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Literatura académica sobre el tema "Radiomics analysis"

Autor: Grafiati

Publicado: 14 de diciembre de 2024

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Artículos de revistas sobre el tema "Radiomics analysis"

Hu, Shuyi, Xiajie Lyu, Weifeng Li, Xiaohan Cui, Qiaoyu Liu, Xiaoliang Xu, Jincheng Wang, Lin Chen, Xudong Zhang y Yin Yin. "Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)". Contrast Media & Molecular Imaging 2022 (25 de junio de 2022): 1–8. http://dx.doi.org/10.1155/2022/7693631.

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Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups P < 0.05 . The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively. Conclusions. Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.

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Yin, Yunchao, Derya Yakar, Rudi A. J. O. Dierckx, Kim B. Mouridsen, Thomas C. Kwee y Robbert J. de Haas. "Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging". Diagnostics 12, n.º 2 (21 de febrero de 2022): 550. http://dx.doi.org/10.3390/diagnostics12020550.

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Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.

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Gelardi, Fabrizia, Lara Cavinato, Rita De Sanctis, Gaia Ninatti, Paola Tiberio, Marcello Rodari, Alberto Zambelli et al. "The Predictive Role of Radiomics in Breast Cancer Patients Imaged by [18F]FDG PET: Preliminary Results from a Prospective Cohort". Diagnostics 14, n.º 20 (17 de octubre de 2024): 2312. http://dx.doi.org/10.3390/diagnostics14202312.

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Background: Recently, radiomics has emerged as a possible image-derived biomarker, predominantly stemming from retrospective analyses. We aimed to prospectively assess the predictive role of [18F]FDG-PET radiomics in breast cancer (BC). Methods: Patients affected by stage I–III BC eligible for neoadjuvant chemotherapy (NAC) staged with [18F]FDG-PET/CT were prospectively enrolled. The pathological response to NAC was assessed on surgical specimens. From each primary breast lesion, we extracted radiomic PET features and their predictive role with respect to pCR was assessed. Uni- and multivariate statistics were used for inference; principal component analysis (PCA) was used for dimensionality reduction. Results: We analysed 93 patients (53 HER2+ and 40 triple-negative (TNBC)). pCR was achieved in 44/93 cases (24/53 HER2+ and 20/40 TNBC). Age, molecular subtype, Ki67 percent, and stage could not predict pCR in multivariate analysis. In univariate analysis, 10 radiomic indices resulted in p < 0.1. We found that 3/22 radiomic principal components were discriminative for pCR. Using a cross-validation approach, radiomic principal components failed to discriminate pCR groups but predicted the stage (mean accuracy = 0.79 ± 0.08). Conclusions: This study shows the potential of PET radiomics for staging purposes in BC; the possible role of radiomics in predicting the pCR response to NAC in BC needs to be further investigated.

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Cinarer, Gokalp y Bulent Gursel Emiroglu. "Statistical analysis of radiomic features in differentiation of glioma grades". New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, n.º 12 (30 de abril de 2020): 68–79. http://dx.doi.org/10.18844/gjpaas.v0i12.4988.

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Radiomics is an important quantitative feature extraction tool used in many areas such as image processing and computer-aided diagnosis. In this study, the discriminability of brain cancer tumour grades (Grade II and Grade III) with radiomic features were analysed statistically. The data set consists of 121 patients, 77 patients with Grade II tumours and 44 patients with Grade III tumours. A total of 107 radiomic features were extracted, including three groups of radiomic features such as morphological, first-order and texture. Relationships between the characteristics of each group were tested by Spearman’s correlation analysis. Differences between Grade II and Grade III tumour categories were analysed with Mann–Whitney U test. According to the results, it was seen that radiomic features can be used to differentiate the features of tumour levels evaluated in the same category. These results show that by employing radiomic features brain cancer grade detection can help machine learning technologies and radiological analysis. Keywords: Radiomics, glioma, image processing.

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Chilaca-Rosas, Maria-Fatima, Melissa Garcia-Lezama, Sergio Moreno-Jimenez y Ernesto Roldan-Valadez. "Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation". Diagnostics 13, n.º 5 (23 de febrero de 2023): 849. http://dx.doi.org/10.3390/diagnostics13050849.

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Background: Radiomics refers to a recent area of knowledge that studies features extracted from different imaging techniques and subsequently transformed into high-dimensional data that can be associated with biological events. Diffuse midline gliomas (DMG) are one of the most devastating types of cancer, with a median survival of approximately 11 months after diagnosis and 4–5 months after radiological and clinical progression. Methods: A retrospective study. From a database of 91 patients with DMG, only 12 had the H3.3K27M mutation and brain MRI DICOM files available. Radiomic features were extracted from MRI T1 and T2 sequences using LIFEx software. Statistical analysis included normal distribution tests and the Mann–Whitney U test, ROC analysis, and calculation of cut-off values. Results: A total of 5760 radiomic values were included in the analyses. AUROC demonstrated 13 radiomics with statistical significance for progression-free survival (PFS) and overall survival (OS). Diagnostic performance tests showed nine radiomics with specificity for PFS above 90% and one with a sensitivity of 97.2%. For OS, 3 out of 4 radiomics demonstrated between 80 and 90% sensitivity. Conclusions: Several radiomic features demonstrated statistical significance and have the potential to further aid DMG diagnostic assessment non-invasively. The most significant radiomics were first- and second-order features with GLCM texture profile, GLZLM_GLNU, and NGLDM_Contrast.

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Hu, Yumin, Qiaoyou Weng, Haihong Xia, Tao Chen, Chunli Kong, Weiyue Chen, Peipei Pang, Min Xu, Chenying Lu y Jiansong Ji. "A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer". Abdominal Radiology 46, n.º 6 (junio de 2021): 2384–92. http://dx.doi.org/10.1007/s00261-021-03120-w.

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Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.

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Lei, Chu-qian, Wei Wei, Zhen-yu Liu, Qian-Qian Xiong, Ci-Qiu Yang, Teng Zhu, Liu-Lu Zhang, Mei Yang, Jie Tian y Kun Wang. "Radiomics analysis for pathological classification prediction in BI-RADS category 4 mammographic calcifications." Journal of Clinical Oncology 37, n.º 15_suppl (20 de mayo de 2019): e13055-e13055. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13055.

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e13055 Background: To establish and validate a radiomics-based imaging diagnostic model to predict Breast Imaging Reporting and Data System (BI-RADS) category 4 calcification of breast with mammographic images before biopsy and assess its value. Methods: A total of 212 BI-RADS category 4 pathology-proven mammographic calcifications without obvious mass on mammography were retrospectively enrolled (159 in primary cohort and 53 in validation cohort). All patients received ultrasound inspection and the results were available. 8286 radiomic features were extracted from each mammography images. We utilized machine learning to build a radiomic signature based on optimal features. Independent clinical factors were selected by multivariable logistic regression analysis, and we incorporated the radiomic signatures and risk clinical factors to build a radiomic nomogram. The performance of the radiomic nomogram were assessed by the area under the receiver-operating characteristic curve (AUC). Results: Six features were selected to develop the radiomic signatures based on the primary cohort. Combining with menopausal states, the individualized radiomic nomogram reached an AUC of 0.803 in the validation cohorts, and its clinical utility was confirmed by the decision curve analysis. The difference was significant between the AUC value of differentiating results of the radiomic nomogram compared with ultrasound, mammography and combined modality respectively(p < 0.05 in all three groups). Especially, for patients with MG+/US- calcifications, radiomics nomogram can be screen out benign calcifications. Conclusions: Based on mammographic radiomics, we developed a method for prediction of pathological classification in BI-RADS IV calcification, which has a certain predictive effect.

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Wei, Zhi-Yao, Zhe Zhang, Dong-Li Zhao, Wen-Ming Zhao y Yuan-Guang Meng. "Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer". World Journal of Clinical Cases 12, n.º 26 (16 de septiembre de 2024): 5908–21. http://dx.doi.org/10.12998/wjcc.v12.i26.5908.

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BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. RESULTS Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

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Kalasauskas, Darius, Michael Kosterhon, Naureen Keric, Oliver Korczynski, Andrea Kronfeld, Florian Ringel, Ahmed Othman y Marc A. Brockmann. "Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors". Cancers 14, n.º 3 (7 de febrero de 2022): 836. http://dx.doi.org/10.3390/cancers14030836.

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The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

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Huang, Yen-Cho, Shih-Ming Huang, Jih-Hsiang Yeh, Tung-Chieh Chang, Din-Li Tsan, Chien-Yu Lin y Shu-Ju Tu. "Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy". Diagnostics 14, n.º 9 (30 de abril de 2024): 941. http://dx.doi.org/10.3390/diagnostics14090941.

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Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. Methods: A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. Results: Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. Conclusions: Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models.

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Tesis sobre el tema "Radiomics analysis"

Xu, Chongrui. "Quantitative Radiomic Analysis for Prognostic Medical Applications". Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21517.

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Radiomics, a non-invasive and quantitative mining medical imaging information method, could extract molecular biological features and enormous feature combinations to customise individualised treatment and solve the problem of heterogeneity, satisfying the standards of precision medicine. However, it faces many challenges in the feature selection process, including redundant features, irrelevant features and the overfitting risk. More important, people know little about radiomics biological background and its connection to radiology, so it is difficult to apply radiology directly to medicine as it lacks interpretability. The core of this thesis is radiomic biology analysis that connects radiomic imaging information with molecular biology information to achieve a medical “gold standard” for cancer management. Developing methods to succeed in the feature selection process of data of varying dimensions is the main goal of this paper. Our major contributions in this thesis can be summarised as below: 1. We firstly proposed an unsupervised learning framework to guide supervised learning in the reduction of feature dimensions from large cohorts Non-Small Cell Lung Cancer data (NSCLC) on both clinical data and radiomic data for survival prediction. 2. An interpretable machine learning approach measures the contribution of features for each case and the connection of radiomics to its underlying biological features to make clinical decisions in leukemia and breast cancer cases. The weight of the feature can be estimated by measuring the distance of the approximate perturbation centre. 3. Based on the framework of feature selection that we proposed, to ensure the fairness and stability of the data split when processing classification results, cross-validation is embedded in the training process. We further propose a traversal selection method, optimising the computational complexity of the selection process to obtain the most robust feature set.

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Ortiz, Ramón Rafael. "Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging". Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/119118.

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[ES] En los últimos años, los investigadores han intentado explotar la información de las imágenes médicas a través de la evaluación de parámetros cuantitativos para ayudar a los clínicos con el diagnóstico de enfermedades. Esta práctica ha sido bautizada como radiomics. El análisis de texturas proporciona una gran variedad de parámetros que permiten cuantificar la heterogeneidad característica de diferentes tejidos, especialmente cuando se obtienen de imagen por resonancia magnética (IRM). Basándonos en esto, decidimos estudiar las posibilidades de los parámetros texturales extraídos de IRM para caracterizar varios trastornos cerebrales. El potencial de las texturas se analizó con enfoques de aprendizaje automático, usando diferentes clasificadores y métodos de selección de características para hallar el modelo óptimo para cada tarea específica. En esta tesis, la metodología radiomics se usó para realizar cuatro proyectos independientes. En el primer proyecto, estudiamos la diferenciación entre glioblastomas multiformes (GBMs) y metástasis cerebrales (MCs) en IRM convencional. Estos tipos de tumores cerebrales pueden confundirse al diagnosticarse, ya que presentan un perfil radiológico similar y los datos clínicos pueden no ser concluyentes. Con el fin de evitar procedimientos exhaustivos e invasivos, estudiamos el poder discriminatorio de texturas 2D extraídas de imágenes de referencia T1 filtradas y sin filtrar. Los resultados sugieren que los parámetros texturales proporcionan información sobre la heterogeneidad de los GBMs y las MCs que puede servir para distinguir con precisión ambas lesiones cuando se utiliza un enfoque de aprendizaje automático adecuado. En el segundo proyecto, analizamos la clasificación de las MCs según su origen primario en IRM de referencia. En un porcentaje de pacientes, las MCs son diagnosticadas como la primera manifestación de un tumor primario desconocido. Con el fin de detectar el tumor primario de una forma no invasiva y más rápida, examinamos la capacidad del análisis de texturas 2D y 3D para diferenciar las MCs derivadas de los tumores primarios más propensos a metastatizar (cáncer de pulmón, cáncer de mama y melanoma) en imágenes T1. Los resultados mostraron que se logra una alta precisión al usar un conjunto reducido de texturas 3D para diferenciar MCs de cáncer de pulmón de MCs de cáncer de mama y melanoma. En el tercer proyecto, evaluamos las propiedades del hipocampo en IRM para identificar las diferentes etapas de la enfermedad de Alzheimer (EA). Los criterios actuales para diagnosticar la EA requieren la presencia de déficits cognitivos severos. Con la idea de establecer nuevos biomarcadores para detectar la EA en sus primeras etapas, evaluamos un conjunto de texturas 2D y 3D extraídas de IRM del hipocampo de pacientes con EA avanzada, deterioro cognitivo leve y normalidad cognitiva. Muchos parámetros de textura 3D resultaron ser estadísticamente significativos para diferenciar entre pacientes con EA y sujetos de las otras dos poblaciones. Al combinar estos parámetros con técnicas de aprendizaje automático, se obtuvo una alta precisión. En el cuarto proyecto, intentamos caracterizar los patrones de heterogeneidad del ictus cerebral isquémico en IRM estructural. En IRM cerebral de individuos de edad avanzada, algunos procesos patológicos presentan características similares, como las lesiones por ictus y las hiperintensidades de la sustancia blanca (HSBs). Dado que los ictus afectan también al tejido adyacente, decidimos estudiar la viabilidad de texturas 3D extraídas de las HSBs, la sustancia blanca no afectada y las estructuras subcorticales para diferenciar sujetos afectados por ictus lacunares o corticales visibles en IRM convencional (imágenes T1, T2 y FLAIR) de sujetos sin ictus. Las texturas no sirvieron para diferenciar ictus corticales y lacunares, pero se lograron resultados prometedores para discernir pacientes qu
[CAT] En els últims anys, els investigadors han intentat explotar la informació de les imatges mèdiques a través de l'avaluació de nombrosos paràmetres quantitatius per ajudar els clínics amb el diagnòstic i la valoració de malalties. Aquesta pràctica ha sigut batejada com radiomics,. L'anàlisi de textures proporciona una gran varietat de paràmetres que permeten quantificar l'heterogeneïtat característica de diferents teixits, especialment quan s'obtenen a partir d'imatge per ressonància magnètica (IRM). Basant-nos en aquests fets, vam decidir estudiar les possibilitats dels paràmetres texturals extrets d'IRM per caracteritzar diversos trastorns cerebrals. El potencial de les textures es va analitzar amb mètodes d'aprenentatge automàtic, usant diferents classificadors i mètodes de selecció de característiques per trobar el model òptim per a cada tasca específica. En aquesta tesi, la metodologia radiomics es va emprar per realitzar quatre projectes independents. En el primer projecte, vam estudiar la diferenciació entre glioblastomes multiformes (GBMs) i metàstasis cerebrals (MCs) en IRM convencional. Aquests tipus de tumors cerebrals poden confondre's al diagnosticar-se ja que presenten un perfil radiològic similar i les dades clíniques poden no ser concloents. Per tal d'evitar procediments exhaustius i invasius, vam estudiar el poder discriminatori de textures 2D extretes d'imatges de referència T1 filtrades i sense filtrar. Els resultats suggereixen que els paràmetres texturals proporcionen informació sobre l'heterogeneïtat dels GBMs i les MCs que pot servir per distingir amb precisió ambdues lesions quan s'utilitza una aproximació d'aprenentatge automàtic adequada. En el segon projecte, vam analitzar la classificació de MCs segons el seu origen primari en IRM de referència. En un percentatge de pacients, les MCs són diagnosticades com la primera manifestació d'un tumor primari desconegut. Per tal de detectar el tumor primari d'una forma no invasiva i més ràpida, vam examinar la capacitat de l'anàlisi de textura 2D i 3D per diferenciar les MCs derivades dels tumors primaris més propensos a metastatitzar (càncer de pulmó, càncer de mama i melanoma) en imatges T1. Els resultats van mostrar que s'aconsegueix una alta precisió quan s'utilitza un conjunt reduït de textures 3D per diferenciar les MCs de càncer de pulmó de les MCs de càncer de mama i melanoma. En el tercer projecte, vam avaluar les propietats de l'hipocamp en la IRM per identificar les diferents etapes de la malaltia d'Alzheimer (MA). Els criteris actuals per diagnosticar la MA requereixen la presència de dèficits cognitius severs. Amb la idea d'establir nous biomarcadors per detectar la MA en les seues primeres etapes, vam avaluar un conjunt de textures 2D i 3D extretes d'IRM de l'hipocamp de pacients amb MA avançada, deteriorament cognitiu lleu i normalitat cognitiva. Molts paràmetres de textura 3D van resultar ser estadísticament significatius per diferenciar entre pacients amb MA i individus de les altres dues poblacions. En combinar aquests paràmetres amb tècniques d'aprenentatge automàtic, es va obtenir una alta precisió. En el quart projecte, vam intentar caracteritzar els patrons d'heterogeneïtat de l'ictus cerebral isquèmic en la IRM estructural. En la IRM cerebral d'individus d'edat avançada, alguns processos patològics presenten característiques similars, com les lesions per ictus i les hiperintensitats de la substància blanca (HSBs). Atès que els ictus tenen efecte també en teixit adjacent, vam decidir estudiar la viabilitat de textures 3D extretes de les HSBs, la substància blanca no afectada i les estructures subcorticals per diferenciar individus afectats per ictus llacunars o corticals visibles en IRM convencional (imatges T1, T2 i FLAIR) d'individus sense ictus. Les textures no foren útils per diferenciar ictus corticals i llacunars, però es van obtenir resultats prometedors per disce
[EN] Over the last years, researchers have attempted to exploit the information provided by medical images through the evaluation of numerous imaging quantitative parameters in order to help clinicians with the diagnosis and assessment of many lesions and diseases. This practice has been recently named as radiomics. Texture analysis supply a wide range of features that allow quantifying the distinctive heterogeneity of different tissues, especially when obtained from magnetic resonance imaging (MRI). With this in mind, we decided to study the possibilities of texture features from MRI in order to characterize several disorders that affect the human brain. The potential of texture features was analyzed with various machine learning approaches, involving different classifiers and feature selection methods so as to find the optimal model to accomplish each specific task. In this thesis, the radiomics methodology was used to perform four independent projects. In the first project, we studied the differentiation between glioblastomas (GBMs) and brain metastases (BMs) in conventional MRI. Sometimes these types of brain tumors can be misdiagnosed since they may present a similar radiological profile and the clinical data may be inconclusive. With the aim of avoiding exhaustive and invasive procedures, we studied the discriminatory power of a large amount of 2D texture features extracted from baseline original and filtered T1-weighted images. The results suggest that 2D texture features provide some heterogeneity information of GBMs and BMs that can help in their accurate discernment when using the proper machine learning approach. In the second project, we analyzed the classification of BMs by their primary site of origin in baseline MRI. A percentage of patients are diagnosed with BM as the first manifestation of an unknown primary tumor. In order to detect the primary tumor in a faster non-invasive way, we examined the capability of 2D and 3D texture analysis to differentiate BMs derived from the most common primary tumors (lung cancer, breast cancer and melanoma) in T1-weighted images. The results showed that high accuracy was achieved when using a reduced set of 3D descriptors to differentiate lung cancer BMs from breast cancer and melanoma BMs. In the third project, we evaluated the hippocampus MRI profile of Alzheimer's disease (AD) patients to identify the different stages of the disease. The current criteria for diagnosing AD require the presence of relevant cognitive deficits. With the purpose of establishing new biomarkers to detect AD in its early stages, we evaluated a set of 2D and 3D texture features extracted from MRI scans of the hippocampus of patients with advanced AD, early mild cognitive impairment and cognitive normality. Many 3D texture parameters resulted to be statistically significant to differentiate between AD patients and subjects from the other two populations. When combining these 3D parameters with machine learning techniques, high accuracy was obtained. In the fourth project, we attempted to characterize the heterogeneity patterns of ischemic stroke in structural MRI. In brain MRI of older individuals, some pathological processes present similar imaging characteristics, like in the case of stroke lesions and white matter hyperintensities (WMH) of diverse natures. Given that stroke effects are present not only in the affected region, but also in unaffected tissue, we investigated the feasibility of 3D texture features from WMH, normal-appearing white matter and subcortical structures to differentiate individuals who had a lacunar or cortical stroke visible on conventional brain MRI (T1-weighted, T2-weighted and FLAIR images) from subjects who did not. Texture features were not useful to differentiate between post-acute cortical and lacunar strokes, but promising results were achieved for discerning between patients presenting an old stroke and normal-ageing patients who never had a stroke.
Ortiz Ramón, R. (2019). Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/119118
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Iyer, Sukanya Raj. "Deformation heterogeneity radiomics to predict molecular sub-types and overall survival in pediatric Medulloblastoma". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1588601774292049.

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Wang, Dingqian. "Quantitative analysis with machine learning models for multi-parametric brain imaging data". Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/22245.

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Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping.

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Boughdad, Sarah. "Contributions of radiomics in ¹⁸F-FDG PET/CT and in MRI in breast cancer". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS500.

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Le cancer du sein est une pathologie fréquente pour lequel les examens TEP/TDM au ¹⁸F-FDG et IRM mammaire sont fréquemment réalisés en routine. Il existe cependant une sous-utilisation des informations apportées par chacune de ces techniques d'imagerie. En pratique, l’interprétation de ces examens est principalement basée sur l’analyse visuelle et l'analyse « quantitative » se résume généralement au SUVmax seul en TEP/TDM et à l’étude du rehaussement du signal après injection de produit de contraste en IRM mammaire (DCE-MRI). L’arrivée de nouvelles machines hybrides TEP/ IRM, nous a amené à évaluer l'apport d’une quantification avancée des images issues de chacune de ces modalités séparément et en combinaison. Cela rejoint un domaine en expansion « la radiomique » qui consiste à extraire un grand nombre de caractéristiques quantitatives des images médicales pour décrypter l’hétérogénéité tumorale ou améliorer la prédiction du pronostic.L’objectif de notre travail était d’étudier l’apport des données radiomiques extraites de l’imagerie TEP au ¹⁸F-FDG et de l’IRM avec injection de produit de contraste réalisées avant traitement pour caractériser l’hétérogénéité tumorale dans le cancer du sein, en prenant en compte les différents sous-types moléculaires de cancer du sein, à savoir les tumeurs luminales (Lum A, Lum B HER2- et Lum B HER2+), triple-négatives et HER2+. Une importance particulière a été portée sur la valeur prédictive des informations radiomiques extraites de ces 2 techniques d’imagerie pour prédire le pronostic dans un groupe de patientes traitées par chimiothérapie néo-adjuvante. L’influence de variations physiologiques telles que l’âge sur le calcul des données radiomiques dans le tissu mammaire normal et cancéreux séparément a également été explorée, de même que la variabilité multicentrique des index radiomiques. L’extraction de ces données radiomiques a été effectuée grace au logiciel LiFex développé au sein du laboratoire IMIV sur une base de données-patientes recueillie en rétrospective.Nous avons rapporté pour la première fois l’influence de l’âge sur le calcul des indices « radiomiques » en TEP dans le tissu mammaire sain dans 2 institutions différentes mais aussi dans les tumeurs mammaires notamment celle triple-négatives. Des associations significatives entre le « phénotype tumoral radiomique » en imagerie TEP et IRM et des données pronostiques reconnues dans le cancer du sein ont été mises en évidence. En outre, nous avons démontré l’existence d’une grande variabilité pour le « profil radiomique » en TEP parmi les tumeurs présentant le même sous-type moléculaire. Cela suggére l’existence d’informations non-redondantes au sein du « phénotype tumoral métabolique » de chaque tumeur mammaire défini par les données radiomiques. L’exploration de cette variabilité s’est révélée intéressante pour améliorer la prédiction de la réponse histologique chez les patientes avec des tumeurs triple-négatives traitées par chimiothérapie néo-adjuvante. Par ailleurs, les mesures effectuées dans la région mammaire péri-tumorale chez les patientes traitées par chimiothérapie néo-adjuvante se sont montrées prédictives pour les patientes avec des tumeurs Lum B HER2-. En IRM nous avons montré l’importance de standardiser la méthode de mesure des caractéristiques radiomiques. Nous avons observé que les caractéristiques radiomiques issues des images DCE-MRI étaient moins associées aux caractéristiques moléculaires des tumeurs et avaient une valeur prédictive moindre. Nous avons également proposé une nouvelle méthode relativement standardisée pour le calcul des données radiomiques en IRM mammaire avec des résultats intéressants mais cette méthode doit encore être optimisée. Cependant, nos résultats suggèrent que les données extraites de la totalité du volume tumorale en IRM compléteraient efficacement les caractéristiques radiomiques TEP et le sous-type moléculaire pour prédire la réponse à la chimiothérapie néo-adjuvante
Breast cancer is a common disease for which ¹⁸F-FDG PET/CT and breast MRI are frequently performed in routine practice. However, the different information provided by each of these imaging techniques are currently under-exploited. Indeed, in routine the interpretation of these scans is mainly based on visual analysis whereas the « quantitative » analysis of PET/CT data is generally limited to the sole use of the SUVmax while in breast MRI, simple parameters to characterize tumor enhancement after injection of contrast medium are used. The advent of PET/MRI machines, calls for an evaluation of the contribution of a more advanced quantification of each of the modalities separately and in combination in the setting of breast cancer. This is along with the concept of « Radiomics » a field currently expanding and which consists in extracting many quantitative characteristics from medical images used in clinical practice to decipher tumor heterogeneity or improve prediction of prognosis. The aim of our work was to study the contribution of radiomic data extracted from ¹⁸F-FDG PET and MRI imaging with contrast injection to characterize tumor heterogeneity in breast cancer taking into account the different molecular subtypes of breast cancer, namely luminal (Lum A, Lum B HER2- and Lum B HER2 +), triple-negative and HER2 + tumors. In this context, we focused on the prediction of prognosis in patients treated with neo-adjuvant chemotherapy. The influence of physiological variations such as age on the calculation of radiomic data in normal breast and breast tumors separately was also explored, as well as the multi-center variability of radioman features. Radiomic features were extracted using the LiFex software developed within IMIV laboratory. The patient database used for the studies were all retrospective data. We reported for the first time the influence of age on the values of radiomic features in healthy breast tissue in patients recruited from 2 different institutions but also in breast tumors especially those with a triple-negative subtype. Similarly, significant associations between the radiomic tumor phenotype in PET and MRI imaging and well-established prognostic factors in breast cancer have been identified. In addition, we showed a large variability in the PET « radiomic profile » of breast tumors with similar breast cancer subtype suggesting complementary information within their metabolic phenotype defined by radiomic features. Moreover, taking into account this variability has been shown to be of particular interest in improving the prediction of pathological response in patients with triple-negative tumors treated with neoadjuvant chemotherapy. A peri-tumoral breast tissue region satellite to the breast tumor was also investigated and appeared to bear some prognostic information in patients with Lum B HER2- tumors treated with neoadjuvant chemotherapy. In MR, we demonstrated the need to harmonize the methods for radiomic feature calculation. Overall, we observed that radiomic features derived from MR were less informative about the molecular features of the tumors than radiomic features extracted from PET data and were of lower prognostic value. Yet, the combination of the enhanced tumor volume in MR with a PET radiomic feature and the tumor molecular subtype yielded enhanced the accuracy with which response to neoadjuvant therapy could be predicted compared to features from one modality only or molecular subtype only

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Mahon, Rebecca N. "Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty". VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5516.

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ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2018 Major Director: Dr. Elisabeth Weiss, Professor, Department of Radiation Oncology Radiomics, an advanced form of imaging analysis, is a growing field of interest in medicine. Radiomics seeks to extract quantitative information from images through use of computer vision techniques to assist in improving treatment. Early prediction of treatment response is one way of improving overall patient care. This work seeks to explore the feasibility of building predictive models from radiomic texture features extracted from magnetic resonance (MR) and computed tomography (CT) images of lung cancer patients. First, repeatable primary tumor texture features from each imaging modality were identified to ensure a sufficient number of repeatable features existed for model development. Then a workflow was developed to build models to predict overall survival and local control using single modality and multi-modality radiomics features. The workflow was also applied to normal tissue contours as a control study. Multiple significant models were identified for the single modality MR- and CT-based models, while the multi-modality models were promising indicating exploration with a larger cohort is warranted. Another way advances in imaging analysis can be leveraged is in improving accuracy of contours. Unfortunately, the tumor can be close in appearance to normal tissue on medical images creating high uncertainty in the tumor boundary. As the entire defined target is treated, providing physicians with additional information when delineating the target volume can improve the accuracy of the contour and potentially reduce the amount of normal tissue incorporated into the contour. Convolution neural networks were developed and trained to identify the tumor interface with normal tissue and for one network to identify the tumor location. A mock tool was presented using the output of the network to provide the physician with the uncertainty in prediction of the interface type and the probability of the contour delineation uncertainty exceeding 5mm for the top three predictions.

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Oliver, Jasmine Alexandria. "Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis". Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6123.

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Positron Emission Tomography (PET) is an imaging modality that has become increasingly beneficial in Radiotherapy by improving treatment planning (1). PET reveals tumor volumes that are not well visualized on computed tomography CT or MRI, recognizes metastatic disease, and assesses radiotherapy treatment (1). It also reveals areas of the tumor that are more radiosensitive allowing for dose painting - a non-homogenous dose treatment across the tumor (1). However, PET is not without limitations. The quantitative unit of PET images, the Standardized Uptake Value (SUV), is affected by many factors such as reconstruction algorithm, patient weight, and tracer uptake time (2). In fact, PET is so sensitive that a patient imaged twice in a single day on the same machine and same protocol will produce different SUV values. The objective of this research was to increase the capabilities of PET by exploring other quantitative PET/CT measures for Radiotherapy treatment applications. The technique of quantitative image feature analysis, nowadays known as radiomics, was applied to PET and CT images. Image features were then extracted from PET/CT images and how the features differed between conventional and respiratory-gated PET/CT images in lung cancer was analyzed. The influence of noise on image features was analyzed by applying uncorrelated, Gaussian noise to PET/CT images and measuring how significantly noise affected features. Quantitative PET/CT measures outside of image feature analysis were also investigated. The correlation of esophageal metabolic tumor volumes (tumor volume demonstrating high metabolic uptake) and endoscopically implanted fiducial markers was studied. It was found that certain image features differed greatly between conventional and respiratory-gated PET/CT. The differences were mainly due to the effect of respiratory motion including affine motion, rotational motion and tumor deformation. Also, certain feature groups were more affected by noise than others. For instance, contour-dependent shape features exhibited the least change with noise. Comparatively, GLSZM features exhibited the greatest change with added noise. Discordance was discovered between the inferior and superior tumor fiducial markers and metabolic tumor volume (MTV). This demonstrated a need for both fiducial markers and MTV to provide a comprehensive view of a tumor. These studies called attention to the differences in features caused by factors such as motion, acquisition parameters, and noise, etc. Investigators should be aware of these effects. PET/CT radiomic features are indeed highly affected by noise and motion. For accurate clinical use, these effects must be account by investigators and future clinical users. Further investigation is warranted towards the standardization of PET/CT radiomic feature acquisition and clinical application.

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Prasanna, Prateek. "NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS". Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.

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Chirra, Prathyush V. Chirra. "EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI". Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062.

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Basu, Satrajit. "Developing Predictive Models for Lung Tumor Analysis". Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/3963.

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A CT-scan of lungs has become ubiquitous as a thoracic diagnostic tool. Thus, using CT-scan images in developing predictive models for tumor types and survival time of patients afflicted with Non-Small Cell Lung Cancer (NSCLC) would provide a novel approach to non-invasive tumor analysis. It can provide an alternative to histopathological techniques such as needle biopsy. Two major tumor analysis problems were addressed in course of this study, tumor type classification and survival time prediction. CT-scan images of 109 patients with NSCLC were used in this study. The first involved classifying tumor types into two major classes of non-small cell lung tumors, Adenocarcinoma and Squamous-cell Carcinoma, each constituting 30% of all lung tumors. In a first of its kind investigation, a large group of 2D and 3D image features, which were hypothesized to be useful, are evaluated for effectiveness in classifying the tumors. Classifiers including decision trees and support vector machines (SVM) were used along with feature selection techniques (wrappers and relief-F) to build models for tumor classification. Results show that over the large feature space for both 2D and 3D features it is possible to predict tumor classes with over 63% accuracy, showing new features may be of help. The accuracy achieved using 2D and 3D features is similar, with 3D easier to use. The tumor classification study was then extended by introducing the Bronchioalveolar Carcinoma (BAC) tumor type. Following up on the hypothesis that Bronchioalveolar Carcinoma is substantially different from other NSCLC tumor types, a two-class problem was created, where an attempt was made to differentiate BAC from the other two tumor types. To make a three-class problem a two-class problem, misclassification amongst Adenocarcinoma and Squamous-cell Carcinoma were ignored. Using the same prediction models as the previous study and just 3D image features, tumor classes were predicted with around 77% accuracy. The final study involved predicting two year survival time in patients suffering from NSCLC. Using a subset of the image features and a handful of clinical features, predictive models were developed to predict two year survival time in 95 NSCLC patients. A support vector machine classifier, naive Bayes classifier and decision tree classifier were used to develop the predictive models. Using the Area Under the Curve (AUC) as a performance metric, different models were developed and analyzed for their effectiveness in predicting survival time. A novel feature selection method to group features based on a correlation measure has been proposed in this work along with feature space reduction using principal component analysis. The parameters for the support vector machine were tuned using grid search. A model based on a combination of image and clinical features, achieved the best performance with an AUC of 0.69, using dimensionality reduction by means of principal component analysis along with grid search to tune the parameters of the SVM classifier. The study showed the effectiveness of a predominantly image feature space in predicting survival time. A comparison of the performance of the models from different classifiers also indicate SVMs consistently outperformed or matched the other two classifiers for this data.

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Libros sobre el tema "Radiomics analysis"

Ma, Xuelei, Lei Deng, Rong Tian y Chunxiao Guo, eds. Novel Methods for Oncologic Imaging Analysis: Radiomics, Machine Learning, and Artificial Intelligence. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-347-9.

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Capítulos de libros sobre el tema "Radiomics analysis"

Veeraraghavan, Harini. "Radiomics analysis for gynecologic cancers". En Radiomics and Radiogenomics, 319–35. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2019] |: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351208277-19.

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Ghosh, Adarsh y Suraj D. Serai. "Radiomics and Texture Analysis". En Advanced Clinical MRI of the Kidney, 407–18. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40169-5_27.

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Chen, Qingfeng. "Fusion and Radiomics Study of Multimodal Medical Images". En Association Analysis Techniques and Applications in Bioinformatics, 301–24. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8251-6_10.

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Yang, Jiancheng, Rongyao Fang, Bingbing Ni, Yamin Li, Yi Xu y Linguo Li. "Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis". En Lecture Notes in Computer Science, 658–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32226-7_73.

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Morvan, Ludivine, Cristina Nanni, Anne-Victoire Michaud, Bastien Jamet, Clément Bailly, Caroline Bodet-Milin, Stephane Chauvie et al. "Learned Deep Radiomics for Survival Analysis with Attention". En Predictive Intelligence in Medicine, 35–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59354-4_4.

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El Naqa, Issam. "Computerized Prediction of Treatment Outcomes and Radiomics Analysis". En Image-Based Computer-Assisted Radiation Therapy, 357–75. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2945-5_14.

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Klontzas, Michail E. y Renato Cuocolo. "Machine Learning Methods for Radiomics Analysis: Algorithms Made Easy". En Imaging Informatics for Healthcare Professionals, 69–85. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25928-9_4.

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Shi, Zhenwei, Chong Zhang, Inge Compter, Maikel Verduin, Ann Hoeben, Danielle Eekers, Andre Dekker y Leonard Wee. "A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma". En Radiomics and Radiogenomics in Neuro-oncology, 70–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40124-5_8.

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Piantadosi, Gabriele, Giampaolo Bovenzi, Giuseppe Argenziano, Elvira Moscarella, Domenico Parmeggiani, Ludovico Docimo y Carlo Sansone. "Skin Lesions Classification: A Radiomics Approach with Deep CNN". En New Trends in Image Analysis and Processing – ICIAP 2019, 252–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30754-7_26.

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Ali, Muhammad, Viviana Benfante, Giuseppe Cutaia, Leonardo Salvaggio, Sara Rubino, Marzia Portoghese, Marcella Ferraro et al. "Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI". En Image Analysis and Processing - ICIAP 2023 Workshops, 83–92. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51026-7_8.

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Actas de conferencias sobre el tema "Radiomics analysis"

Filos, Dimitris, Dimitris Fotopoulos, Maria Anastasia Rouni y Ioanna Chouvarda. "Machine Learning-Based Whole Gland Radiomics Analysis for Prostate Cancer Classification". En 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635588.

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Smith, R. L., K. Al-Battat, R. John, M. Li, I. Ackerley, E. Spezi, K. Wells, N. Morley y C. Marshall. "From Radiomics to Deep Learning: Leveraging Gramian Matrix Features in CNNs for NSCLC Survival Analysis". En 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10657697.

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Ahmadyar, Y., R. Samimi, A. Kamali-Asl, J. Majidpour, H. Arabi y H. Zaidi. "Predicting Neoadjuvant Therapy Response in Breast Cancer Patients via Radiomics Analysis of Dynamic Contrast-Enhanced MRI Imaging Features". En 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655295.

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Duman, A., J. Powell, S. Thomas y E. Spezi. "Evaluation of Radiomic Analysis over the Comparison of Machine Learning Approach and Radiomic Risk Score on Glioblastoma". En Cardiff University Engineering Research Conference 2023. Cardiff University Press, 2024. http://dx.doi.org/10.18573/conf1.f.

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Accurate patient prognosis is important to provide an effective treatment plan for Glioblastoma (GBM) patients. Radiomics analysis extracts quantitative features from medical images. Such features can be used to build models to support medical decisions for diagnosis, prognosis, and therapeutic response. The progress of radiomics analysis is continuously improving. The aim of this research is to extract standardised radiomic features from MRI scans of GBM patients, perform feature selection, and compare radiomic-based risk score (RRS) and machine learning (ML) approaches for the risk stratification of GBM patients. We have also tested the generalisability of these models which is crucial for clinical implementation. Our work demonstrates that a stratification model based on logistic regression generalised better than the RRS method when applied to new unseen datasets.

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Colter, L., J. Kohlhammer, S. Wesarg, F. Jung, I. Stenin, C. Plettenberg, J. Schipper y K. Scheckenbach. "Multimodal "Radiomics" data analysis and visualization". En Abstract- und Posterband – 89. Jahresversammlung der Deutschen Gesellschaft für HNO-Heilkunde, Kopf- und Hals-Chirurgie e.V., Bonn – Forschung heute – Zukunft morgen. Georg Thieme Verlag KG, 2018. http://dx.doi.org/10.1055/s-0038-1639818.

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Chaddad, Ahmad. "Stability in Radiomics Analysis: Advancements and Challenges". En 2023 IEEE International Conference on E-health Networking, Application & Services (Healthcom). IEEE, 2023. http://dx.doi.org/10.1109/healthcom56612.2023.10472376.

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Haniff, Nurin Syazwina Mohd, Muhammad Khalis Bin Abdul Karim, Nur Syafina Ali, Mohd Amiruddin Abdul Rahman, Nurul Huda Osman y M. Iqbal Saripan. "Magnetic Resonance Imaging Radiomics Analysis for Predicting Hepatocellular Carcinoma". En 2021 International Congress of Advanced Technology and Engineering (ICOTEN). IEEE, 2021. http://dx.doi.org/10.1109/icoten52080.2021.9493533.

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Luna, Eduardo Almeda, José María Luna y Sebastián Ventura. "Radiomics Software Tools: A comparative Analysis on Breast Cancer". En 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2023. http://dx.doi.org/10.1109/cbms58004.2023.00276.

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S, Sherly Angel, Nidhi N. Nishanimath y Nandish S. "Radiomics Features Analysis From Lung Cancer Using CT Images". En 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2021. http://dx.doi.org/10.1109/conecct52877.2021.9622697.

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Oyibo, P., P. Brynolfsson y E. Spezi. "Integrating Radiomic Image Analysis in the Hero Imaging Platform". En Cardiff University School of Engineering Research Conference, 23–27. Cardiff University Press, 2024. http://dx.doi.org/10.18573/conf3.g.

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We present the integration of radiomic image analysis in the Hero Imaging platform. We created a SPAARC node on Hero Imaging Platform, which facilitates access to SPAARC’s cutting-edge functionality with the visual programming simplicity of the Hero Imaging platform. Subsequently, we validated the reliability of the SPAARCHero using the IBSI validation dataset of multimodal imaging (CT, F18-FDG -PET, and T1w MRI) comprising 51 patients with soft-tissue sarcoma. This validation involved evaluation of the ICC of SPAARCHero and two other IBSI compliant software, MIRP and SPAARCMATLAB using IBSI 2 phase 3 configuration and Test cases. Out of the 486 extracted features, 462 were found to be reproducible across the three software, with lower bounds of 95% confidence intervals (CIs) of ICCs greater than 0.75. Our results verify the compliance of SPAARCHero with IBSI standard for Radiomics feature extraction.

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Informes sobre el tema "Radiomics analysis"

Ouyang, Zhiqiang, Qian Li, Guangrong Zheng, Tengfei Ke, Jun Yang y Chengde Liao. Radiomics for predicting tumor microenvironment phenotypes in non-small cell lung cance: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, septiembre de 2022. http://dx.doi.org/10.37766/inplasy2022.9.0060.

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Review question / Objective: Tumor microenvironment (TIME) phenotype is an important factor to affect the response and prognosis of immunotherapy in non-small cell lung cancer (NSCLC). Recently, accumulating studies have noninvasivly perdited the TIME phenotypes of NSCLC by using CT or PET/CT based radiomics. We will conduct this study by means of meta-analysis to eveluate the power and value of CT or PET/CT based radiomics for predicting TIME phenotypes in NSCLC patients. Condition being studied: At present, several recent prospective or retrospective cohort studies and randomized controlled studies have confirmed that CT or PET/CT-based radiomics were the potential tools to predict TIME phenotypes in NSCLC. However, this conclusion is controversial because of the difference of prediction profermance of different studies. The published and unpublished investigations will be included in this study. We will comprehensively evaluate the heterogeneity of these investigations, and the power and value of radiomics for predicting TIME phenotypes.

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Chen, Jie, Xinyue Zhang, Chi Xu y Kefu Liu. Diagnostic Performance of Radiomics Analysis for Pulmonary Cancer Airway Spread: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, octubre de 2024. http://dx.doi.org/10.37766/inplasy2024.10.0103.

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Wang, Chih-Keng, Ting-Wei Wang, Chia-Fung Lu y Yu-Te Wu. Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, febrero de 2024. http://dx.doi.org/10.37766/inplasy2024.2.0101.

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Chang, Ke-Vin. Ultrasound Radiomics for Diagnosing Carpal Tunnel Syndrome: a Protocol for Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, septiembre de 2023. http://dx.doi.org/10.37766/inplasy2023.9.0069.

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Yang, Jiawen, Shuzong You, Limin Zhang, Huangqi Zhang, Binhao Zhang, Xue Dong, Wenting Pan, Shaofeng Duan y Wenbin Ji. Prediction Power of Radiomics in Early Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, enero de 2022. http://dx.doi.org/10.37766/inplasy2022.1.0099.

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Wang, Yingxuan, Cheng Yan y Liqin Zhao. The value of radiomics-based machine learning for hepatocellular carcinoma after TACE: a systematic evaluation and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, junio de 2022. http://dx.doi.org/10.37766/inplasy2022.6.0100.

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Review question / Objective: Meta-analysis was performed to predict the efficacy and survival status of patients with hepatocellular carcinoma after the application of TACE, applying clinical models, radiomic models and combined models for non-invasive assessment.We performed a Meta-analysis on the prediction of efficacy and survival status after TACE for hepatocellular carcinoma. Condition being studied: Patients were scanned using CT or MR machines, and some patients had multiple follow-up records, and imaging feature extraction software was applied to extract regions of interest and build multiple prediction models.Literature screening was conducted by two reviewers independently, who had more than 3 years’ experience in imaging diagnosis and was cross-checked. Disagreements were settled by a third reviewer.

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zheng, xiushan. CT-based radiomics for prediction of lymph node metastasis in lung cancer A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, marzo de 2022. http://dx.doi.org/10.37766/inplasy2022.3.0167.

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