Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma

作者：Huan Lin, Junjie Hua, Yumeng Wang, Mingwei Chen, Yanting Liang, Li‐Xu Yan, Wei Zhao, Shiwei Luo, Deqing Hong, Xin Chen, Xipeng Pan, Jun Liu, Zaiyi Liu · 发表于：Journal for ImmunoTherapy of Cancer · 年份：2025 · DOI：10.1136/jitc-2024-010723 · 被引用次数：14 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Cancer Immunotherapy and Biomarkers

BACKGROUND: Current prognostic and predictive biomarkers for lung adenocarcinoma (LUAD) predominantly rely on unimodal approaches, limiting their characterization ability. There is an urgent need for a comprehensive and accurate biomarker to guide individualized adjuvant therapy decisions. METHODS: In this retrospective study, data from patients with resectable LUAD (stage I-III) were collected from two hospitals and a publicly available dataset, forming a training dataset (n=223), a validation dataset (n=95), a testing dataset (n=449), and the non-small cell lung cancer (NSCLC) Radiogenomics dataset (n=59). Tumor and peritumor scores were constructed from preoperative CT radiomics features (shape/intensity/texture). An immune score was derived from the density of tumor-infiltrating lymphocytes (TILs) within the cancer epithelium and stroma on hematoxylin and eosin-stained whole-slide images. A clinical score was constructed based on clinicopathological risk factors. A Cox regression model was employed to integrate these scores, thereby constructing a multimodal nomogram to predict disease-free survival (DFS). The adjuvant chemotherapy benefit rate was subsequently calculated based on this nomogram. RESULTS: The multimodal nomogram outperformed each of the unimodal scores in predicting DFS, with a C-index of 0.769 (vs 0.634-0.731) in the training dataset, 0.730 (vs 0.548-0.713) in the validation dataset, and 0.751 (vs 0.660-0.692) in the testing dataset. It was independently ...