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}

翻译：肾细胞癌是一种全球性的重大健康挑战，患者生存率较低。本研究旨在开发一种综合深度学习模型，通过整合CT影像和临床数据来预测肾细胞癌患者的生存概率，并解决既往研究的局限性，以促进对需要紧急治疗患者的识别。所提出的框架包含三个模块：3D图像特征提取器、临床变量选择和生存预测。特征提取器基于3D CNN架构，可从CT图像预测与死亡率相关的肾细胞癌ISUP分级。通过斯皮尔曼评分和随机森林重要性评分作为标准，系统选择临床变量。基于深度学习的网络采用离散LogisticHazard损失进行训练，完成生存预测。我们进行了九组独立实验，采用不同阈值下的斯皮尔曼评分和重要性评分确定不同数量的临床变量。研究结果表明，所提出的策略在基于CT扫描和临床因素的肾癌预后方面超越了现有文献。表现最佳的实验在测试队列上实现了0.84的一致性指数和0.8的曲线下面积值，展现出强大的预测能力。本研究开发的多模态深度学习方法在利用CT影像和临床数据估计肾细胞癌患者生存概率方面显示出良好效果，这可能对识别需要紧急治疗的患者具有潜在意义，从而改善患者预后。本项目创建的代码已公开在：\href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub}