Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans

作者：Rajesh P. Shah, Heather M. Selby, Pritam Mukherjee, Shefali S. Verma, Peiyi Xie, Qinmei Xu, Millie Das, Sachin B. Malik, Olivier Gevaert, Sandy Napel · 发表于：JCO Clinical Cancer Informatics · 年份：2021 · DOI：10.1200/cci.21.00021 · 被引用次数：19 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Research Studies、Lung Cancer Diagnosis and Treatment

PURPOSE: Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size. MATERIALS AND METHODS: Computed tomography scans from a single institution were selected and resampled to 1 × 1 × 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance. RESULTS: A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve...