Background/Objectives: Ovarian cancer is a complex disease with poor outcomes that affects women worldwide. The lack of successful therapeutic options for this malignancy has led to the need to identify novel biomarkers for patient stratification. Here, we aim to develop the outcome predictors based on the gene expression data as they may serve to identify categories of patients who are more likely to respond to certain therapies. Methods: We used The Cancer Genome Atlas (TCGA) ovarian cancer transcriptomic data from 372 patients and approximately 16,600 genes to train and evaluate the deep learning survival models. In addition, we collected an in-house validation dataset of 12 patients to assess the performance of the trained survival models for their direct use in clinical practice. Despite deceptive generalization capabilities, we demonstrated how our model can be interpreted to uncover biological processes associated with survival. We calculated the contributions of the input genes to the output of the best trained model and derived the corresponding molecular pathways. Results: These pathways allowed us to stratify the TCGA patients into high-risk and low-risk groups (p-value 0.025). We validated the stratification ability of the identified pathways on the in-house dataset consisting of 12 patients (p-value 0.229) and on the external clinical and molecular dataset consisting of 274 patients (p-value 0.006). Conclusions: The deep learning-based models for survival prediction with RNA-seq data could be used to detect and interpret the gene-sets associated with survival in ovarian cancer patients and open a new avenue for future research.

Survival prediction with whole slide images (WSIs) can provide guidance for a better patient care and treatment selection but it is a challenging computer vision task with its particularities. Despite the great results showed by the recent survival analysis models with WSIs, the collection of the large annotated WSI datasets for survival analysis could be hindered by disease rareness or clinical trials constraints and be infeasible in the real-life medical practice. To overcome these limitations we propose to assess the performance of the digital pathology foundation models for prediction of survival outcomes on the small size ovarian cancer datasets. Our experimental results demonstrate that these models show promising results, their improved performance open the possibility to investigate the mechanisms of response to a particular therapy and in general could accelerate the adoption of machine learning models in medical practice.