Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer

By Chintan Parmar, Patrick Grossmann, D. Rietveld, M. Rietbergen, Philippe Lambin, Hugo J. W. L. Aerts. Frontiers in Oncology, 2015

381 citations recorded by the source corpus.

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. Methods Two 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. Results We 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). Conclusion Our 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.

Subfields: Health Informatics, Otorhinolaryngology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/640e70226454d4ed8934c40183b31ad4a057fbf8

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Citations in the corpus

Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, methodology): “Cross-validation has frequently been used as internal validation with the AUC of the receiver-operating characteristic curve (or its generalization, concordance index) evaluating the performance accuracy.(7,37,91)”
Data Analysis Strategies in Medical Imaging (2018, methodology): “To avoid this, use of standard settings for hyperparameters(50) or locked and independent validation data(51) is recommended.”
Characterization of PET/CT images using texture analysis: the past, the present… any future? (2016, methodology): “Contrary to the field of PET/CT, the use of machine-learning techniques including robust feature selection, the combination of features within classifiers and cross-validation or validation in an external cohort are well established in the fields of CT [7, 77, 108, 109] and MR [110–114] radiomics.”