Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi, China.
Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China.
Front Immunol. 2024 May 22;15:1407632. doi: 10.3389/fimmu.2024.1407632. eCollection 2024.
Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions.
A cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions.
The findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS.
A ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.
信迪利单抗联合化疗已被证明对晚期胃和胃食管交界腺癌（GC/GEJC）患者有效，是一种联合免疫疗法。中国进行的一项多中心研究显示，中位无进展生存期（PFS）为 7.1 个月。然而，这种免疫疗法的反应持续时间预测尚未得到充分研究。此外，基线实验室特征预测 PFS 的潜力在很大程度上仍未得到探索。因此，我们开发了一个可解释的机器学习（ML）框架 iPFS-SC，旨在使用基线（治疗前）实验室特征预测 PFS，并提供预测的解释。
纳入了 146 例晚期 GC/GEJC 患者及其基线实验室特征的 iPFS-SC 框架。通过前向特征选择过程，确定了预测性的基线特征，并开发了四种 ML 算法，根据 7.1 个月的阈值对 PFS 持续时间进行分类。此外，我们采用可解释人工智能（XAI）方法阐明特征与模型预测之间的关系。
结果表明，LightGBM 在预测晚期 GC/GEJC 患者的 PFS 方面的准确率为 0.70。此外，在识别 PFS 持续时间短于 7.1 个月的患者方面，F1 得分为 0.77。通过特征选择过程，我们确定了 11 个预测性特征。此外，我们的框架还促进了发现实验室特征与 PFS 之间的关系。
我们开发了一种基于 ML 的框架，可准确预测信迪利单抗联合化疗的反应持续时间。建议的预测特征可通过常规实验室检查轻松获得。此外，XAI 技术可提供全局和个体水平的综合解释，说明 PFS 预测。该框架使患者能够更好地了解其治疗计划，同时临床医生可以根据模型提供的解释来定制治疗方法。
