Donghia Rossella, Guerra Vito, Pesole Pasqua Letizia, Liso Marina
National Institute of Gastroenterology, IRCCS "S. de Bellis," Research Hospital, Bari, Italy.
Front Nutr. 2023 Jan 23;10:1066749. doi: 10.3389/fnut.2023.1066749. eCollection 2023.
The aim of this study was to evaluate the contribution of macro- and micronutrients intake to mortality in patients with gastrointestinal cancer, comparing the classical statistical approaches with a new generation algorithm. In 1992, the ONCONUT project was started with the aim of evaluating the relationship between diet and cancer development in a Southern Italian elderly population. Patients who died of specific death causes (ICD-10 from 150.0 to 159.9) were included in the study ( = 3,505) and survival analysis was applied. This cohort was used to test the performance of different techniques, namely Cox proportional-hazards model, random survival forest (RSF), Survival Support Vector Machine (SSVM), and C-index, applied to quantify the performance. Lastly, the new prediction mode, denominated Shapley Additive Explanation (SHAP), was adopted. RSF had the best performance (0.7653711 and 0.7725246, for macro- and micronutrients, respectively), while SSVM had the worst C-index (0.5667753 and 0.545222). SHAP was helpful to understand the role of single patient features on mortality. Using SHAP together with RSF and classical CPH was most helpful, and shows promise for future clinical applications.
本研究旨在评估常量营养素和微量营养素摄入对胃肠道癌患者死亡率的影响，将经典统计方法与新一代算法进行比较。1992年，ONCONUT项目启动，旨在评估意大利南部老年人群饮食与癌症发展之间的关系。死于特定死因（国际疾病分类第十版，编码从150.0至159.9）的患者被纳入研究（n = 3505），并应用生存分析。该队列用于测试不同技术的性能，即Cox比例风险模型、随机生存森林（RSF）、生存支持向量机（SSVM）和C指数，以量化性能。最后，采用了名为Shapley加性解释（SHAP）的新预测模式。RSF表现最佳（常量营养素和微量营养素的C指数分别为0.7653711和0.7725246），而SSVM的C指数最差（0.5667753和0.545222）。SHAP有助于理解单个患者特征对死亡率的作用。将SHAP与RSF和经典的CPH一起使用最有帮助，并显示出在未来临床应用中的前景。
