Habitat Imaging-Based 18F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases - Chen, Ling; Liu, Kanfeng; Zhao, Xin; Shen, Hui; Zhao, Kui; Zhu, Wentao (Frontiers in Oncology, 2021)
Purpose: To propose and evaluate habitat imaging-based 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics for preoperatively discriminating non-small cell lung cancer (NSCLC) and benign inflammatory diseases (BIDs). Methods: Three hundred seventeen 18F-FDG PET/CT scans were acquired from patients who underwent aspiration biopsy or surgical resection. All volumes of interest (VOIs) were semiautomatically segmented. Each VOI was separated into variant subregions, namely, habitat imaging, based on our adapted clustering-based habitat generation method. Radiomics features were extracted from these subregions. Three feature selection methods and six classifiers were applied to construct the habitat imaging-based radiomics models for fivefold cross validation. The radiomics models whose features extracted by conventional habitat-based methods and nonhabitat method were also constructed. For comparison, the performances were evaluated in the validation set in terms of the area under the receiver operating characteristic curve (AUC). Pairwise t-test was applied to test the significant improvement between the adapted habitat-based method and the conventional methods. Results: A total of 1858 radiomics features were extracted. After feature selection, habitat imaging-based 18F-FDG PET/CT radiomics models were constructed. The AUC of the adapted clustering-based habitat radiomics was 0.7270±0.0147, which showed significantly improved discrimination performance compared to the conventional methods (P < .001). Furthermore, the combination of features extracted by our adaptive habitat imaging-based method and nonhabitat method showed the best performance than the other combinations. Conclusion: Habitat imaging-based 18F-FDG PET/CT radiomics shows potential as a biomarker for discriminating NSCLC and BIDs, which indicates that the microenvironmental variations in NSCLC and BID can be captured by PET/CT.