Abstract 7396: Development of deep neural networks that guide more individualized radiotherapy to the lung
Authors: Mohamed Abazeed
Journal: Cancer Research (2024)

Abstract Introduction: Radiotherapy is dependent on an expert-based model that impedes implementation of many potentially useful applications that could aid decision makers. This model is a barrier to a learning healthcare system due to noise introduced by decision-maker variation. Surmounting this barrier will require compelling clinical outcome data and highly useful and accurate information systems. We sought to develop new augmented-intelligence capabilities to better predict both high-dose lung irradiation treatment efficacy (i.e., local tumor control) and toxicity (i.e., radiation-induced pneumonitis). Methods: We curated one of the largest datasets of lung stereotactic body radiotherapy (SBRT) datasets globally (~1200 cases). We input pre-therapy lung computed tomography (CT) images using a bounding box approach that captures the gross tumor volume and adjacent normal lung into a multi-task deep neural network to generate an image fingerprint that predicts local failures after treatment. Separately, we input axial slices from CT scans of the lung into convolutional neural network (CNN) and trained using an outcome per slice method. We then aggregated intra-patient slices into a recurrent neural network (RNN) to estimate individual patient risk for pneumonitis. We validated our findings with the independent study cohort. Risk scores for local failure and pneumonitis were combined with clinical variables to assess the relative contribution of the deep learning-derived image risk scores. Results: A total of 1045 patients met our eligibility criteria. Radiation treatments in patients with high local failure risk scores failed at a significantly higher rate than in patients with low scores; 3-year cumulative incidence of local failure in the internal study cohort was 20·3% (16·0-24·9) in patients with high scores and 5·7% (95% CI 3·5-8·8) in patients with low scores (hazard ratio [HR]=3·64 [95% CI 2·19-6·05], p&amp;lt;0·0001). Models that included the local failure risk score and clinical variables predicted treatment failures with a concordance index (C-index) of 0·72 (95% CI 0·67-0·77), a significant improvement compared with classical radiomics (p&amp;lt;0·0001) or clinical variables (p&amp;lt;0·0001) alone. Radiation pneumonitis risk scores were highly predictive of toxicity, with an accuracy and CI of 0.78 (95% CI 0.72-0.85). A model that includes dosimetry variates (V20), improved the CI to 0.85 (95% CI 0.80-0.90). Conclusions: The image-based deep learning frameworks developed herein represent the first opportunity to use medical images to individualize radiotherapy dose and toxicity predictions. Our results signify a new roadmap for deep learning-guided predictions and treatment guidance in the image-replete and highly standardized discipline of radiation oncology. A phase 2 study assessing machine-derived dose predictions is currently enrolling patients. Citation Format: Mohamed Abazeed. Development of deep neural networks that guide more individualized radiotherapy to the lung [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7396.