AI驱动的双任务预测模型用于NSCLC免疫治疗疗效和毒性的共分层
AI-driven dual-task prediction model for co-stratifying efficacy and toxicity in NSCLC immunotherapy.
作者
作者单位
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, China.
- The School of Medical Imaging, Nanjing Medical University, Nanjing, China.
- Department of Oncology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
- Department of Thoracic Surgery, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China.
- Hospital Development Management Office, Nanjing Medical University, Nanjing, China.
- Department of Oncology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, China.
- Department of Intensive Care Unit, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, China.
摘要
中文
虽然PD-1抑制剂对非小细胞肺癌（NSCLC）有效，但可诱导免疫相关不良事件（irAEs），发生率高达15.2%，且可能致命。目前，能够同时预测irAEs和免疫检查点抑制剂（ICI）应答者的有效预测生物标志物仍然难以获得。这一局限性阻碍了这些药物的安全临床应用。本研究纳入了333名接受PD-1抑制剂单药或联合治疗的晚期NSCLC患者。使用影像组学和深度学习方法提取CT影像特征。构建了三个单模态和两个多模态模型，并行预测irAEs（≥3级）和ICI应答者。SHAP算法用于识别对irAEs和ICI应答者预测均有贡献的临床特征。整合临床特征与深度学习影像组学特征（DenseNet）的CDML-DenseNet模型在预测irAEs方面表现出优越性能（AUC=0.85），优于单模态影像组学模型。在ICI应答者预测方面，CDML-DenseNet模型的AUC达到0.866。预后营养指数（PNI）被确定为irAEs和ICI应答者预测模型中的关键特征。与无irAEs的ICI应答者相比，对ICI无应答但出现irAEs的患者PNI显著更低（46.8±8.779，P<0.05）。我们的多模态CDML-DenseNet模型能有效预测接受PD-1抑制剂的NSCLC患者的irAEs和ICI应答者。该方法为平衡免疫治疗疗效和毒性提供了新框架。此外，易于获得且成本低廉的PNI为临床医生提供了实用工具，以识别可能经历irAEs的无应答者并优化治疗决策。
English
While effective against non-small cell lung cancer (NSCLC), PD-1 inhibitors can induce immune-related adverse events (irAEs), occurring in up to 15.2% of patients and potentially fatal. Currently, effective predictive biomarkers capable of simultaneously forecasting both irAEs and immune checkpoint inhibitor (ICI) responders remain elusive. This limitation hinders the safe clinical application of these agents. This study enrolled 333 advanced NSCLC patients treated with PD-1 inhibitor monotherapy or combination therapy. CT imaging features were extracted using radiomics and deep-learning approaches. Three unimodal and two multimodal models were constructed to predict irAEs (Grade ≥3) and ICI responders in parallel. The SHAP algorithm was used to identify clinical features contributing to the prediction of both irAEs and ICI responders. The CDML-DenseNet model, integrating clinical features with deep-learning-derived radiomics features (DenseNet), demonstrated superior performance in predicting irAEs (AUC = 0.85), outperforming single-modal radiomics models. For ICI responder prediction, the CDML-DenseNet model achieved an AUC of 0.866. The Prognostic Nutritional Index (PNI) was identified as a key feature in both irAEs and ICI responder prediction models. Patients who were non-responders to ICIs but experienced irAEs had significantly lower PNI (46.8 ± 8.779, P < 0.05) compared with ICI responders without irAEs. Our multimodal CDML-DenseNet model effectively predicts both irAEs and ICI responders in NSCLC patients receiving PD-1 inhibitors. This approach provides a novel framework for balancing immunotherapy efficacy and toxicity. Furthermore, the readily available and cost-effective PNI offers clinicians a practical tool to identify potential non-responders experiencing irAEs and to refine treatment decisions.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 7.7
- 新锐分区
- 3区