Dataset Information

Machine Learning methods for Quantitative Radiomic Biomarkers.

ABSTRACT: Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84?± 0.05, AUC = 0.65?± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66?±?0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.

SUBMITTER: Parmar C

PROVIDER: S-EPMC4538374 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Machine Learning methods for Quantitative Radiomic Biomarkers.

Parmar Chintan C Grossmann Patrick P Bussink Johan J Lambin Philippe P Aerts Hugo J W L HJWL

Scientific reports 20150817

Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed to ...[more]

PMID: 26278466

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Project description:Introduction"Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine-learning approaches are required. In this radiomic study, 13 feature selection methods and 11 machine-learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients.MethodsTwo independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted of 101 head and neck cancer patients. Cohort HN2 (n?=?95) was used for validation. A total of 440 radiomic features were extracted from the segmented tumor regions in CT images. Feature selection and classification methods were compared using an unbiased evaluation framework.ResultsWe observed that the three feature selection methods minimum redundancy maximum relevance (AUC?=?0.69, Stability?=?0.66), mutual information feature selection (AUC?=?0.66, Stability?=?0.69), and conditional infomax feature extraction (AUC?=?0.68, Stability?=?0.7) had high prognostic performance and stability. The three classifiers BY (AUC?=?0.67, RSD?=?11.28), RF (AUC?=?0.61, RSD?=?7.36), and NN (AUC?=?0.62, RSD?=?10.52) also showed high prognostic performance and stability. Analysis investigating performance variability indicated that the choice of classification method is the major factor driving the performance variation (29.02% of total variance).ConclusionOur study identified prognostic and reliable machine-learning methods for the prediction of overall survival of head and neck cancer patients. Identification of optimal machine-learning methods for radiomics-based prognostic analyses could broaden the scope of radiomics in precision oncology and cancer care.

Project description:Background: Microglia plays complex and crucial roles in multiple sclerosis (MS). This study aimed to explore the biological significance of microglia-associated genes in experimental autoimmune encephalomyelitis (EAE) . Methods: Differentially expressed genes (DEGs) were screened with six machine learning (ML) methods, which were also utilized to validate the microglia-associated DEGs in three public databases. ceRNA and Protein–protein interaction (PPI) network analyses were utilized to identify the interaction of the 6 novel biomarkers with other molecules. Then, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were employed to quantify the relative abundance of each immune cell infiltration, respectively. qRT-PCR was performed to test the expression of key DEGs in murine models. Results: A total of 247 DEmRNA, 499 DElncRNAs and 269 DEcircRNAs were identified. With screening strategy of five ML algorithms, 6 DEmRNAs were obtained including NGP, HIST1H2BJ, PBLD1, MBLN3, CD180 and F10. Then the 6 DEmRNAs were used as a multigene signature to construct models to differentiate EAE from normal microglia, and AUC value for each model was greater than 0.8. The diagnostic value of these 6 DEmRNAs were identified and further verified by qRT-PCR. Then, differential expression for five out of these 6 DEmRNAs, namely NGP, HIST1H2BJ, PBLD1, MBLN3, and F10 were confirmed. Using PPI analysis, DEmRNAs frequently interacting with transcription factors (TFs), potential drugs and RBPs were identified. With immune cell infiltration analyses, we found EAE microglia presented high levels of immune infiltration, especially Nature Killer (NK) cells and CD8+ T cells. We also reported circRNA (circRNA_00638) was predicted to bind to 76 RBPs. Conclusions: We identified and validated 6 novel microglia related genes and developed a multigene signature with ML methods to confirm their ability to accurately diagnose and characterize biological alterations in EAE microglia. The six key DEmRNAs might also be latent targets for immunoregulatory therapy.

Project description:ObjectiveTo develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.MethodsOverall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.ResultsFor clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)).ConclusionCombination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.

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Machine Learning methods for Quantitative Radiomic Biomarkers.

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Machine Learning methods for Quantitative Radiomic Biomarkers.

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