Enhancing risk stratification in cancer treatment outcomes: a deep learning-based comparative study of survival and binary models

Abstract For cancer treatment outcome prediction, binary classification models categorize patients based on fixed time points, which may oversimplify disease progression. In contrast, survival prediction models leverage time-to-event data, offering a more comprehensive analysis. This study compares the performance of deep learning-based binary and survival prediction models for treatment outcome prediction. We focus on head and neck cancer (HNC) and kidney cancer, two malignancies with distinct treatment paradigms, to assess the advantages of survival modeling over binary classification. Using the RadCure HNC dataset ( n = 2,550), we developed fused convolution neural network and transformer models to predict mortality and recurrence at 2 years post-treatment. For kidney cancer, we used a modified transformer-based multi-instance learning model on the Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset ( n = 226), integrating histopathology, multi-omics, and clinical features to predict outcomes at 3 years. Survival models outperformed binary models across single- and multi-modal data. On RadCure dataset, survival models achieved area under the receiver operating characteristic curves (AUROCs) of 0.852, 0.835, 0.811, and 0.828 for mortality and local, regional and distant recurrence predictions, surpassing binary models (0.834, 0.833, 0.809, and 0.805). Notably, survival models for overall survival trained on as little as 20% of the available data achieved performance comparable to binary models trained on the full dataset. With TCGA-KIRC dataset, survival model improved AUROCs to 0.812 (mortality) and 0.836 (recurrence), outperforming binary models (0.770 and 0.798). Overall, survival prediction models provide more robust and data-efficient risk stratification compared to binary models in deep learning setting. By capturing time-dependent risk trajectories, these models enable better data driven personalized cancer treatment strategies.