Background/Objectives: Immunotherapy is a viable therapeutic approach for non-small cell lung cancer (NSCLC). Despite the significant survival benefit of immune checkpoint inhibitors PD-1/PD-L1, on average; the objective response rate is around 20% as monotherapy and around 50% in combination with chemotherapy. While PD-L1 IHC is used as a predictive biomarker, its accuracy is subpar. Methods: In this work, we develop a machine learning (ML) method to predict response to immunotherapy in NSCLC from multimodal clinicopathological biomarkers, tumor and peritumoral radiomic biomarkers from CT images. We further learn a graph structure to understand the associations between biomarkers and treatment response. The graph is then used to create sentences with clinical hypotheses that are finally used in a Large Language Model (LLM) that explains the treatment response predicated on the biomarkers that are comprehensible to clinicians. From a retrospective study, a training dataset of NSCLC with n = 248 tumors from 140 subjects was used for feature selection, ML model training, learning the graph structure, and fine-tuning LLM. Results: An AUC = 0.83 was achieved for prediction of treatment response on a separate test dataset of n = 84 tumors from 47 subjects. Conclusions: Our study therefore not only improves the prediction of immunotherapy response in patients with NSCLC from multimodal data but also assists the clinicians in making clinically interpretable predictions by providing language-based explanations.
背景/目的：免疫疗法是非小细胞肺癌（NSCLC）的一种可行治疗手段。尽管免疫检查点抑制剂PD-1/PD-L1在总体上能带来显著的生存获益，但其单药治疗的客观缓解率平均约为20%，联合化疗时约为50%。虽然PD-L1免疫组化被用作预测性生物标志物，但其准确性欠佳。方法：本研究开发了一种机器学习方法，通过整合多模态临床病理生物标志物、CT图像中的肿瘤及瘤周影像组学特征，预测NSCLC患者对免疫治疗的反应。我们进一步学习图结构以理解生物标志物与治疗反应之间的关联，并利用该结构生成包含临床假设的语句，最终输入大型语言模型，生成临床医生可理解的、基于生物标志物的治疗反应解释。通过一项回顾性研究，我们使用包含140例患者共248个肿瘤的训练数据集进行特征选择、机器学习模型训练、图结构学习及大型语言模型微调。结果：在包含47例患者共84个肿瘤的独立测试集上，模型预测治疗反应的曲线下面积达到0.83。结论：本研究不仅通过多模态数据提升了NSCLC患者免疫治疗反应的预测效能，还通过提供基于语言的解释，协助临床医生做出具有临床可解释性的预测。
肿瘤(癌症)患者之家