Extended application of a CT-based artificial intelligence prognostication model in patients with primary lung cancer undergoing stereotactic ablative radiotherapy. (December 2021)

• Record Type: Journal Article
• Authors: Kim, Hyungjin; Lee, Joo Ho; Kim, Hak Jae; Park, Chang Min; Wu, Hong-Gyun; Goo, Jin Mo
• Is Part Of: Radiotherapy and oncology. Volume 165(2021)
• Page Start: 166
• Page End: 173
• Publication Date: 2021-12
• Subjects: AUC area under the time-dependent receiver operating characteristic curve -- CI confidence interval -- DFS disease-free survival -- DLPM deep learning prognostication model -- HR hazard ratio -- IQR interquartile range -- OS overall survival -- SABR stereotactic ablative radiotherapy; Deep learning -- Validation study -- Lung neoplasms -- Prognosis -- Multidetector computed tomography -- Stereotactic ablative radiotherapy; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2021.10.022
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: The target population of a deep learning prognostication model could be extended. The model predicted survival in patients receiving stereotactic radiotherapy for lung cancer. The deep learning model output was an independent prognostic factor for survival. Heat map visualized the association of intra- and peri-tumoral features with survival. Abstract: Background and purpose: To validate a computed tomography (CT)-based deep learning prognostication model, originally developed for a surgical cohort, in patients with primary lung cancer undergoing stereotactic ablative radiotherapy (SABR). Materials and methods: This retrospective study identified patients with clinical stage T1-2N0M0 lung cancer treated with SABR between 2013 and 2018. The outcomes were local recurrence-free survival (LRFS), disease-free survival (DFS), and overall survival (OS). The discrimination performance of the model, which extracted a quantitative score of cumulative risk for an adverse event up to 900 days, was evaluated using time-dependent receiver operating characteristic curve analysis. Multivariable Cox regression was performed to investigate the independent prognostic value of the model output adjusting for clinical factors including age, sex, smoking status, and clinical T category. Results: In total, 135 patients (median age, 78 years; 101 men; 78 [57.8%] adenocarcinomas and 57 [42.2%] squamous cell carcinomas) were evaluated. Most patients (117/135) were treated with 48–60 Gy in four fractions. Median biologically effective dose was 150.0 Gy (interquartile range, 126.9, 150.0 Gy). For LRFS, the area under the curve (AUC) was 0.72 (95% confidence interval [CI]: 0.58, 0.87). The AUCs were 0.70 (95% CI: 0.60, 0.81) for DFS and 0.66 (95% CI: 0.54, 0.77) for OS. Model output was associated with LRFS (adjusted hazard ratio [HR], 1.043; 95% CI: 1.003, 1.085; P = 0.04), DFS (adjusted HR, 1.03; 95% CI: 1.01, 1.05; P = 0.008), and OS (adjusted HR, 1.025; 95% CI: 1.002, 1.047; P = 0.03). Conclusion: This study showed external validity and transportability of the CT-based deep learning prognostication model for radiotherapy candidates.