Shuang Zhao, Xingyu Xiong, Yue Cheng, Mingjing Yu
Department of Respiratory and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Clin Respir J. 2024 Dec;18(12):e70044. doi: 10.1111/crj.70044.
Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without mutations, but drug resistance often reduces effectiveness. Developing more effective methods to predict and monitor chemotherapy benefits early is crucial.
We carried out a retrospective cohort study of NSCLC patients without targeted mutations who received chemotherapy at West China Hospital from 2009 to 2013. We identified variables associated with chemotherapy outcomes and built four predictive models by machine learning. Shapley additive explanations (SHAP) interpreted the best model's predictions. The Kaplan-Meier method assessed key variables' impact on 5-year overall survival.
The study enrolled 461 NSCLC patients. Eight variables were selected for the model: differentiation, surgery history, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), total bilirubin (TBIL), total protein (TP), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH). The extreme gradient boosting (Xgboost) model exhibited superior discriminatory ability in predicting complete response (CR) probabilities to chemotherapy, with an AUC of 0.78. SHAP plots showed surgery history and high differentiation were related to CR benefits from chemotherapy. Absence of surgery, higher NLR, higher PLR, and higher LDH were all independent prognostic factors for poor survivals in NSCLC patients without mutations receiving chemotherapy.
By machine learning, we developed a predictive model to assess chemotherapy benefits in NSCLC patients without targeted mutations, utilizing eight readily available and non-invasive clinical indicators. Demonstrating satisfactory predictive performance and clinical practicability, this model may help clinicians identify patients' tendency to benefit from chemotherapy, potentially improving their prognosis.
非小细胞肺癌（NSCLC）是一项全球性的健康挑战。化疗仍然是晚期无突变NSCLC的标准治疗方法，但耐药性常常会降低疗效。开发更有效的方法来早期预测和监测化疗效果至关重要。
我们对2009年至2013年在华西医院接受化疗的无靶向突变NSCLC患者进行了一项回顾性队列研究。我们确定了与化疗结果相关的变量，并通过机器学习建立了四个预测模型。Shapley加法解释（SHAP）对最佳模型的预测进行了解释。Kaplan-Meier方法评估了关键变量对5年总生存的影响。
该研究纳入了461例NSCLC患者。模型选取了八个变量：分化程度、手术史、中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）、总胆红素（TBIL）、总蛋白（TP）、谷丙转氨酶（ALT）和乳酸脱氢酶（LDH）。极端梯度提升（Xgboost）模型在预测化疗完全缓解（CR）概率方面表现出卓越的区分能力，曲线下面积（AUC）为0.78。SHAP图显示手术史和高分化程度与化疗的CR获益相关。未进行手术、较高的NLR、较高的PLR和较高的LDH都是接受化疗的无突变NSCLC患者生存不良的独立预后因素。
通过机器学习，我们开发了一个预测模型，利用八个易于获得的非侵入性临床指标来评估无靶向突变NSCLC患者的化疗效果。该模型展示出令人满意的预测性能和临床实用性，可能有助于临床医生识别患者从化疗中获益的倾向，从而改善他们的预后。
