Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median 68.3 years [range 32.5–93.3], Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n 771, age median 68.0 years [range 32.5–93.3], We then employed a transfer learning approach to achieve the same for surgery patients (n 391, age median 69.1 years [range 37.2–88.0], We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] 0.70 [95% CI 0.63–0.78], The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p 0.001) and surgery (p 0.03) datasets.