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Rau, Cheng-Shyuan, Shao-Chun Wu, Jung-Fang Chuang, Chun-Ying Huang, Hang-Tsung Liu, Peng-Chen Chien, and Ching-Hua Hsieh. "Machine Learning Models of Survival Prediction in Trauma Patients." Journal of Clinical Medicine 8, no. 6 (June 5, 2019): 799. http://dx.doi.org/10.3390/jcm8060799.
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Background: We aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS). Methods: Enrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves. Results: In the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models. Conclusions: These four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.
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Riviere, Paul, Christopher Tokeshi, Jiayi Hou, Vinit Nalawade, Reith Sarkar, Anthony J. Paravati, Melody Schiaffino, Brent Rose, Ronghui Xu, and James D. Murphy. "Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–7. http://dx.doi.org/10.1200/cci.18.00111.
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PURPOSE Treatment decisions about localized prostate cancer depend on accurate estimation of the patient’s life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient’s health. METHODS We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model’s predictive capacity was compared with existing survival models with a metric of explained randomness (ρ2) ranging from 0 to 1, with 1 indicating an ideal prediction. RESULTS Our noncancer survival model included 143 covariates and had improved survival prediction (ρ2 = 0.60) compared with the Charlson comorbidity index (ρ2 = 0.26) and Elixhauser comorbidity index (ρ2 = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (ρ2 = 0.71) to the Memorial Sloan Kettering prediction model (ρ2 = 0.68). CONCLUSION Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.
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Copeland-Fields, L., T. Griffin, T. Jenkins, M. Buckley, and LC Wise. "Comparison of outcome predictions made by physicians, by nurses, and by using the Mortality Prediction Model." American Journal of Critical Care 10, no. 5 (September 1, 2001): 313–19. http://dx.doi.org/10.4037/ajcc2001.10.5.313.
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BACKGROUND: Critical care nurses must collaborate with physicians, patients, and patients' families when making decisions about aggressiveness of care. However, few studies address nurses' ability to predict outcomes. OBJECTIVES: To compare predictions of survival outcomes made by nurses, by physicians, and by using the Mortality Prediction Model. METHODS: Predictions of survival and function and attitudes toward aggressiveness of care based on the predictions were recorded on questionnaires in the emergency department by emergency and intensive care unit physicians and by intensive care unit nurses at the time of admission to the unit between February and September 1995 for 235 consecutive adult nontrauma patients. Scores on the Mortality Prediction Model were calculated on admission. Data on 85 of the 235 patients were analyzed by using descriptive, chi 2, and correlational statistics. Nurses' predictions of function were compared with patients' actual outcomes 6 months after admission. RESULTS: Nurses' predictions of survival were comparable to those of emergency physicians and superior to those obtained by using the objective tool. Years of nursing experience had no relationship to attitudes toward aggressiveness of care. Nurses accurately predicted functional outcomes in 52% of the followed-up cases. Intensive care physicians were more accurate than nurses and emergency physicians in predicting survival. All predictions made by clinicians were superior to those obtained by using the model. CONCLUSIONS: Nurses can predict survival outcomes as accurately as physicians do. Greater sensitivity and specificity are necessary before clinical judgment or predictive tools can be considered as screens for determining aggressiveness of care.
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Karakiewicz, Pierre I., Alberto Briganti, Felix K. H. Chun, Quoc-Dien Trinh, Paul Perrotte, Vincenzo Ficarra, Luca Cindolo, et al. "Multi-Institutional Validation of a New Renal Cancer–Specific Survival Nomogram." Journal of Clinical Oncology 25, no. 11 (April 10, 2007): 1316–22. http://dx.doi.org/10.1200/jco.2006.06.1218.
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Purpose We tested the hypothesis that the prediction of renal cancer–specific survival can be improved if traditional predictor variables are used within a prognostic nomogram. Patients and Methods Two cohorts of patients treated with either radical or partial nephrectomy for renal cortical tumors were used: one (n = 2,530) for nomogram development and for internal validation (200 bootstrap resamples), and a second (n = 1,422) for external validation. Cox proportional hazards regression analyses modeled the 2002 TNM stages, tumor size, Fuhrman grade, histologic subtype, local symptoms, age, and sex. The accuracy of the nomogram was compared with an established staging scheme. Results Cancer-specific mortality was observed in 598 (23.6%) patients, whereas 200 (7.9%) died as a result of other causes. Follow-up ranged from 0.1 to 286 months (median, 38.8 months). External validation of the nomogram at 1, 2, 5, and 10 years after nephrectomy revealed predictive accuracy of 87.8%, 89.2%, 86.7%, and 88.8%, respectively. Conversely, the alternative staging scheme predicting at 2 and 5 years was less accurate, as evidenced by 86.1% (P = .006) and 83.9% (P = .02) estimates. Conclusion The new nomogram is more contemporary, provides predictions that reach further in time and, compared with its alternative, which predicts at 2 and 5 years, generates 3.1% and 2.8% more accurate predictions, respectively.
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Schumacher, M., E. Graf, and T. Gerds. "How to Assess Prognostic Models for Survival Data: A Case Study in Oncology." Methods of Information in Medicine 42, no. 05 (2003): 564–71. http://dx.doi.org/10.1055/s-0038-1634384.
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Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.
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Zeng, Yuan, Wenhua Liang, and Jian He. "Association of postoperative radiotherapy with survival in patients with completely resected pathologic N2 non-small cell lung cancer treated with chemotherapy." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e21008-e21008. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e21008.
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e21008 Background: The current staging system for completely resected pathological N2 non-small-cell lung cancer (NSCLC) treated with chemotherapy is not suitable for predicting those patients most likely to benefit or not from post-operative radiotherapy (PORT). This study aimed to construct a survival prediction model that will enable individualized predictions of the net survival of PORT. Methods: A total of 3094 cases between 2002 and 2014 were extracted from the Surveillance, Epidemiology, and End Results databases. Patient characteristics were included as covariates, and their association for overall survival (OS) with and witout PORT was assessed. Externally validate data of 602 patients were included from China. Results: A total of 3696 patients were included for analysis. Age, gender, examined lymph node, positive lymph node, tumor size, extent of surgery, and visceral pleural invasion were significantly associated with OS, with P < .05. The calibration curve for OS showed great agreement between prediction by survival prediction model and actual observation.In the training cohort, the C-index for OS was 0.619 (95% CI, 0.598-0.641) in the PORT group and 0.627 (95% CI, 0.605-0.648) in the non-PORT group. In the externally validation cohort, the C-index for OS was 0.599 (95% CI, 0.485- 0.713) in the PORT group and 0.595 (95% CI, 0.544-0.646) in non-PORT group. The two survival prediction models were developed based on clinical variables to estimate an individual's net survival gain or not attributable to PORT. We found that PORT could improve OS (HR, 0.861; P = 0.044) for patients with a positive PORT net survival benefit. Conclusions: We established a practical survival prediction model that can be used to make individualized predictions about the expected survival of PORT or without PORT in patients with completely resected pathological N2 NSCLC, treated with chemotherapy.
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Hansen, Maiken B., Lone Ross Nylandsted, Morten A. Petersen, Mathilde Adsersen, Leslye Rojas-Concha, and Mogens Groenvold. "Patient-reported symptoms and problems at admission to specialized palliative care improved survival prediction in 30,969 cancer patients: A nationwide register-based study." Palliative Medicine 34, no. 6 (March 18, 2020): 795–805. http://dx.doi.org/10.1177/0269216320908488.
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Background: Large, nationally representative studies of the association between quality of life and survival time in cancer patients in specialized palliative care are missing. Aim: The aim of this study was to investigate whether symptoms/problems at admission to specialized palliative care were associated with survival and if the symptoms/problems may improve prediction of death within 1 week and 1 month, respectively. Setting/participants: All cancer patients who had filled in the EORTC QLQ-C15-PAL at admission to specialized palliative care in Denmark in 2010–2017 were included through the Danish Palliative Care Database. Cox regression was used to identify clinical variables (gender, age, type of contact (inpatient vs outpatient), and cancer site) and symptoms/problems significantly associated with survival. To test whether symptoms/problems improved survival predictions, the overall accuracy (area under the receiver operating characteristic curve) for different prediction models was compared. The validity of the prediction models was tested with data on 5,508 patients admitted to palliative care in 2018. Results: The study included 30,969 patients with an average age of 68.9 years; 50% were women. Gender, age, type of contact, cancer site, and most symptoms/problems were significantly associated with survival time. The predictive value of symptoms/problems was trivial except for physical function, which clearly improved the overall accuracy for 1-week and 1-month predictions of death when added to models including only clinical variables. Conclusion: Most symptoms/problems were significantly associated with survival and mainly physical function improved predictions of death. Interestingly, the predictive value of physical function was the same as all clinical variables combined (in hospice) or even higher (in palliative care teams).
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Chowdary, Raj P., Sean P. Campbell, Michael Rosenberg, and Norman E. Hugo. "Dermofluorometric Prediction of Flap Survival." Annals of Plastic Surgery 19, no. 2 (August 1987): 154–57. http://dx.doi.org/10.1097/00000637-198708000-00008.
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Fricker, Janet. "Survival prediction with neuroendocrine differentiation?" Lancet Oncology 7, no. 11 (November 2006): 891. http://dx.doi.org/10.1016/s1470-2045(06)70921-4.
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Tuhrim, Stanley, James M. Dambrosia, Thomas R. Price, Jay P. Mohr, Philip A. Wolf, Albert Heyman, and Carlos S. Kase. "Prediction of intracerebral hemorrhage survival." Annals of Neurology 24, no. 2 (August 1988): 258–63. http://dx.doi.org/10.1002/ana.410240213.
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Saporiti, A., M. Althabe, L. Landry, E. Gabai, E. Carmuega, and J. Mendilaharzu. "PSPI: PEDIATRIC SURVIVAL PREDICTION INDEX." Pediatric Research 26, no. 2 (August 1989): 165. http://dx.doi.org/10.1203/00006450-198908000-00037.
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Chen, Qi-Yue, Qing Zhong, Jun-Feng Zhou, Xian-Tu Qiu, Xue-Yi Dang, Li-Sheng Cai, Guo-Qiang Su, et al. "Development and External Validation of Web-Based Models to Predict the Prognosis of Remnant Gastric Cancer after Surgery: A Multicenter Study." Journal of Oncology 2019 (April 10, 2019): 1–15. http://dx.doi.org/10.1155/2019/6012826.
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Background. Remnant gastric cancer (RGC) is a rare malignant tumor with poor prognosis. There is no universally accepted prognostic model for RGC. Methods. We analyzed data for 253 RGC patients who underwent radical gastrectomy from 6 centers. The prognosis prediction performances of the AJCC7th and AJCC8th TNM staging systems and the TRM staging system for RGC patients were evaluated. Web-based prediction models based on independent prognostic factors were developed to predict the survival of the RGC patients. External validation was performed using a cohort of 49 Chinese patients. Results. The predictive abilities of the AJCC8th and TRM staging systems were no better than those of the AJCC7th staging system (c-index: AJCC7th vs. AJCC8th vs. TRM, 0.743 vs. 0.732 vs. 0.744; P>0.05). Within each staging system, the survival of the two adjacent stages was not well discriminated (P>0.05). Multivariate analysis showed that age, tumor size, T stage, and N stage were independent prognostic factors. Based on the above variables, we developed 3 web-based prediction models, which were superior to the AJCC7th staging system in their discriminatory ability (c-index), predictive homogeneity (likelihood ratio chi-square), predictive accuracy (AIC, BIC), and model stability (time-dependent ROC curves). External validation showed predictable accuracies of 0.780, 0.822, and 0.700, respectively, in predicting overall survival, disease-specific survival, and disease-free survival. Conclusions. The AJCC TNM staging system and the TRM staging system did not enable good distinction among the RGC patients. We have developed and validated visual web-based prediction models that are superior to these staging systems.
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Agrawal, Ankit, Sanchit Misra, Ramanathan Narayanan, Lalith Polepeddi, and Alok Choudhary. "Lung Cancer Survival Prediction using Ensemble Data Mining on Seer Data." Scientific Programming 20, no. 1 (2012): 29–42. http://dx.doi.org/10.1155/2012/920245.
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We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer. Carefully designed preprocessing steps resulted in removal/modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several supervised classification methods were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best prediction performance in terms of accuracy and area under the ROC curve. We have developed an on-line lung cancer outcome calculator for estimating the risk of mortality after 6 months, 9 months, 1 year, 2 year and 5 years of diagnosis, for which a smaller non-redundant subset of 13 attributes was carefully selected using attribute selection techniques, while trying to retain the predictive power of the original set of attributes. Further, ensemble voting models were also created for predicting conditional survival outcome for lung cancer (estimating risk of mortality after 5 years of diagnosis, given that the patient has already survived for a period of time), and included in the calculator. The on-line lung cancer outcome calculator developed as a result of this study is available at http://info.eecs.northwestern.edu:8080/LungCancerOutcomeCalculator/.
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Zhu, Linlin, Xiaoyang Han, Zhiwen Liu, Songze Leng, Ningning Shan, Xiao Lv, Kang Lu, Shouyong Hun, Yinhang Wu, and Xin Liu. "Survival prediction model for patients with mycosis fungoides/Sezary syndrome." Future Oncology 16, no. 31 (November 2020): 2487–98. http://dx.doi.org/10.2217/fon-2020-0502.
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Aim: A nomogram was constructed to forecast the overall survival (OS) of patients with mycosis fungoides/Sezary syndrome. Patients & methods: The clinicopathological information of patients was obtained from the Surveillance, Epidemiology and End Results (SEER) database. A model was established based on the independent prognostic factors. Predictive ability of the model was evaluated with the concordance index and calibration curves. Risk stratification was conducted for patients with similar tumor node metastasis (TNM) stages. Results: The model included 1997 eligible patients and seven prognostic factors for OS. The concordance index of the nomogram was 0.84 in the training and external validation cohorts, which indicated good predictive ability of the model and reliability of the results. The high agreement between the model predictions and actual observations was identified by calibration curves, which demonstrated the prediction accuracy of the model. Risk stratification displayed significant differences for patients with similar TNM stages, which suggested that the OS of patients with similar TNM stages could be further distinguished. Conclusion: We established a reliable nomogram to predict the OS of patients with mycosis fungoides/Sezary syndrome, which highlighted the advantages of nomograms over the conventional TNM staging system and promoted the application of individualized therapeutic strategies.
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Huang, Caiyun, and Changhua Yin. "DEEP LEARNING SURVIVAL PREDICTION FOR LUNG CANCER PATIENTS." Biomedical Engineering: Applications, Basis and Communications 33, no. 04 (May 4, 2021): 2150031. http://dx.doi.org/10.4015/s1016237221500319.
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Lung cancer is one of the most common cancers; lung cancer is a malignant tumor that seriously threatens the lives of patients. Improving survival prediction performance is meaningful for making the treatment plans and improving the survival rates of lung cancer patients. In this paper, an approach for predicting the survival of lung cancer patients is proposed based on pathological images. First, the deep learning method is used to automatically detect lung cancer cells in pathological pictures, and features of the detected lung cancer cells are extracted. In feature selection, an extraction method of topological features is given, it reflects the relationship and distribution characteristics of lung cancer cells, and the extracted topological features are used as predictive factors for survival analysis. In this paper, the extraction methods of global topological features are mainly studied; for example, the overall association, location relationship and distribution of cells, and the global topological features of lung cancer cells are extracted through the Voronoi diagram, Delaunay triangle, and minimum spanning tree methods. Finally, the Cox–LASSO method was used to predict the survival of lung cancer patients. Experimental results show that this method can improve the efficiency and accuracy of cell detection, and there is a higher ability to predict and analyze the survival of lung cancer patients.
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Senders, Joeky T., Patrick Staples, Alireza Mehrtash, David J. Cote, Martin J. B. Taphoorn, David A. Reardon, William B. Gormley, Timothy R. Smith, Marike L. Broekman, and Omar Arnaout. "An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning." Neurosurgery 86, no. 2 (October 5, 2019): E184—E192. http://dx.doi.org/10.1093/neuros/nyz403.
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Abstract BACKGROUND Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata. OBJECTIVE To compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator. METHODS Patients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socioeconomic, clinical, and radiographic features to predict overall survival, 1-yr survival status, and compute personalized survival curves. RESULTS In total, 20 821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance index = 0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/). CONCLUSION The development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with GBM. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.
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Rahman, Saqib, Robert Walker, Nick Maynard, Nigel Trudgill, Tom Crosby, David Cromwell, and Tim Underwood. "Random survival Forests for prediction of survival after oesophagectomy." European Journal of Surgical Oncology 47, no. 1 (January 2021): e4. http://dx.doi.org/10.1016/j.ejso.2020.11.023.
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Inoue, Takamitsu, Norihiko Tsuchiya, Shigeyuki Matsui, Tomomi Kamba, Koji Mitsuzuka, Shingo Hatakeyama, Yohei Horikawa, et al. "Prediction of survival in patients with metastatic prostate cancer by single nucleotide polymorphisms of cancer-associated genes." Journal of Clinical Oncology 30, no. 5_suppl (February 10, 2012): 177. http://dx.doi.org/10.1200/jco.2012.30.5_suppl.177.
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177 Background: Individual genetic variations may have a significant influence on the survival of metastatic prostate cancer (PCa) patients. We aimed to identify target genes and their variations involved in the survival of PCa patients using a single nucleotide polymorphism (SNP) panel. Methods: A total of 185 PCa patients with bone metastasis at initial diagnosis were analyzed. Each patient was genotyped using a Cancer SNP panel that contained 1421 SNPs in 408 cancer-related genes. SNPs associated with the survival were screened by log rank test. A prognostic scoring index using selected SNPs was developed by incorporating the difference in their effect sizes to classify high-risk and low-risk groups and its predictive accuracy was assessed. Results: Fourteen SNPs in six genes, XRCC4, PSM1, GATA3, IL13, CASP8, and IGF1, were identified to have statistically significant association with the cancer-specific survival. The cancer-specific survivals of patients grouped according to the number of risk genotypes of 6 SNPs selected from the 14 SNPs differed significantly (0-1 vs 2-3 vs 4-6 risk genotypes, P = 7.20×10−8). The predictive model using the 14 SNPs showed a statistically significant cross-validated accuracy in predicting the groups at high and low risk groups for poor survival (P = 0.0050). The high-risk group was independently associated with the survival in a multivariate analysis that included conventional clinicopathological variables (P = 0.0060). Conclusions: Using a panel of the SNPs, the prediction of the survival and optimization of the individualized treatment for patients with advanced PCa may be possible.
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Prasad, Birbal, Yongji Tian, and Xinzhong Li. "Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma." Molecular Neurobiology 57, no. 12 (September 1, 2020): 5235–46. http://dx.doi.org/10.1007/s12035-020-02088-w.
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Abstract Glioblastoma multiforme (GBM) is the most aggressive and common primary central nervous system tumour. Despite extensive therapy, GBM patients usually have poor prognosis with a median survival of 12–15 months. Novel molecular biomarkers that can improve survival prediction and help with treatment strategies are still urgently required. Here we aimed to robustly identify a gene signature panel for improved survival prediction in primary GBM patients. We identified 2166 differentially expressed genes (DEGs) using meta-analysis of microarray datasets comprising of 955 samples (biggest primary GBM cohort for such studies as per our knowledge) and 3368 DEGs from RNA-seq dataset with 165 samples. Based on the 1443 common DEGs, using univariate Cox and least absolute shrinkage and selection operator (LASSO) with multivariate Cox regression, we identified a survival associated 4-gene signature panel including IGFBP2, PTPRN, STEAP2 and SLC39A10 and thereafter established a risk score model that performed well in survival prediction. High-risk group patients had significantly poorer survival as compared with those in the low-risk group (AUC = 0.766 for 1-year prediction). Multivariate analysis demonstrated that predictive value of the 4-gene signature panel was independent of other clinical and pathological features and hence is a potential prognostic biomarker. More importantly, we validated this signature in three independent GBM cohorts to test its generality. In conclusion, our integrated analysis using meta-analysis approach maximizes the use of the available gene expression data and robustly identified a 4-gene panel for predicting survival in primary GBM.
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Gui, Weiwei, Weifen Zhu, Weina Lu, Chengxin Shang, Fenping Zheng, Xihua Lin, and Hong Li. "Development and validation of a prognostic nomogram to predict overall survival and cancer-specific survival for patients with anaplastic thyroid carcinoma." PeerJ 8 (May 21, 2020): e9173. http://dx.doi.org/10.7717/peerj.9173.
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Background Anaplastic thyroid carcinoma (ATC) is a rare malignant tumor with a poor prognosis. However, there is no useful clinical prognostic predictive tool for ATC so far. Our study identified risk factors for survival of ATC and created a reliable nomogram to predict overall survival (OS) and cancer-specific survival (CSS) of patients with ATC. Methods A total of 1,404 cases of ATC diagnosed between 1983 and 2013 were extracted from on the Surveillance, Epidemiology and End Results database based on our inclusion criteria. OS and CSS were compared among patients between each variable by Kaplan–Meier methods. The Cox proportional hazards model was used to evaluate multiple prognostic factors and obtain independent predictors. All independent risk factors were included to build nomograms, whose accuracy and practicability were tested by concordance index (C-index), calibration curves, ROC curves, DCA, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results Historic stage, tumor size, surgery and radiotherapy were independent risk factors associated with ATC according to multivariate Cox regression analysis of OS. However, gender was also an important prognostic predictor in CSS besides the factors mentioned above. These characteristics were included in the nomograms predicting OS and CSS of patients with ATC. The nomograms predicting OS and CSS performed well with a C-index of 0.765 and 0.773. ROC curves, DCA, NRI and IDI suggested that the nomogram was superior to TNM staging and age. Conclusion The proposed nomogram is a reliable tool based on the prediction of OS and CSS for patients with ATC. Such a predictive tool can help to predict the survival of the patients.
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Hong, Fangxin, Brad S. Kahl, and Robert Gray. "Incremental Value in Outcome Prediction with Molecular Signatures in Diffuse Large B-Cell Lymphoma,." Blood 118, no. 21 (November 18, 2011): 3687. http://dx.doi.org/10.1182/blood.v118.21.3687.3687.
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Abstract Abstract 3687 INTRODUCTION: Multiple gene expression-based biomarkers have been identified in diffuse large B-cell lymphoma (DLBCL) that are predictive for survival outcomes. Most studies assess predictive significance based on p-value from multivariate Cox regression; few investigations have evaluated the incremental usefulness of these biomarkers in risk prediction. Using the recently developed concordance measures (e.g., C-statistics) on censored survival data, we assessed the usefulness of two published gene-based risk signatures and compared them to the known clinical prognostic factors; with an overall goal of investigating the added value. METHOD: The added value of biomarkers was assessed by C-statistic and the Integrated Discrimination Improvement (IDI). The overall C-statistic is an estimated concordance between prediction and observation (event vs. non-event) - the probability that predicted risk score is higher for subject with earlier time of event. The IDI measures overall improvement in sensitivity and specificity. The signatures we selected are a six-gene predictor by Lossos et al. (2004) and a three-component signature (∼400 genes) by Lenz et al. (2008). We used the Lenz dataset which include 233 patients with DLBCL who received R-CHOP therapy (median follow-up=2.81 yr), and focused on predicting 3-year survival outcome (42% censored). Clinical prognostic factors evaluated are the traditional IPI components (stage, performance status, age, LDH, and number of extra nodal sites). RESULTS: The C-statistic was 0.60 and 0.721 for six-gene predictor and three-component signature, suggesting good discrimination ability by molecular signature when used alone. However, the performance is inferior to IPI risk factors, with a C-statistic of 0.733. When integrating gene signatures with IPI risk factors, the C-statistic was increased to 0.744 and 0.762, an improvement of only 0.011 (95% CI, -0.049 to 0.071) and 0.029 (95% CI, -0.033 to 0.091) for six-gene predictor and three-component signature, respectively. Furthermore, assessment by IDI reveals an added value of only 0.011 (95% CI, -0.008 to 0.081) and 0.076 (95% CI, 0.013 to 0.16) for the two molecular signatures. Kaplan-Meier survival curves for the four quartile groups based on the predictor scores confirms the marginal benefit in risk prediction using molecular signatures. (Figure 1). CONCLUSIONS: These results indicate that molecular biomarkers are inferior to clinical factors for risk assessment in DLBCL and provide little added value in risk prediction. These calculations suggest we will need to consider more than gene expression to develop highly discriminatory risk prediction models. However, the study of gene expression and clinical outcomes retains considerable potential to enhance understanding of disease mechanisms and uncover new therapeutic targets. Disclosures: No relevant conflicts of interest to declare.
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Liang, Yang, Han-ying Huang, Yun Wang, Weida Wang, Jin-Yuan Li, and Ling-ling Shu. "An Accurate Prognostic Survival Model of Survival Based on Expression of Genes Involved in Plasma Cell Metabolism." Blood 136, Supplement 1 (November 5, 2020): 14–15. http://dx.doi.org/10.1182/blood-2020-140930.
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Background: Models to predict survival of persons with plasma cell myeloma are mostly use clinical and laboratory co-variates including cytogenetics and mutation topography. These models have AUCs of 0.55 to 0.77 indicating considerable inaccuracy. We tested whether data of plasma cell metabolism might improve prediction accuracy. Methods: RNA matrix, clinical and laboratory data from datasets GSE13633 (training cohort) and GSE57317, GSE24080 and GSE2658 (validation cohorts) were downloaded from the Gene Expression Omnibus (GEO) database. Genes regulating plasma cell metabolism correlating with survival were identified in uni-variable Cox regression analyses and a metabolic risk score built by Lasso Cox regression analysis. Survivals was compared by Kaplan-Meier curves with log-rank tests. Prediction accuracy of the metabolic risk score was quantified by time-dependent receiver operating characteristic (ROC) curves and the area under the curve. A nomogram was developed including metabolic risk score and ISS stage. Enriched pathways in different metabolic risk score cohorts were evaluated by gene set enrichment analyses (GSEA). We interrogated validity of 3 genes with the largest risk coefficients in vitro using small molecule inhibitors to test effects on cell proliferation and apoptosis by CCK-8 and multi-parameter flow cytometry (MPFC). Results: 7 genes regulating plasma cell metabolism with high risk coefficients were used to develop a metabolic risk score. The score had robust predictive performance with AUCs for 3-year survival in the 4 cohorts of 0.71 [95% Confidence Interval, 0.65, 0.79], 0.88 [0.71, 1.03], 0.70 [0.63,0.76] and 0.71 [0.65, 0.79]. Subjects in the high-risk cohort had worse 3-year survivals compared with those in the low-risk cohort (62% [55, 68%] vs. 85% [80, 90%], P < 0.001; 54% [33, 76%] vs. 92% [82, 102%], P <0.001; 58% [52, 65%] vs. 77% [71, 82%], P <0.001; and 51% [38, 64%] vs. 74% [62, 85%], P <0.001). The metabolic risk score remained an independent prognostic factor for survival in multi-variable regression analyses. A nomogram combining metabolic risk score with ISS score increased prediction accuracy of 5-year survival (AUCs of 0.72 [0.69,0.81] vs. 0.66 [0.60, 0.72]; P = 0.015). In GSEA most gene enrichment pathways in high-risk cohort were metabolism-related. In vitro, the results showed that the three small molecule inhibitors combined with bortezomib had a synergistic inhibitory effect on the growth of myeloma cells H929, MM.1S, U266B1 and RPMI 8226, with an effective synergistic CI value; and enhanced the apoptosis of myeloma cell lines effect. Conclusion: A risk score based on expression of genes involved in plasma cell metabolism predicted survival and significantly improved prediction accuracy of the ISS score. Predictions based on gene expression were validated in vitro. Keywords GEO, plasma cell myeloma, metabolism, prognostic model Disclosures No relevant conflicts of interest to declare.
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Garzín, Benjamón, Kyrre E. Emblem, Kim Mouridsen, Baard Nedregaard, Paulina Due-Tønnessen, Terje Nome, John K. Hald, Atle Bjørnerud, Asta K. Håberg, and Yngve Kvinnsland. "Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction." Acta Radiologica 52, no. 9 (November 2011): 1052–60. http://dx.doi.org/10.1258/ar.2011.100510.
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Background A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant ( P < 0.0001). Conclusion Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients.
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OSCAR, THOMAS P. "Development and Validation of a Predictive Microbiology Model for Survival and Growth of Salmonella on Chicken Stored at 4 to 12°C†." Journal of Food Protection 74, no. 2 (February 1, 2011): 279–84. http://dx.doi.org/10.4315/0362-028x.jfp-10-314.
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Salmonella spp. are a leading cause of foodborne illness. Mathematical models that predict Salmonella survival and growth on food from a low initial dose, in response to storage and handling conditions, are valuable tools for helping assess and manage this public health risk. The objective of this study was to develop and to validate the first predictive microbiology model for survival and growth of a low initial dose of Salmonella on chicken during refrigerated storage. Chicken skin was inoculated with a low initial dose (0.9 log) of a multiple antibiotic-resistant strain of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 4 to 12°C for 0 to 10 days. A general regression neural network (GRNN) model that predicted log change of Salmonella Typhimurium DT104 as a function of time and temperature was developed. Percentage of residuals in an acceptable prediction zone, from −1 (fail-safe) to 0.5 (fail-dangerous) log, was used to validate the GRNN model by using a criterion of 70% acceptable predictions. Survival but not growth of Salmonella Typhimurium DT104 was observed at 4 to 8°C. Maximum growth of Salmonella Typhimurium DT104 during 10 days of storage was 0.7 log at 9°C, 1.1 log at 10°C, 1.8 log at 11°C, and 2.9 log at 12°C. Performance of the GRNN model for predicting dependent data (n = 163) was 85% acceptable predictions, for predicting independent data for interpolation (n = 77) was 84% acceptable predictions, and for predicting independent data for extrapolation (n = 70) to Salmonella Kentucky was 87% acceptable predictions. Thus, the GRNN model provided valid predictions for survival and growth of Salmonella on chicken during refrigerated storage, and therefore the model can be used with confidence to help assess and manage this public health risk.
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Kaplan, Adam, and Eric F. Lock. "Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival." Cancer Informatics 16 (January 1, 2017): 117693511771851. http://dx.doi.org/10.1177/1176935117718517.
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Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal component analysis (PCA). However, the application of PCA is not straightforward for multisource data, wherein multiple sources of ‘omics data measure different but related biological components. In this article, we use recent advances in the dimension reduction of multisource data for predictive modeling. In particular, we apply exploratory results from Joint and Individual Variation Explained (JIVE), an extension of PCA for multisource data, for prediction of differing response types. We conduct illustrative simulations to illustrate the practical advantages and interpretability of our approach. As an application example, we consider predicting survival for patients with glioblastoma multiforme from 3 data sources measuring messenger RNA expression, microRNA expression, and DNA methylation. We also introduce a method to estimate JIVE scores for new samples that were not used in the initial dimension reduction and study its theoretical properties; this method is implemented in the R package R.JIVE on CRAN, in the function jive.predict.
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Huang, Yen-Chun, Shao-Jung Li, Mingchih Chen, Tian-Shyug Lee, and Yu-Ning Chien. "Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients." Healthcare 9, no. 5 (May 7, 2021): 547. http://dx.doi.org/10.3390/healthcare9050547.
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Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.
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Jiang, Shu. "Prediction Based on Random Survival Forest." American Journal of Biomedical Science & Research 6, no. 2 (November 8, 2019): 109–11. http://dx.doi.org/10.34297/ajbsr.2019.06.001005.
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Xu, Xiaojiang, Chris A. Turner, and William R. Santee. "Survival time prediction in marine environments." Journal of Thermal Biology 36, no. 6 (August 2011): 340–45. http://dx.doi.org/10.1016/j.jtherbio.2011.06.009.
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Koh, T. H. H. G. "Prediction of survival for preterm births." BMJ 320, no. 7235 (March 4, 2000): 647. http://dx.doi.org/10.1136/bmj.320.7235.647.
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Draper, E. S. "Prediction of survival for preterm births." BMJ 321, no. 7255 (July 22, 2000): 237. http://dx.doi.org/10.1136/bmj.321.7255.237.
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Jablecki, Charles K., Charles Berry, and Judy Leach. "Survival prediction in amyotrophic lateral sclerosis." Muscle & Nerve 12, no. 10 (October 1989): 833–41. http://dx.doi.org/10.1002/mus.880121008.
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Hao, Lin, Juncheol Kim, Sookhee Kwon, and Il Do Ha. "Deep Learning-Based Survival Analysis for High-Dimensional Survival Data." Mathematics 9, no. 11 (May 28, 2021): 1244. http://dx.doi.org/10.3390/math9111244.
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With the development of high-throughput technologies, more and more high-dimensional or ultra-high-dimensional genomic data are being generated. Therefore, effectively analyzing such data has become a significant challenge. Machine learning (ML) algorithms have been widely applied for modeling nonlinear and complicated interactions in a variety of practical fields such as high-dimensional survival data. Recently, multilayer deep neural network (DNN) models have made remarkable achievements. Thus, a Cox-based DNN prediction survival model (DNNSurv model), which was built with Keras and TensorFlow, was developed. However, its results were only evaluated on the survival datasets with high-dimensional or large sample sizes. In this paper, we evaluated the prediction performance of the DNNSurv model using ultra-high-dimensional and high-dimensional survival datasets and compared it with three popular ML survival prediction models (i.e., random survival forest and the Cox-based LASSO and Ridge models). For this purpose, we also present the optimal setting of several hyperparameters, including the selection of a tuning parameter. The proposed method demonstrated via data analysis that the DNNSurv model performed well overall as compared with the ML models, in terms of the three main evaluation measures (i.e., concordance index, time-dependent Brier score, and the time-dependent AUC) for survival prediction performance.
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White, Nicola, Fiona Reid, Victoria Vickerstaff, Priscilla Harries, and Patrick Stone. "Specialist palliative medicine physicians and nurses accuracy at predicting imminent death (within 72 hours): a short report." BMJ Supportive & Palliative Care 10, no. 2 (March 22, 2020): 209–12. http://dx.doi.org/10.1136/bmjspcare-2020-002224.
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ObjectivesResearch suggests that clinicians are not very accurate at prognosticating in palliative care. The ‘horizon effect’ suggests that accuracy ought to be better when the survival of patients is shorter. The aim of this study was to determine the accuracy of specialist palliative care clinicians at identifying which patients are likely to die within 72 hours.DesignIn a secondary data analysis of a prospective observational study, specialist palliative care doctors and nurses (in a hospice and a hospital palliative care team) provided survival predictions (yes/no/uncertain) about which patients would die within 72 hours.ResultsSurvival predictions were obtained for 49 patients. A prediction from a nurse was obtained for 37/49 patients. A prediction from a doctor was obtained for 46/49 patients. In total, 23 (47%)/49 patients actually died within 72 hours of assessment. Nurses accurately predicted the outcome in 27 (73%)/37 cases. Doctors accurately predicted the outcome in 30 (65%)/46 cases. When comparing predictions given on the same patients (27 [55%]/49), nurses were slightly better at recognising imminent death than doctors (positive predictive value (the proportion of patients who died when the clinician predicted death)=79% vs 60%, respectively). The difference in c-statistics (nurses 0.82 vs doctors 0.63) was not significant (p=0.13).ConclusionEven when patients are in the terminal phase and close to death, clinicians are not very good at predicting how much longer they will survive. Further research is warranted to improve prognostication in this population.
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Wang, Bo, and Jing Zhang. "Multiple Linear Regression Analysis of lncRNA–Disease Association Prediction Based on Clinical Prognosis Data." BioMed Research International 2018 (December 11, 2018): 1–10. http://dx.doi.org/10.1155/2018/3823082.
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Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.
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Chicote, Beatriz, Unai Irusta, Elisabete Aramendi, Raúl Alcaraz, José Rieta, Iraia Isasi, Daniel Alonso, María Baqueriza, and Karlos Ibarguren. "Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest." Entropy 20, no. 8 (August 9, 2018): 591. http://dx.doi.org/10.3390/e20080591.
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Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.
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Kowalski, David G., and George Z. Gertner. "A Validation of TWIGS for Illinois Forests." Northern Journal of Applied Forestry 6, no. 4 (December 1, 1989): 154–56. http://dx.doi.org/10.1093/njaf/6.4.154.
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Abstract The predictive ability of the Central States growth and yield system TWIGS 3.0 was evaluated for Illinois forests. The data used to validate the system were collected from permanent plots that had been established throughout the state and maintained for up to 30 years. TWIGS growth predictions were analyzed through the differences between observed and predicted stand and species characteristics. TWIGS consistently overpredicted mean stand diameter and underpredicted tree survival over a 30-year growth projection. The combined result of prediction errors in diameter growth and tree survival was a consistent underprediction of basal area per acre. Percent error at the twentieth year of projection was -6% for mean stand diameter, 18% for tree survival, and 6% for basal area. TWIGS, with its simulation features and small relative prediction errors for some major timber species, is an adequate growth and yield system for Illinois' mixed hardwood forests. North. J. Appl. For. 6:154-156, December 1989.
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Lan, Yu, and Daniel F. Heitjan. "Adaptive parametric prediction of event times in clinical trials." Clinical Trials 15, no. 2 (January 29, 2018): 159–68. http://dx.doi.org/10.1177/1740774517750633.
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Background: In event-based clinical trials, it is common to conduct interim analyses at planned landmark event counts. Accurate prediction of the timing of these events can support logistical planning and the efficient allocation of resources. As the trial progresses, one may wish to use the accumulating data to refine predictions. Purpose: Available methods to predict event times include parametric cure and non-cure models and a nonparametric approach involving Bayesian bootstrap simulation. The parametric methods work well when their underlying assumptions are met, and the nonparametric method gives calibrated but inefficient predictions across a range of true models. In the early stages of a trial, when predictions have high marginal value, it is difficult to infer the form of the underlying model. We seek to develop a method that will adaptively identify the best-fitting model and use it to create robust predictions. Methods: At each prediction time, we repeat the following steps: (1) resample the data; (2) identify, from among a set of candidate models, the one with the highest posterior probability; and (3) sample from the predictive posterior of the data under the selected model. Results: A Monte Carlo study demonstrates that the adaptive method produces prediction intervals whose coverage is robust within the family of selected models. The intervals are generally wider than those produced assuming the correct model, but narrower than nonparametric prediction intervals. We demonstrate our method with applications to two completed trials: The International Chronic Granulomatous Disease study and Radiation Therapy Oncology Group trial 0129. Limitations: Intervals produced under any method can be badly calibrated when the sample size is small and unhelpfully wide when predicting the remote future. Early predictions can be inaccurate if there are changes in enrollment practices or trends in survival. Conclusions: An adaptive event-time prediction method that selects the model given the available data can give improved robustness compared to methods based on less flexible parametric models.
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Ettaieb, Madeleine H. T., Sander M. J. van Kuijk, Annelies de Wit-Pastoors, Richard A. Feelders, Eleonora P. M. Corssmit, Elisabeth M. W. Eekhoff, Paul van der Valk, et al. "Development and Internal Validation of a Multivariable Prediction Model for Adrenocortical-Carcinoma-Specific Mortality." Cancers 12, no. 9 (September 22, 2020): 2720. http://dx.doi.org/10.3390/cancers12092720.
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Adrenocortical carcinoma (ACC) has an incidence of about 1.0 per million per year. In general, survival of patients with ACC is limited. Predicting survival outcome at time of diagnosis is a clinical challenge. The aim of this study was to develop and internally validate a clinical prediction model for ACC-specific mortality. Data for this retrospective cohort study were obtained from the nine centers of the Dutch Adrenal Network (DAN). Patients who presented with ACC between 1 January 2004 and 31 October 2013 were included. We used multivariable Cox proportional hazards regression to compute the coefficients for the prediction model. Backward stepwise elimination was performed to derive a more parsimonious model. The performance of the initial prediction model was quantified by measures of model fit, discriminative ability, and calibration. We undertook an internal validation step to counteract the possible overfitting of our model. A total of 160 patients were included in the cohort. The median survival time was 35 months, and interquartile range (IQR) 50.7 months. The multivariable modeling yielded a prediction model that included age, modified European Network for the Study of Adrenal Tumors (mENSAT) stage, and radical resection. The c-statistic was 0.77 (95% Confidence Interval: 0.72, 0.81), indicating good predictive performance. We developed a clinical prediction model for ACC-specific mortality. ACC mortality can be estimated using a relatively simple clinical prediction model with good discriminative ability and calibration.
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Troupé, M., J. Manuceau, V. Lánska, and J. Vaillant. "Nonparametric Selection Method of Survival Predictors with an Application to Breast Cancer." Methods of Information in Medicine 40, no. 01 (2001): 12–17. http://dx.doi.org/10.1055/s-0038-1634458.
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AbstractMutual information between a survival variable and covariables provides a new tool for selecting covariables with a high predictive value whenever there is no reasonable parametric model with respect to the observed phenomena. The information rate carried out by covariables can be tested by means of a decomposition similar to the analysis of variance. Moreover, a method based on information conservation can be used for aggregating survival curves corresponding to different modalities of the same selected predictor which increases the prediction efficiency. These results are applied to survival data from 1304 patients with breast cancer followed over a period of ten years.
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Hyung, Woo Jin, Taeil Son, Minseok Park, Hansang Lee, Youn Nam Kim, Hyoung-Il Kim, Jong Won Kim, et al. "Superior prognosis prediction performance of deep learning for gastric cancer compared to Yonsei prognosis prediction model using Cox regression." Journal of Clinical Oncology 35, no. 4_suppl (February 1, 2017): 164. http://dx.doi.org/10.1200/jco.2017.35.4_suppl.164.
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164 Background: Staging systems for cancer are critical to predict the prognosis of patients. Current staging systems for gastric cancer have limitations to predict individualized and precise prediction of patient’s survival after treatment. We aimed to develop prediction model based on deep learning by estimating the survival probability of patients who underwent gastrectomy. Methods: To predict the survival probability, we used a deep neural network model which consisted of 5 layers: input layer, 3 fully connected layer, and output layer with 8 characteristics (age, sex, histology, depth of tumor, number of metastatic and examined lymph node, presence of distant metastasis, and resection extent) of patients which was previously published Yonsei prediction model using Cox regression. Each layer functioned as the nonlinear weighted sum of lower layer. Five-year overall survival was predicted using the deep learning method and it was compared to Yonsei prediction model. The average area under the curve (AUC) was compared between the models. For internal validation, 5-fold cross validations were carried out. We also performed external validation with a dataset from another hospital (n = 1549). . Results: Deep learning predicted 5-year overall survival of patients with an average accuracy of 83.5% in the test set. The average AUC of deep learning by integrating 8 characteristics was significantly higher than that of Yonsei prediction model (AUC: 0.844 vs. 0.831, P < 0.001) with the same variables. In the external validation the average accuracy of survival prediction was 84.1%. The AUC was also greater in a dataset from other hospital in Korea (AUC: 0.852 vs. 0.847, P = 0.023) Conclusions: Prognosis prediction with deep learning showed superior survival predictive power compared to prediction model using Cox regression. It can provide individualized and precise stratification based on the risk using characteristics of gastric cancer patients.
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Richter, Jakob, Katrin Madjar, and Jörg Rahnenführer. "Model-based optimization of subgroup weights for survival analysis." Bioinformatics 35, no. 14 (July 2019): i484—i491. http://dx.doi.org/10.1093/bioinformatics/btz361.
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AbstractMotivationTo obtain a reliable prediction model for a specific cancer subgroup or cohort is often difficult due to limited sample size and, in survival analysis, due to potentially high censoring rates. Sometimes similar data from other patient subgroups are available, e.g. from other clinical centers. Simple pooling of all subgroups can decrease the variance of the predicted parameters of the prediction models, but also increase the bias due to heterogeneity between the cohorts. A promising compromise is to identify those subgroups with a similar relationship between covariates and target variable and then include only these for model building.ResultsWe propose a subgroup-based weighted likelihood approach for survival prediction with high-dimensional genetic covariates. When predicting survival for a specific subgroup, for every other subgroup an individual weight determines the strength with which its observations enter into model building. MBO (model-based optimization) can be used to quickly find a good prediction model in the presence of a large number of hyperparameters. We use MBO to identify the best model for survival prediction of a specific subgroup by optimizing the weights for additional subgroups for a Cox model. The approach is evaluated on a set of lung cancer cohorts with gene expression measurements. The resulting models have competitive prediction quality, and they reflect the similarity of the corresponding cancer subgroups, with both weights close to 0 and close to 1 and medium weights.Availability and implementationmlrMBO is implemented as an R-package and is freely available at http://github.com/mlr-org/mlrMBO.
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Mao, Weimin, Xinming Zhou, Qixun Chen, Youhua Jiang, Xun Yang, Jie Wu, Kaiyi Tao, et al. "Nomogram predicting long-term survival probability of thoracic esophageal squamous cell carcinoma after radical esophagectomy." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 4094. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.4094.
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4094 Background: Nomograms have been widely and successfully used for numerous cancers to obtain reliable prognostic information for each individual patient.To date, however, no studies have conducted survival estimates using nomograms for esophageal squamous-cell carcinoma (ESCC) in Chinese population.The purpose of this study is to develop a nomogram to predict the long-term survival probabilities in patients diagnosed with ESCC after radical esophagectomy. Methods: This study involves a dataset containing 1923 patients who underwent radical esophagectomy for ESCC at Zhejiang Cancer Hospital in Hangzhou, China. Among them, 1,578 patients with no missing data were used to build a prognostic nomogram based on Cox proportional hazard regression model. A multivariate survival analysis using Cox regression model was applied to identify significant variables with P-values <0.05. On the basis of the predictive model with the identified variables, a nomogram was constructed for predicting five-year and ten-year overall survival probabilities. The prediction model was internally validated using bootstrap resampling, assessing its optimism-corrected discrimination and calibration. Results: The median of overall survival times of 1578 ESCC patients was 35.6 months, and the 5-year and 10-year survival rate was 32% and 20%, respectively. The multivariate Cox model identified alcohol, tumor length, surgical approach, number of surgical removed lymph node, ratio of metastatic lymph nodes, region of lymph nodes dissection, depth of invasion, differentiation of tumor, postoperative complications as covariates significantly associated with survival. Across the 100 bootstrap replicates, the median optimism-corrected summary C-index for predicting survival was 0.713 (SE=0.011). Conclusions: A nomogram predicting 5- and 10-year overall survival after radical esophagectomy for ESCC in Chinese population was constructed and validated based on nine significant variables. The nomogram can be applied in daily clinical practice for individualized survival prediction of ESCC patients after potentially curative esophagectomy.
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Cao, Quang V. "A method to derive a tree survival model from any existing stand survival model." Canadian Journal of Forest Research 49, no. 12 (December 2019): 1598–603. http://dx.doi.org/10.1139/cjfr-2019-0171.
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This study addresses a situation in which a forest manager has been using a whole-stand model that seems to predict well for their stands and now wants to derive an individual-tree model from it to form an integrated system that can perform well at both stand and tree levels. A simple method was developed to derive tree survival models from three existing stand-level survival models. The derived tree survival models were based on the difference between the diameter of a given tree and the diameter at which tree and stand survival probabilities are equal. For stand survival prediction, each stand model performed less adequately than its derived tree model, and one of the derived tree survival models was the best overall. For tree survival prediction, the same derived tree model also performed best overall. Even though only three stand-level survival models were considered in this study, the method presented here should be applicable to any stand survival model. When no tree survival data were available, tree survival models derived from stand survival models ranked lowest in terms of performance but produced acceptable evaluation statistics for predicting tree-level survival.
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Laudano, M. A., M. Desai, K. K. Badani, A. L. Hirsh, M. C. Benson, and J. M. McKiernan. "Prediction of long-term survival conditional on years survived following nephrectomy." Journal of Clinical Oncology 26, no. 15_suppl (May 20, 2008): 5120. http://dx.doi.org/10.1200/jco.2008.26.15_suppl.5120.
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Wang, He-Hui, Ke-Na Dai, and A.-Bing Li. "A Nomogram Predicting Overall and Cancer-Specific Survival of Patients with Primary Bone Lymphoma: A Large Population-Based Study." BioMed Research International 2020 (August 20, 2020): 1–10. http://dx.doi.org/10.1155/2020/4235939.
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We aimed to develop a nomogram for evaluating the overall survival (OS) and cancer-specific survival (CSS) in patients with primary bone lymphoma (PBL). Patients diagnosed with PBL between 2007 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to the training cohort and validation cohort (2 : 1). The nomogram was developed by the training cohort and validated by the validation cohort using the concordance index (C-index), calibration plots, and decision curve analyses (DCAs). The C-index for CSS and OS prediction in the training cohort were 0.76 and 0.77, respectively; in the validation cohort, they were 0.76 and 0.79, respectively. The calibration curve showed good consistency between nomogram prediction and actual survival. The DCA indicated obvious net benefits of the new predictive model. The nomogram showed favorable applicability and accuracy, and it will be a reliable tool for predicting OS and CSS in patients with PBL.
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Teno, Joan M., Frank E. Harrell, William Knaus, Russell S. Phillips, Albert W. Wu, Alfred Connors, Neil S. Wenger, et al. "Prediction of Survival for Older Hospitalized Patients: The HELP Survival Model." Journal of the American Geriatrics Society 48, S1 (May 2000): S16—S24. http://dx.doi.org/10.1111/j.1532-5415.2000.tb03126.x.
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Arends, Coralie R., Japke F. Petersen, Vincent Noort, Adriana J. Timmermans, C. René Leemans, Remco Bree, Michiel W. M. Brekel, and Martijn M. Stuiver. "Optimizing Survival Predictions of Hypopharynx Cancer: Development of a Clinical Prediction Model." Laryngoscope 130, no. 9 (November 6, 2019): 2166–72. http://dx.doi.org/10.1002/lary.28345.
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Du, Mi, Dandara G. Haag, John W. Lynch, and Murthy N. Mittinty. "Comparison of the Tree-Based Machine Learning Algorithms to Cox Regression in Predicting the Survival of Oral and Pharyngeal Cancers: Analyses Based on SEER Database." Cancers 12, no. 10 (September 29, 2020): 2802. http://dx.doi.org/10.3390/cancers12102802.
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This study aims to demonstrate the use of the tree-based machine learning algorithms to predict the 3- and 5-year disease-specific survival of oral and pharyngeal cancers (OPCs) and compare their performance with the traditional Cox regression. A total of 21,154 individuals diagnosed with OPCs between 2004 and 2009 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Three tree-based machine learning algorithms (survival tree (ST), random forest (RF) and conditional inference forest (CF)), together with a reference technique (Cox proportional hazard models (Cox)), were used to develop the survival prediction models. To handle the missing values in predictors, we applied the substantive model compatible version of the fully conditional specification imputation approach to the Cox model, whereas we used RF to impute missing data for the ST, RF and CF models. For internal validation, we used 10-fold cross-validation with 50 iterations in the model development datasets. Following this, model performance was evaluated using the C-index, integrated Brier score (IBS) and calibration curves in the test datasets. For predicting the 3-year survival of OPCs with the complete cases, the C-index in the development sets were 0.77 (0.77, 0.77), 0.70 (0.70, 0.70), 0.83 (0.83, 0.84) and 0.83 (0.83, 0.86) for Cox, ST, RF and CF, respectively. Similar results were observed in the 5-year survival prediction models, with C-index for Cox, ST, RF and CF being 0.76 (0.76, 0.76), 0.69 (0.69, 0.70), 0.83 (0.83, 0.83) and 0.85 (0.84, 0.86), respectively, in development datasets. The prediction error curves based on IBS showed a similar pattern for these models. The predictive performance remained unchanged in the analyses with imputed data. Additionally, a free web-based calculator was developed for potential clinical use. In conclusion, compared to Cox regression, ST had a lower and RF and CF had a higher predictive accuracy in predicting the 3- and 5-year OPCs survival using SEER data. The RF and CF algorithms provide non-parametric alternatives to Cox regression to be of clinical use for estimating the survival probability of OPCs patients.
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Grande, Gunn E., Morag C. Farquhar, Stephen I. G. Barclay, and Christopher J. Todd. "Quality of life measures (EORTC QLQ-C30 and SF-36) as predictors of survival in palliative colorectal and lung cancer patients." Palliative and Supportive Care 7, no. 3 (September 2009): 289–97. http://dx.doi.org/10.1017/s1478951509990216.
Full textAbstract:
AbstractObjective:Self-reported health-related quality of life (HRQoL) is an important predictor of survival alongside clinical variables and physicians' prediction. This study assessed whether better prediction is achieved using generic (SF-36) HRQoL measures or cancer-specific (EORTC QLQ-C30) measures that include symptoms.Method:Fifty-four lung and 46 colorectal patients comprised the sample. Ninety-four died before study conclusion. EORTC QLQ-C30 and SF-36 scores and demographic and clinical information were collected at baseline. Follow-up was 5 years. Deaths were flagged by the Office of National Statistics. Cox regression survival analyses were conducted. Surviving cases were censored in the analysis.Results:Univariate analyses showed that survival was significantly associated with better EORTC QLQ-C30 physical functioning, role functioning, and global health and less dyspnea and appetite loss. For the SF-36, survival was significantly associated with better emotional role functioning, general health, energy/vitality, and social functioning. The SF-36 summary score for mental health was significantly related to better survival, whereas the SF-36 summary score for physical health was not. In the multivariate analysis, only the SF-36 mental health summary score remained an independent, significant predictor, mainly due to considerable intercorrelations between HRQoL scales. However, models combining the SF-36 mental health summary score with diagnosis explained a similar amount of variance (12%–13%) as models combining diagnosis with single scale SF-36 Energy/Vitality or EORTC QLQ-C30 Appetite Loss.Significance of results:HRQoL contributes significantly to prediction of survival. Generic measures are at least as useful as disease-specific measures including symptoms. Intercorrelations between HRQoL variables and between HRQoL and clinical variables makes it difficult to identify prime predictors. We need to identify variables that are as independent of each other as possible to maximize predictive power and produce more consistent results.
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Stamenic, Danko, Annick Rousseau, Marie Essig, Philippe Gatault, Mathias Buchler, Matthieu Filloux, Pierre Marquet, and Aurélie Prémaud. "A Prognostic Tool for Individualized Prediction of Graft Failure Risk within Ten Years after Kidney Transplantation." Journal of Transplantation 2019 (April 8, 2019): 1–10. http://dx.doi.org/10.1155/2019/7245142.
Full textAbstract:
Identification of patients at risk of kidney graft loss relies on early individual prediction of graft failure. Data from 616 kidney transplant recipients with a follow-up of at least one year were retrospectively studied. A joint latent class model investigating the impact of serum creatinine (Scr) time-trajectories and onset of de novo donor-specific anti-HLA antibody (dnDSA) on graft survival was developed. The capacity of the model to calculate individual predicted probabilities of graft failure over time was evaluated in 80 independent patients. The model classified the patients in three latent classes with significantly different Scr time profiles and different graft survivals. Donor age contributed to explaining latent class membership. In addition to the SCr classes, the other variables retained in the survival model were proteinuria measured one-year after transplantation (HR=2.4, p=0.01), pretransplant non-donor-specific antibodies (HR=3.3, p<0.001), and dnDSA in patient who experienced acute rejection (HR=15.9, p=0.02). In the validation dataset, individual predictions of graft failure risk provided good predictive performances (sensitivity, specificity, and overall accuracy of graft failure prediction at ten years were 77.7%, 95.8%, and 85%, resp.) for the 60 patients who had not developed dnDSA. For patients with dnDSA individual risk of graft failure was not predicted with a so good performance.