Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features

作者：Howard E. Morgan, Kai Wang, Michael Dohopolski, Xiao Liang, Michael R. Folkert, David J. Sher, Jing Wang · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2021 · DOI：10.21037/qims-21-274 · 被引用次数：23 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Head and Neck Cancer Studies、Advanced Radiotherapy Techniques

Background: Local failure (LF) following chemoradiation (CRT) for head and neck cancer is associated with poor overall survival. If machine learning techniques could stratify patients at risk of treatment failure based on baseline and intra-treatment imaging, such a model could facilitate response-adapted approaches to escalate, de-escalate, or switch therapy. Methods: A 1:2 retrospective case control cohort of patients treated at a single institution with definitive radiotherapy for head and neck cancer who failed locally, in-field at a primary or nodal structure were included. Radiomic features were extracted from baseline CT and CBCT scans at fractions 1 and 21 (delta) of radiotherapy with PyRadiomics and were selected for by: reproducibility (intra-class correlation coefficients ≥0.95), redundancy [maximum relevance and minimum redundancy (mRMR)], and informativeness [recursive feature elimination (RFE)]. Separate models predicting LF of primaries or nodes were created using the explainable boosting machine (EBM) classifier with 5-fold cross-validation for (I) clinical only, (II) radiomic only (CT1 and delta features), and (III) fused models (clinical + radiomic). Twenty-five iterations were performed, and predicted scores were averaged with a parallel ensemble design. Receiver operating characteristic curves were compared between models with paired-samples t-tests. Results: The fused ensemble model for primaries (using clinical, CT1, and delta features) achieved an AUC o...