An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction

• Published in: The Lancet. Digital health 2019-07, Vol.1 (3), p.e136-e147
• Main Authors: Lou, Bin, Doken, Semihcan, Zhuang, Tingliang, Wingerter, Danielle, Gidwani, Mishka, Mistry, Nilesh, Ladic, Lance, Kamen, Ali, Abazeed, Mohamed E
• Format: Article
• Language: English
• ISSN: 2589-7500; 2589-7500
• DOI: 10.1016/S2589-7500(19)30058-5

Background: Radiotherapy continues to be delivered without consideration of individual tumour characteristics. To advance towards more precise treatments in radiotherapy, we queried the lung CT-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualisation of radiotherapy dose. Methods: An institutional review board-approved study (IRB 14-562) was used to identify patients treated with lung stereotactic body radiotherapy. Patients with primary (stage IA–IV) or recurrent lung cancer and patients with other cancer types with solitary metastases or oligometastases to the lung were included. Patients without digitally accessible CT image or radiotherapy structure data were excluded. The internal study cohort received treatment at the main campus of the Cleveland Clinic (Cleveland, OH, USA). The independent validation cohort received treatment at seven affiliate regional or national sites. We input pre-therapy lung CT images into Deep Profiler, a multi-task deep neural network that has radiomics incorporated into the training process, to generate an image fingerprint that predicts time-to-event treatment outcomes and approximates classical radiomic features. We validated our findings with the independent study cohort. Deep Profiler was combined with clinical variables to derive iGray, an individualised dose that estimates treatment failure probability to be below 5%. Findings: A total of 1275 patients were assessed for eligibility and 944 met our eligibility criteria; 849 were in the internal study cohort and 95 were in the independent validation cohort. Radiation treatments in patients with high Deep Profiler 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 Deep Profiler scores and 5·7% (95% CI 3·5–8·8) in patients with low Deep Profiler scores (hazard ratio [HR]=3·64 [95% CI 2·19–6·05], p