Mahootiha Maryamalsadat, Qadir Hemin Ali, Bergsland Jacob, Balasingham Ilangko
The Intervention Centre, Oslo University Hospital, Oslo, 0372, Norway; Faculty of Medicine, University of Oslo, Oslo, 0372, Norway.
The Intervention Centre, Oslo University Hospital, Oslo, 0372, Norway.
Comput Methods Programs Biomed. 2024 Feb;244:107978. doi: 10.1016/j.cmpb.2023.107978. Epub 2023 Dec 14.
Renal cell carcinoma represents a significant global health challenge with a low survival rate. The aim of this research was 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. Based on the 3D CNN architecture, the feature extractor module predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. Clinical variables are systematically selected 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: GitHub.
肾细胞癌是一项重大的全球健康挑战，生存率较低。本研究的目的是设计一种综合深度学习模型，通过整合CT成像和临床数据，解决先前研究中发现的局限性，从而能够预测肾细胞癌患者的生存概率。目的是便于识别需要紧急治疗的患者。
所提出的框架包括三个模块：3D图像特征提取器、临床变量选择和生存预测。基于3D CNN架构，特征提取器模块从CT图像中预测与死亡率相关的肾细胞癌肿瘤的ISUP分级。使用Spearman评分和随机森林重要性评分作为标准系统地选择临床变量。一个基于深度学习的网络，使用基于离散LogisticHazard的损失进行训练，进行生存预测。进行了九个不同的实验，根据Spearman评分和重要性评分的不同阈值确定不同数量的临床变量。
我们的研究结果表明，所提出的策略优于目前基于CT扫描和临床因素的肾癌预后文献。表现最佳的实验在测试队列中产生了0.84的一致性指数和0.8的曲线下面积值，这表明具有很强的预测能力。
本研究中开发的多模态深度学习方法在使用CT成像和临床数据估计肾细胞癌患者的生存概率方面显示出有希望的结果。这可能对识别需要紧急治疗的患者具有潜在意义，有可能改善患者的预后。为该项目创建的代码可在以下网址供公众使用：GitHub。
