Dead space wiki dana farber1/6/2024 To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test–retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman’s rank-order correlation = 0.88) variations. 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. 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 = 0.70, p < 0.001) and surgery (AUC = 0.71, p < 0.001) patients. We then employed a transfer learning approach to achieve the same for surgery patients ( n = 391, age median = 69.1 years, survival median = 3.1 years ). 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, survival median = 1.3 years ). We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years, survival median = 1.7 years ).
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