  Renal cell carcinoma represents a significant global health challenge with a
low survival rate. This research aimed to devise a comprehensive deep-learning
model capable of predicting survival probabilities in patients with renal cell
carcinoma by integrating CT imaging and clinical data and addressing the
limitations observed in prior studies. The aim is to facilitate the
identification of patients requiring urgent treatment. The proposed framework
comprises three modules: a 3D image feature extractor, clinical variable
selection, and survival prediction. The feature extractor module, based on the
3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors
linked to mortality rates from CT images. A selection of clinical variables is
systematically chosen using the Spearman score and random forest importance
score as criteria. A deep learning-based network, trained with discrete
LogisticHazard-based loss, performs the survival prediction. Nine distinct
experiments are performed, with varying numbers of clinical variables
determined by different thresholds of the Spearman and importance scores. Our
findings demonstrate that the proposed strategy surpasses the current
literature on renal cancer prognosis based on CT scans and clinical factors.
The best-performing experiment yielded a concordance index of 0.84 and an area
under the curve value of 0.8 on the test cohort, which suggests strong
predictive power. The multimodal deep-learning approach developed in this study
shows promising results in estimating survival probabilities for renal cell
carcinoma patients using CT imaging and clinical data. This may have potential
implications in identifying patients who require urgent treatment, potentially
improving patient outcomes. The code created for this project is available for
the public on:
\href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub}
