PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■Sintilimab联合化疗已被证明可作为晚期胃和胃食管交界腺癌（GC/GEJC）患者的联合免疫疗法有效。在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,对这种免疫治疗反应持续时间的预测尚未得到彻底研究.此外,基线实验室特征在预测PFS方面的潜力仍未被探索.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，并提供对预测的解释。
■146名晚期GC/GEJC患者的队列，连同他们的基线实验室特征，包含在iPFS-SC框架中。通过前向特征选择过程，确定了预测基线特征，并开发了四种ML算法来根据7.1个月的阈值对PFS持续时间进行分类。此外，我们采用了可解释的人工智能(XAI)方法来阐明特征与模型预测之间的关系.
■研究结果表明，LightGBM在预测晚期GC/GEJC患者的PFS方面达到了0.70的准确性。此外，对于确定PFS持续时间短于7.1个月的患者,F1评分为0.77分.通过特征选择过程，我们确定了11个预测特征。此外,我们的框架有助于发现实验室功能和PFS之间的关系.
■开发了基于ML的框架，以高精度预测Sintilimab加化疗反应持续时间。建议的预测功能可以通过常规实验室测试轻松访问。此外，XAI技术提供全面的解释，在全球和个人层面，关于PFS预测。该框架使患者能够更好地了解他们的治疗计划，而临床医生可以根据模型提供的解释定制治疗方法。
■146名晚期GC/GEJC患者的队列，连同他们的基线实验室特征，包含在iPFS-SC框架中。通过前向特征选择过程，确定了预测基线特征，并开发了四种ML算法来根据7.1个月的阈值对PFS持续时间进行分类。此外，我们采用了可解释的人工智能(XAI)方法来阐明特征与模型预测之间的关系.
■研究结果表明，LightGBM在预测晚期GC/GEJC患者的PFS方面达到了0.70的准确性。此外，对于确定PFS持续时间短于7.1个月的患者,F1评分为0.77分.通过特征选择过程，我们确定了11个预测特征。此外,我们的框架有助于发现实验室功能和PFS之间的关系.
■开发了基于ML的框架，以高精度预测Sintilimab加化疗反应持续时间。建议的预测功能可以通过常规实验室测试轻松访问。此外，XAI技术提供全面的解释，在全球和个人层面，关于PFS预测。该框架使患者能够更好地了解他们的治疗计划，而临床医生可以根据模型提供的解释定制治疗方法。
