methods: Outcome and HRQoL (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30) data were collected prospectively in a phase III study assessing gemcitabine and cisplatin versus methotrexate, vinblastine, doxorubicin, and cisplatin in locally advanced or metastatic bladder cancer. Prespecified baseline clinical factors (performance status, tumor-node-metastasis staging, visceral metastases [VM], alkaline phosphatase [AP] level, number of metastatic sites, prior radiotherapy, disease measurability, sex, time from diagnosis, and sites of disease) and selected HRQoL parameters (global QoL; all functional scales; symptoms: pain, fatigue, insomnia, dyspnea, anorexia) were evaluated using Cox’s proportional hazards model. Factors with individual prognostic value (P < .05) on outcomes in univariate models were assessed for joint prognostic value in a multivariate model. A final model was developed using a backward selection strategy. Results: Patients with baseline HRQoL were included (364 of 405, 90%). The final model predicted longer survival with low/normal AP levels, no VM, high physical functioning, low role functioning, and no anorexia. Positive prognostic factors for time to progressive disease were good performance status, low/normal AP levels, no VM, and minimal fatigue; for time to treatment failure, they were low/normal AP levels, minimal fatigue, and no anorexia. Global QoL was a significant predictor of outcome in univariate analyses but was not retained in the multivariate model. Conclusion: HRQoL parameters are independent prognostic factors for outcome in advanced bladder cancer; their prognostic importance needs further evaluation.
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Morine, Yuji, Mitsuo Shimada, Toru Utsunomiya, Satoru Imura, Tetsuya Ikemoto, Jun Hanaoka, Koji Sugimoto, Yu Saito, Shinichiro Yamada, and Michihito Asanoma. "Usefulness of gemcitabine combined with 5-fluorouracil and cisplatin (GFP) for patients with advanced biliary carcinoma as a postoperative adjuvant chemotherapy." Journal of Clinical Oncology 30, no. 4_suppl (February 1, 2012): 367. http://dx.doi.org/10.1200/jco.2012.30.4_suppl.367.
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367 Background: Prognosis of patients with advanced biliary carcinoma is still poor, and chemotherapy has been shown to have little impact. The aim of the present study was to clarify the effectiveness of GEM combined with CDDP and 5FU (GFP) therapy for advanced biliary carcinoma. The usefulness as a postoperative adjuvant chemotherapy using GFP (UMIN000006924) was evaluated. Methods: 1. Identification of independent poor prognostic factors: One hundred one patients with biliary carcinoma before induction of GFP chemotherapy, including intrahepatic cholangiocarcinoma (IHC: n=33), hilar cholangiocarcinoma (HC: n=29), and gallbladder cancer (GBC: n=39) were enrolled. The prognostic factors were investigated by multivariate analysis using Cox’s proportional hazard model. 2. Clinical usefulness of adjuvant GFP chemotherapy: One hundred forty-eight patients with biliary carcinoma (IHC: n=46, HC: n=43, GBC: n=39), who were positive for poor prognostic factors, received postoperative adjuvant GFP chemotherapy. The prognosis of these patients was compared to those not having postoperative adjuvant chemotherapy. Results: 1. In multivariate analysis, non-curative resection, lymph nodes metastasis and intrahepatic metastasis were identified as the independent prognostic factors. 2. Seventy-nine patients (53.3%) had poor prognostic factors. Of these, 22 patients received postoperative adjuvant GFP chemotherapy. The prognosis of patients with poor prognostic factors, who received postoperative adjuvant GFP chemotherapy, was significantly better than those not having such adjuvant chemotherapy (p<0.01) (3y survival: 59.6% vs. 18.9%). Furthermore, in patients with non-curative resection, postoperative adjuvant GFP chemotherapy also prolonged postoperative prognosis (p<0.01) (3y survival: 62.5% vs. 9.4%) Conclusions: Postoperative GFP adjuvant chemotherapy for patients with poor prognostic factors may improve the surgical outcomes.
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Sutar, Roshan, and Pooja Chaudhary. "Prognostic disclosure in cancer care: a systematic literature review." Palliative Care and Social Practice 16 (January 2022): 263235242211010. http://dx.doi.org/10.1177/26323524221101077.
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Background: Collusion in cancer care is the diplomatic concealment of information between a triad of the health care professional (HCP), patient, and caregiver. Free and expressive communication is determined by multiple factors, which establishes a healthy balance between ‘patient-centric’ and ‘family-centric’ decision making. The lack of a universal approach to prognostic disclosure techniques emphasizes the need for a systematic review of contemporary practice. Methods: A systematic review of the literature was conducted till June 2020 using themes based on cancer, communication, prognostic disclosure, and collusion by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Fifty-three studies involving 10,569 subjects were studied for their utility on prognostic disclosure using different communication methods and interfaces. Twenty-three studies used a face-to-face interview with subjects while in-person telephonic interviews were conducted in two studies, 16 studies implicated semi-structured questionnaires, and 6 studies mentioned the development of a new technique/tool for disclosure. The duration of a session for prognosis-disclosure ranged from 22 min to 1 h. The involvement of palliative care specialists and mental health professionals was limited during the disclosure of the prognosis. Conclusion: The findings of the review indicate that patients in cancer care are aware of their diagnosis and to a certain extent of prognosis despite nondisclosure by their family members and treating teams. This review emphasizes the assessment of ‘disclosure wishes’ among patients and caregivers in separate interviews rather than simply relying on one specific method of interviewing. The nonconfrontational approach and training among HCPs are of utmost importance to build therapeutic resilience among the treating team involved in cancer care. Since many factors such as family wishes, cultural dissonance, medical model, and patient perception could become barriers to prognostic disclosure, there is a need to develop a universal approach to prognostic disclosure and handling associated collusion.
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Cavalieri, Stefano, Mara S. Serafini, Andrea Carenzo, Silvana Canevari, Ruud H. Brakenhoff, C. René Leemans, Irene H. Nauta, et al. "Clinical Validity of a Prognostic Gene Expression Cluster-Based Model in Human Papillomavirus–Positive Oropharyngeal Carcinoma." JCO Precision Oncology, no. 5 (November 2021): 1666–76. http://dx.doi.org/10.1200/po.21.00094.
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PURPOSE Under common therapeutic regimens, the prognosis of human papillomavirus (HPV)–positive squamous oropharyngeal carcinomas (OPCs) is more favorable than HPV-negative OPCs. However, the prognosis of some tumors is dismal, and validated prognostic factors are missing in clinical practice. The present work aimed to validate the prognostic significance of our published three-cluster model and to compare its prognostic value with those of the 8th edition of the tumor-node-metastasis staging system (TNM8) and published signatures and clustering models. METHODS Patients with HPV DNA-positive OPCs with locoregionally advanced nonmetastatic disease treated with curative intent (BD2Decide observational study, NCT02832102 ) were considered as validation cohort. Patients were treated in seven European centers, with expertise in the multidisciplinary management of patients with head and neck cancer. The median follow-up was 46.2 months (95% CI, 41.2 to 50), and data collection was concluded in September 2019. The primary end point of this study was overall survival (OS). Three-clustering models and seven prognostic signatures were compared with our three-cluster model. RESULTS The study population consisted of 235 patients. The three-cluster model confirmed its prognostic value. Two-year OS in each cluster was 100% in the low-risk cluster, 96.6% in the intermediate-risk cluster, and 86.3% in the high-risk cluster ( P = .00074). For the high-risk cluster, we observed an area under the curve = 0.832 for 2-year OS, significantly outperforming TNM 8th edition (area under the curve = 0.596), and functional and biological differences were identified for each cluster. CONCLUSION The rigorous clinical selection of the cases included in this study confirmed the robustness of our three-cluster model in HPV-positive OPCs. The prognostic value was found to be independent and superior compared with TNM8. The next step includes the translation of the three-cluster model in clinical practice. This could open the way to future exploration of already available therapies in HPV-positive OPCs tailoring de-escalation or intensification according to the three-cluster model.
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Jiao, Yan, Yanqing Li, Peiqiang Jiang, Wei Han, and Yahui Liu. "PGM5: a novel diagnostic and prognostic biomarker for liver cancer." PeerJ 7 (June 11, 2019): e7070. http://dx.doi.org/10.7717/peerj.7070.
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Background Liver cancer is a common malignancy and a significant public health problem worldwide, but diagnosis and prognostic evaluation remain challenging for clinicians. Metabolic reprogramming is a hallmark of cancer, and we therefore examined the diagnostic and prognostic value of a metabolic enzyme, phosphoglucomutase-like protein 5 (PGM5), in liver cancer. Methods All data were from The Cancer Genome Atlas database. R and related statistical packages were used for data analysis. Hepatic PGM5 expression was determined in different groups, and the chi-squared test and Fisher’s exact test were used to determine the significance of differences. The pROC package was used to determine receiver operating characteristic (ROC) curves, the survival package was used to for survival analysis and development of a Cox multivariable model, and the ggplot2 package was used for data visualization. Results PGM5 expression was significantly lower in cancerous than adjacent normal liver tissues, and had modest diagnostic value based on ROC analysis and calculations of area under the curve (AUC). Hepatic PGM5 expression had positive associations with male sex and survival, but negative associations with advanced histologic type, advanced histologic grade, advanced stage, and advanced T classification. Patents with low PGM5 levels had poorer overall survival and relapse-free survival. PGM5 was independently associated with patient prognosis. Conclusion PGM5 has potential use as a diagnostic and prognostic biomarker for liver cancer.
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Bamias, Aristotelis, Axel Merseburger, Yohann Loriot, Nicholas James, Ernest Choy, Daniel Castellano, F. Lopez-Rios, et al. "New prognostic model in patients with advanced urothelial carcinoma treated with second-line immune checkpoint inhibitors." Journal for ImmunoTherapy of Cancer 11, no. 1 (January 2023): e005977. http://dx.doi.org/10.1136/jitc-2022-005977.
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BackgroundBellmunt Risk Score, based on Eastern Cooperative Oncology Group (ECOG) performance status (PS), hemoglobin levels and presence of liver metastases, is the most established prognostic algorithm for patients with advanced urothelial cancer (aUC) progressing after platinum-based chemotherapy. Nevertheless, existing algorithms may not be sufficient following the introduction of immunotherapy. Our aim was to develop an improved prognostic model in patients receiving second-line atezolizumab for aUC.MethodsPatients with aUC progressing after cisplatin/carboplatin-based chemotherapy and enrolled in the prospective, single-arm, phase IIIb SAUL study were included in this analysis. Patients were treated with 3-weekly atezolizumab 1200 mg intravenously. The development and internal validation of a prognostic model for overall survival (OS) was performed using Cox regression analyses, bootstrapping methods and calibration.ResultsIn 936 patients, ECOG PS, alkaline phosphatase, hemoglobin, neutrophil-to-lymphocyte ratio, liver metastases, bone metastases and time from last chemotherapy were identified as independent prognostic factors. In a 4-tier model, median OS for patients with 0–1, 2, 3–4 and 5–7 risk factors was 18.6, 10.4, 4.8 and 2.1 months, respectively. Compared with Bellmunt Risk Score, this model provided enhanced prognostic separation, with a c-index of 0.725 vs 0.685 and increment in c-statistic of 0.04 (p<0.001). Inclusion of PD-L1 expression did not improve the model.ConclusionsWe developed and internally validated a prognostic model for patients with aUC receiving postplatinum immunotherapy. This model represents an improvement over the Bellmunt algorithm and could aid selection of patients with aUC for second-line immunotherapy.Trial registration numberNCT02928406.
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Li, Xinghui, Yang Yu, Cheng Zheng, Yue Zhang, Chuandao Shi, Lei Zhang, and Hui Qiao. "Dynamic Nomogram for Predicting Long-Term Survival in Terms of Preoperative and Postoperative Radiotherapy Benefits for Advanced Gastric Cancer." International Journal of Environmental Research and Public Health 20, no. 3 (February 3, 2023): 2747. http://dx.doi.org/10.3390/ijerph20032747.
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Studies on the prognostic significance of preoperative radiotherapy (PERT) and postoperative radiotherapy (PORT) in patients with advanced gastric cancer (GC) remain elusive. The aim of the study was to evaluate the survival advantage of preoperative and postoperative radiotherapy and construct a dynamic nomogram model to provide customized prediction of the probability of prognostic events for advanced GC patients. We collected clinical records from 2010 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database with a specific target for stage II-IV GC patients treated with PERT or PORT. We used the least absolute shrinkage and selection operator (LASSO) regression model to identify factors that contribute to the overall survival (OS) of GC patients. The dynamic nomogram infographic was constructed based on the prognostic factors of tumor-specific survival. Out of the 3215 total patients (2271 [70.6%] male; median age, 61 [SD = 12] years), 1204 were in the PERT group and 2011 in the PORT group. Receiving PORT was associated with a survival advantage over PERT for stage II GC patients (HR = 0.791, 95% CI= 0.712–0.879, p < 0.001). The 1-, 3-, and 5-year OS rates were 89.9%, 63.8%, and 53.8% in the PORT group, whereas the corresponding rates were significantly lower in the PERT group (86.4%, 57.1%, and 44.3%, respectively, all p < 0.05). The survival prediction model demonstrated that patients aged > 65 years, with an advanced cancer development stage and tumor size >3 were independent risk factors for poor prognosis (all HR > 1, p < 0.05). In this study, a dynamic nomogram was established based on the LASSO model to provide a statistical basis for the clinical characteristics and predictive factors of advanced GC in a large population. PORT demonstrated significantly better treatment advantages than PERT for stage II GC patients.
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Ren, Zhixuan, Jiakang Zhang, Dayong Zheng, Yue Luo, Zhenghui Song, Fengsheng Chen, Aimin Li, and Xinhui Liu. "Identification of Prognosis-Related Oxidative Stress Model with Immunosuppression in HCC." Biomedicines 11, no. 3 (February 24, 2023): 695. http://dx.doi.org/10.3390/biomedicines11030695.
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For hepatocellular carcinoma (HCC) patients, we attempted to establish a new oxidative stress (OS)-related prognostic model for predicting prognosis, exploring immune microenvironment, and predicting the immunotherapy response. Significantly differently expressed oxidative stress-related genes (DEOSGs) between normal and HCC samples from the Cancer Genome Atlas (TCGA) were screened, and then based on weighted gene coexpression network analysis (WGCNA), HCC-related hub genes were discovered. Based on the least absolute shrinkage and selection operator (LASSO) and cox regression analysis, a prognostic model was developed. We validated the prognostic model’s predictive power using an external validation cohort: the International Cancer Genome Consortium (ICGC).Then a nomogram was determined. Furthermore, we also examined the relationship of the risk model and clinical characteristics as well as immune microenvironment. 434 DEOSGs, comprising 62 downregulated and 372 upregulated genes (p < 0.05 and |log2FC| ≥ 1), and 257 HCC-related hub genes were recognized in HCC. Afterward, we built a five-DEOSG (LOX, CYP2C9, EIF2B4, EZH2, and SRXN1) prognostic risk model. Using the nomogram, the risk model was shown to have good prognostic value. Compared to the low risk group, HCC patients with high risk had poorer outcomes, worse pathological grades, and advanced tumor stages (p < 0.05). There were significant increases in LOX, EIF2B4, EZH2, and SRXN1 expression in HCC samples, while CYP2C9 expression was decreased. Finally, Real-time PCR (RT-qPCR) confirmed the mRNA expressions of five genes (CYP2C9, EIF2B4, EZH2, SRXN1, LOX) in HCC cell lines. Our study constructed a prognostic OS-related model with strong predictive power and potential as an immunosuppressive biomarker for HCC leading to improving prediction and providing new insights for HCC immunotherapy.
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Zhang, Guanran, and Zhangzhe Yan. "A New Definition of Pyroptosis-Related Gene Markers to Predict the Prognosis of Lung Adenocarcinoma." BioMed Research International 2021 (November 26, 2021): 1–18. http://dx.doi.org/10.1155/2021/8175003.
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Pyroptosis, the prototype of programmed cell death, is crucial to the development of multicellular organisms. Lung cancer is one of the most lethal cancers in the world. Because lung cancer progresses quickly, it is mostly found at an advanced stage, resulting in a very poor prognosis of lung cancer. At present, there is no treatment with good prognosis, but pyroptosis-based tumor therapy may be able to solve this problem. In the past few decades, it has been found that pyroptosis can affect the invasion, proliferation, and metastasis of tumor and apoptosis is an important system to resist cancer. Our study is aimed at constructing a prognostic model within pyroptosis-related genes. We developed a prognostic model by using TCGA and GEO database, and differentially expressed genes (DEGs) were identified. Five genes (NLRP1, NOD1, NLRC4, CASP9, and PLCG1) were identified to construct a prognostic model. According to the median risk score calculated by our formula, we divided patients into the high- and low-risk groups. Pyroptosis-related genes play important roles in tumor immunity and can be used to predict the prognosis of lung adenocarcinoma (LUAD).
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He, Jian, Zhiqiang Mo, Qicong Mai, Feng Shi, and Xiaoming Chen. "A prognostic model to predict survival after hepatic arterial infusion chemotherapy for hepatocellular carcinoma with portal vein invasion." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e16157-e16157. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e16157.
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e16157 Background: We aimed to establish a prognostic model to predict survival for the patient with advanced hepatocellular carcinoma (HCC) after the treatment with hepatic arterial infusion chemotherapy (HAIC) of oxaliplatin plus fluorouracil/leucovorin. Methods: 164 patients diagnosed of HCC with portal vein invasion and treated with HAIC of oxaliplatin plus fluorouracil/leucovorin between 1/2018 and 1/2021 at the Guangdong provincial people’s hospital and were randomly divided to training(N=82) and validation(N=82) cohort. We investigated the impact of baseline characteristics and tumour load on overall survival (OS, log-rank test) and developed a prognostic model in the training cohort by using a stepwise Cox regression model and validated in validation cohort. Results: The final presentation of the model was “NCV-score= 0.974* serum neutrophil/lymphocyte ratio+ 1.239* Child-pugh stage+ 0.661* portal Vein invasion grade”, which further selected the first quartile of the linear predictor, namely 5.7, as cut-off values. The NCV-score differentiated two risk categories(≥5.7, ＜5.7) with distinct prognosis (median OS: 6.6 vs. 11.2 months, p <0.001). The prognostic model was used to develop nomogram for predicting individual survival of HAIC candidates and was validated in validation cohort to identify patients who obtain satisfying survival benefit for HAIC. Conclusions: The NCV-score identifies advanced HCC patients who obtain satisfying survival benefit for HAIC of oxaliplatin plus fluorouracil/leucovorin and provides individual survival predicting.[Table: see text]
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Yeo, Kwang-Hee, Ho Hyun Kim, Dong-Yi Kim, Young-Jin Kim, and Jae-Kyun Ju. "A Distribution Weighted Prognostic Scoring Model for Node Status in Advanced Rectal Cancer." Cancer Research and Treatment 46, no. 1 (January 15, 2014): 41–47. http://dx.doi.org/10.4143/crt.2014.46.1.41.
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Bandoh, Takafumi, Hiroshi Toyoshima, and Touru Isoyama. "Evaluation of Prognostic Factors of Advanced Gastric Cancer by Cox Proportional Hazards Model." Japanese Journal of Gastroenterological Surgery 26, no. 11 (1993): 2567–71. http://dx.doi.org/10.5833/jjgs.26.2567.
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Chen, Chuangzhen, Siqia Chen, Quynh-Thu Le, Jianzhou Chen, Zhijian Chen, Dongsheng Li, Mingzhen Zhou, and Derui Li. "Prognostic model for distant metastasis in locally advanced nasopharyngeal carcinoma after concurrent chemoradiotherapy." Head & Neck 37, no. 2 (March 25, 2014): 209–14. http://dx.doi.org/10.1002/hed.23583.
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Ganti, Apar Kishor, Xiaofei Wang, Thomas E. Stinchcombe, Yinpeng Wang, Jeffrey Bradley, Harvey J. Cohen, Karen Kelly, et al. "Clinical prognostic model for older patients with advanced non-small cell lung cancer." Journal of Geriatric Oncology 10, no. 4 (July 2019): 555–59. http://dx.doi.org/10.1016/j.jgo.2019.02.007.
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Chen, Zhan-Hong, Yingfen Hong, Xiao-kun Ma, Xing Li, Dong-hao Wu, Qu Lin, Min Dong, et al. "Identification of prognostic value of lymphocyte-to-monocyte ratio in patients with advanced HBV-associated hepatocellular carcinoma." Journal of Clinical Oncology 34, no. 4_suppl (February 1, 2016): 206. http://dx.doi.org/10.1200/jco.2016.34.4_suppl.206.
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206 Background: Inflammatory microenvironment plays an important role in the progression of HCC. Peripheral blood LMR, as a novel inflammatory biomarker combining an estimate of host immune homeostasis and tumor microenvironment, has been found to be a predictor for clinical outcomes in various malignancies. There have been no reports regarding the prognostic value of LMR in advanced HCC until now. We want to investigate the prognostic value of LMR in patients with advanced HBV-associated hepatocellular carcinoma. Methods: From September 2008 to June 2010, a total of 174 patients with HBV-associated advanced HCC without fever or signs of infections were analyzed. Clinicopathological parameters, including LMR, were evaluated to identify predictors of overall survival. Univariate and multivariate analyses were performed, using the Cox proportional hazards model. The best cutoff was determined with time-dependent receiver operating characteristic curve. Results: Univariate and multivariate analyses showed that LMR was an independent prognostic factor in overall survival in patients with advanced HCC(P < 0.01 ). The best cutoff point of LMR was 4.52. All patients were dichotomized into either a low LMR group( ≤ 4.52) or a high LMR group( > 4.52). Overall survival(OS) of high LMR group was significantly longer than that of low LMR group(P < 0.01 ). High LMR group patients had significantly higher 6-month OS rate(50% vs 23%, P < 0.01) than that of low LMR group patients. Higher LMR level was significantly correlated with the presence of metastasis and larger tumor size(P < 0.05). Conclusions: LMR is an independent prognostic factor of advanced HCC patients. Higher Baseline LMR levels indicates better prognosis.
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Wang, Fei, Chao Cheng, Shengnan Ren, Zhongyi Wu, Tao Wang, Xiaodong Yang, Changjing Zuo, Zhuangzhi Yan, and Zhaobang Liu. "Prognostic Evaluation Based on Dual-Time 18F-FDG PET/CT Radiomics Features in Patients with Locally Advanced Pancreatic Cancer Treated by Stereotactic Body Radiation Therapy." Journal of Oncology 2022 (July 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/6528865.
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Background. 18F-FDG PET/CT is widely used in the prognosis evaluation of tumor patients. The radiomics features can provide additional information for clinical prognostic assessment. Purpose. Purpose is to explore the prognostic value of radiomics features from dual-time 18F-FDG PET/CT images for locally advanced pancreatic cancer (LAPC) patients treated with stereotactic body radiation therapy (SBRT). Materials and Methods. This retrospective study included 70 LAPC patients who received early and delayed 18F-FDG PET/CT scans before SBRT treatment. A total of 1188 quantitative imaging features were extracted from dual-time PET/CT images. To avoid overfitting, the univariate analysis and elastic net were used to obtain a sparse set of image features that were applied to develop a radiomics score (Rad-score). Then, the Harrell consistency index (C-index) was used to evaluate the prognosis model. Results. The Rad-score from dual-time images contains six features, including intensity histogram, morphological, and texture features. In the validation cohort, the univariate analysis showed that the Rad-score was the independent prognostic factor ( p < 0.001 , hazard ratio [HR]: 3.2). And in the multivariate analysis, the Rad-score was the only prognostic factor ( p < 0.01 , HR: 4.1) that was significantly associated with the overall survival (OS) of patients. In addition, according to cross-validation, the C-index of the prognosis model based on the Rad-score from dual-time images is better than the early and delayed images (0.720 vs. 0.683 vs. 0.583). Conclusion. The Rad-score based on dual-time 18F-FDG PET/CT images is a promising noninvasive method with better prognostic value.
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Owusuaa, Catherine, Annemieke van der Padt-Pruijsten, Jan C. Drooger, Joan B. Heijns, Anne-Marie Dietvorst, Ellen C. J. Janssens-van Vliet, Daan Nieboer, Joachim G. J. V. Aerts, Agnes van der Heide, and Carin C. D. van der Rijt. "Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer." JAMA Network Open 5, no. 11 (November 30, 2022): e2244350. http://dx.doi.org/10.1001/jamanetworkopen.2022.44350.
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ImportanceTo optimize palliative care in patients with cancer who are in their last year of life, timely and accurate prognostication is needed. However, available instruments for prognostication, such as the surprise question (“Would I be surprised if this patient died in the next year?”) and various prediction models using clinical variables, are not well validated or lack discriminative ability.ObjectiveTo develop and validate a prediction model to calculate the 1-year risk of death among patients with advanced cancer.Design, Setting, and ParticipantsThis multicenter prospective prognostic study was performed in the general oncology inpatient and outpatient clinics of 6 hospitals in the Netherlands. A total of 867 patients were enrolled between June 2 and November 22, 2017, and followed up for 1 year. The primary analyses were performed from October 9 to 25, 2019, with the most recent analyses performed from June 19 to 22, 2022. Cox proportional hazards regression analysis was used to develop a prediction model including 3 categories of candidate predictors: clinician responses to the surprise question, patient clinical characteristics, and patient laboratory values. Data on race and ethnicity were not collected because most patients were expected to be of White race and Dutch ethnicity, and race and ethnicity were not considered as prognostic factors. The models’ discriminative ability was assessed using internal-external validation by study hospital and measured using the C statistic. Patients 18 years and older with locally advanced or metastatic cancer were eligible. Patients with hematologic cancer were excluded.Main Outcomes and MeasuresThe risk of death by 1 year.ResultsAmong 867 patients, the median age was 66 years (IQR, 56-72 years), and 411 individuals (47.4%) were male. The 1-year mortality rate was 41.6% (361 patients). Three prediction models with increasing complexity were developed: (1) a simple model including the surprise question, (2) a clinical model including the surprise question and clinical characteristics (age, cancer type prognosis, visceral metastases, brain metastases, Eastern Cooperative Oncology Group performance status, weight loss, pain, and dyspnea), and (3) an extended model including the surprise question, clinical characteristics, and laboratory values (hemoglobin, C-reactive protein, and serum albumin). The pooled C statistic was 0.69 (95% CI, 0.67-0.71) for the simple model, 0.76 (95% CI, 0.73-0.78) for the clinical model, and 0.78 (95% CI, 0.76-0.80) for the extended model. A nomogram and web-based calculator were developed to support clinicians in adequately caring for patients with advanced cancer.Conclusions and RelevanceIn this study, a prediction model including the surprise question, clinical characteristics, and laboratory values had better discriminative ability in predicting death among patients with advanced cancer than models including the surprise question, clinical characteristics, or laboratory values alone. The nomogram and web-based calculator developed for this study can be used by clinicians to identify patients who may benefit from palliative care and advance care planning. Further exploration of the feasibility and external validity of the model is needed.
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Ohno, Eiji, Miyuki Abe, Hitohiro Sasaki, and Kazuki Okuhiro. "Validation of 2 Prognostic Models in Hospitalized Patients With Advanced Hematological Malignancies in Japan." American Journal of Hospice and Palliative Medicine® 34, no. 3 (July 10, 2016): 258–62. http://dx.doi.org/10.1177/1049909115615567.
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Patients with advanced hematological malignancies are less likely to be referred to specialist palliative care services compared with patients having solid tumors. It has been reported that one of the most important reasons for the lack of referral is difficulties in the prognostication of terminally ill patients with hematologic malignancies. The study objective was to evaluate the predictive accuracy of the Palliative Prognostic Index (PPI) and the prognostic model developed by Kripp et al in hospitalized patients under the care of a hematologist. Using clinical charts, we retrospectively calculated the above scores. We reviewed the records of 114 patients admitted to the hematology ward. The inclusion criterion was patient with disease considered incurable using standard treatments. The prognostic models were assessed according to the original reports. Using PPI cutoff points of 2 and 4, we divided the patients into 3 groups of significantly different survival times ( P < .01). Moreover, we confirmed the usefulness of predicting survival <3 and <6 weeks using PPI scores of 6 and 4 as cutoff points, respectively. When we classified patients according to the prognostic model of Kripp et al, the high-risk group survived significantly shorter times than the intermediate- and low-risk groups ( P < .001). However, there was no significant difference in survival between the intermediate- and low-risk groups. Use of these models might enable physicians to provide more appropriate end-of-life care and to refer patients to palliative care earlier.
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Hernando-Cubero, Jorge, Natalia Alvarez-Garcia, Roberto A. Pazo Cid, Javier Martinez Trufero, Maria Alvarez, Juan Lao Romera, Esther Millastre, et al. "Prognostic models in advanced gastric cancer in first and second line chemotherapy treatment in Spanish population." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e15603-e15603. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e15603.
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e15603 Background: No globally accepted prognostic score has been developed in advanced gastric cancer (AGC). The purpose of this study is to explore baseline host or tumor related prognostic factors in spanish AGC patients in first and second line chemotherapy treatment. In addition we compare our scores with previously published scores in asian and european population. Methods: A total of 166 patients with AGC treated in our institution between 2012 and 2016 were screened. 119 received first line chemotherapy (CT) and 47 of them also received second line CT and were included in the analysis. Prognostic factors were evaluated using the Cox proportional hazard model. We use as comparators four first line and three second line scores published in literature. Results: The overal survival (OS) in first line and second line patients were 9 and 5 months. To construct first line CT score we selected four risk factors: ECOG≥2, Her2 negative, Irinotecan based CT and albumin < 3,6mg/dl. OS were 23 months in low risk group, who had zero or one risk points, 15 months for patients in the moderate risk group, who had two or three risk points, and 5 months for patients in the high risk group, who had all four risk points. In the second line CT score we included four risk factors: ECOG ≥2, albumin < 3.6mg/dl, Hb < 11.5mg/dl and CA19.9 reduction less than 30% after 2 CT cycles. OS were 30 months in low risk group, who had zero or one risk points, 16 months for patients in the moderate risk group, who had two or three risk points, and 3 months for patients in the high risk group, who had all four risk points. Conclusions: In the present study, we propose two new prognostic scores for patients with AGC developed in the same cohort and including HER2 status. This prognostic model could help clinicians choose and applicable treatment based on the stimated prognosis. [Table: see text]
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Kogo, Mari, Tomiko Sunaga, Shoko Nakamura, Takahiro Akita, Tatsuya Kurihara, Yusuke Shikama, Hiroaki Nakajima, Takashi Tobe, Keiichiro Yoneyama, and Yuji Kiuchi. "Prognostic Index for Survival in Patients with Advanced Non-Small-Cell Lung Cancer Treated with Third-Generation Agents." Chemotherapy 62, no. 4 (2017): 239–45. http://dx.doi.org/10.1159/000468508.
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We retrospectively evaluated clinical data from patients with advanced non-small-cell lung cancer (NSCLC) treated with third-generation chemotherapy agents prior to treatment, to determine a reliable method for predicting prognosis in such patients. We analyzed 100 patients who received third-generation agents (paclitaxel, docetaxel, gemcitabine, irinotecan, and vinorelbine) for the treatment of advanced NSCLC. Factors significantly related to prognosis were evaluated using the Cox regression model, and the prognostic index (PI) was determined by combining these factors. The mean follow-up duration was 12.6 months (0.2-67.0 months). Multivariate analysis identified pleural effusion, absolute neutrophil count (ANC), and C-reactive protein (CRP) level as significant factors that independently contribute to prognosis in patients with advanced NSCLC treated with third-generation agents (p < 0.05). The PI was calculated using these 3 factors, according to the following formula: PI = 0.581 × pleural effusion + 0.125 × ANC + 0.105 × CRP. The death rate in the group with the highest PI scores was significantly higher than in the group with the lowest scores (p < 0.001). Pleural effusion, ANC, and CRP level were the most important factors that contributed to prognosis following chemotherapy with third-generation agents in patients with advanced NSCLC. The PI is suggested to be an appropriate index to predict the prognosis of patients with NSCLC.
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Apolo, Andrea Borghese, George Philips, Irina Ostrovnaya, Jonathan E. Rosenberg, Matthew I. Milowsky, Eric Jay Small, Dean F. Bajorin, and Susan Halabi. "External validation of prognostic models for overall survival (OS) in patients (pts) with advanced cancer (UC) treated with cisplatin-based chemotherapy." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 4592. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.4592.
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4592 Background: The most commonly used model predicting OS for UC pts treated with cisplatin-based chemotherapy is based on 2-variables (visceral metastases and performance status), developed at MSKCC in 1999, and validated in a phase III study (DeSantis JCO 2011). A prognostic model of OS for advanced UC pts based on 4 variables (visceral metastases, albumin, performance status, and hemoglobin) was developed using 308 pts from MSKCC (ASCO 2007 abstr 5055). We report the discriminative ability of the 4- and 2- variable models for advanced UC pts using an independent dataset from CALGB 90102. Methods: The analysis was performed using an external multi-institutional dataset from CALGB 90102. The primary measurement of predictive discrimination was Harrell’s c-index which was computed with 95% confidence interval (CI). To assess whether there was a statistically significant difference in discrimination between the two models, the U statistic was used to test whether the predictions of the 4-variable model in all possible pairs were more concordant with actual observations than the 2-variable model in the same pairs. Results: CALGB 90102 included 74 UC pts (58 males, 16 females), median age 64 years, treated with cisplatin, gemcitabine and gefitinib, enrolled from 7/02 to 4/05 with a median follow-up of 72.5 months. Visceral metastases were present in 64% (bone, 18%, liver, 31%, lung, 43%), median KPS 90%. The MSKCC 2-variable risk group distribution was 30% =0, 65%= 1 and 5%=2. The median OS =12.7 months (95% CI=10.4-20.5) with 68 deaths observed. When applied to the CALGB cohort, the predictive accuracy for the 4- and 2-variable models were 0.63 (95 CI= 0.56- 0.69) and 0.58 (95% CI= 0.52-0.65), respectively. There was a statistically significant difference in discrimination between the two models (p =0.019), with superiority of the 4-variable model compared to the 2-variable model. Conclusions: A 4-variable prognostic nomogram for survival in pts with advanced UC was superior to a 2-variable risk-group model. The 4-variable prognostic model may replace the widely used 2-variable model and can be used in the design and conduct of future phase II and III trials in advanced UC.
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Takahari, Daisuke, Narikazu Boku, Satoru Iwasa, Junki Mizusawa, Tadayoshi Hashimoto, Takaki Yoshikawa, Shigenori Kadowaki, et al. "The new prognostic index of advanced gastric cancer using the data from JCOG1013." Journal of Clinical Oncology 41, no. 4_suppl (February 1, 2023): 342. http://dx.doi.org/10.1200/jco.2023.41.4_suppl.342.
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342 Background: We previously reported that performance status (PS) ≥1, no prior gastrectomy, number of metastatic sites ≥2, and high serum ALP level are poor prognosis factors and proposed a JCOG prognostic index by analyzing the data of advanced gastric cancer (AGC) patients enrolled in JCOG9912 (Takahari D et al. Oncologist 2014). Recently, Neutrocyte/ Lymphocyte Ratio (NLR) and diffuse type of cancer have also been reported to be prognostic factors of AGC. Methods: Prognostic factors were assessed in patients with AGC who enrolled in JCOG1013, the phase III study comparing docetaxel, cisplatin, and S-1 (DCS) versus cisplatin and S-1 (CS) in first-line treatment (Yamada Y et al. Lancet Gastroenterol Hepatol. 2019) was retrospectively evaluated using a Cox proportional regression model. The overall adequacy of risk prediction in the modified JCOG prognostic index and the original index were assessed by C-statistics. Results: Among 741 patients, 730 with all data for this analysis were selected. The median overall survival (OS) was 14.9 months (95% confidence interval [CI], 14.1–15.8). The median OS of the good (n=233), moderate (n=444), and poor (n=53) risk groups by the original JCOG index was 19.0, 14.2, and 9.1 months, respectively. The HRs compared with the good group were 1.59 [95% CI, 1.33–1.89; p < 0.0001] in the moderate group, and 2.47 [95% CI, 1.81–3.38; p < 0.0001] in the poor group with C-statistics of 0.572. In the multivariable analysis of 7 factors which showed p<0.1 in univariable analysis, PS ≥1 (hazard ratio [HR], 1.490; 95% CI, 1.264–1.755; p < 0.0001), diffuse type (HR, 1.337; 95% CI, 1.122–1.594; p = 0.012), number of metastatic sites ≥2 (HR, 1.256; 95% CI, 1.058–1.492; p = 0.0093), and NLR≥3.1 (HR, 1.539; 95% CI, 1.309–1.809; p < 0.0001) were significantly associated with worse prognosis, whereas no prior gastrectomy, peritoneal metastasis, and high serum ALP level were not. As the modified JCOG prognostic index, patients were classified into three groups according to the number of these four newly identified prognostic risk factors: good (no), moderate (1 or 2), and poor (3 or 4). Median OS of the good (n=221), moderate (n=441), and poor (n=68) risk groups was 20.6, 13.7, and 9.4 months, respectively. The HRs compared with the good group were 1.70 [95% CI, 1.42–2.03; p < 0.0001] in the moderate group, and 3.11 [95% CI, 2.33–4.14; p < 0.0001] in the poor group with C-statistics of 0.591. Three groups by the modified JCOG prognostic index also showed clear separation of OS in either subgroup of DCS or CS. Conclusions: Modified JCOG prognostic index, including NLR ≥3.1 and diffuse type instead of no prior gastrectomy and high serum ALP level, showed clear stratification of OS in JCOG1013.