Radiomics-based Prediction of Local Recurrence after Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

• Published in: Annals of the American Thoracic Society Vol. 22; no. 8; p. 1236
• Main Authors: Ogbonna, Chioma P, Breen, William G, Le Noach, Pierre, Rajagopalan, Srinivasan, Hostetter, Logan J, Maldonado, Fabien, Bartholmai, Brian J, Merrell, Kenneth W, Peikert, Tobias
• Format: Journal Article
• Language: English
• Published: United States 01.08.2025
• Subjects: Aged; Aged, 80 and over; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Non-Small-Cell Lung - radiotherapy; Case-Control Studies; Female; Humans; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Lung Neoplasms - radiotherapy; Male; Middle Aged; Neoplasm Recurrence, Local - diagnostic imaging; Neoplasm Staging; Radiomics; Radiosurgery - methods; Retrospective Studies; Tomography, X-Ray Computed - methods; radiomics; local recurrence; stereotactic body radiation therapy (SBRT); non–small cell lung cancer (NSCLC); computer-aided nodule analysis and risk yield (CANARY)
• ISSN: 2325-6621
• DOI: 10.1513/AnnalsATS.202410-1047OC

Stereotactic body radiation therapy (SBRT) represents an effective therapeutic strategy for early-stage non-small cell lung cancer (NSCLC); however, local and systemic recurrences represent ongoing challenges. Computed tomography (CT) radiomics-based risk models can potentially be used to predict the risk of local recurrence on pretreatment CT scans. Development of a radiomics model to predict local recurrence after SBRT in patients with NSCLC. This single-institution study includes a retrospective case-control training set (20 patients with local recurrence and 40 control subjects) and an independent validation set (198 consecutive cases) of patients with early-stage NSCLC treated with SBRT. Tumors were semiautomatically segmented, and 102 quantitative radiomic features, including texture, landscape, spatial, nodule shape, and nodule surface features, were extracted. These features were included in three separate multivariable models to predict the risk of recurrence on the basis of pre-SBRT, post-SBRT, and the difference between the pre-SBRT and post-SBRT scans (Delta model). The pre-SBRT model was subsequently validated in an independent validation set. Thirteen independent variables were selected for the models using the Boruta algorithm. The sensitivity, specificity, and area under the curve of the pre-SBRT, post-SBRT, and Delta models were 85%, 90%, and 0.91; 85%, 92.5%, and 0.92; and 85%, 92.5%, and 0.94, respectively. The pre-SBRT model was validated in the independent validation set (area under the curve, 0.89; confidence interval, 0.83-0.92), because this model was believed to be the most useful to assist in individualized treatment planning. Radiomic analysis facilitated the development of three high-performing models predicting local recurrence using either pre-SBRT CT, post-SBRT CT, or the change between these two. We successfully validated the most clinically relevant model, the pre-SBRT model. Although this model needs further validation, it may facilitate individualized surveillance, treatment planning, and selection of adjuvant therapy.