Lu Xiaoyan, Liu Fan, E Jiahui, Cai Xiaoting, Yang Jingyi, Wang Xueqi, Zhang Yuwei, Sun Bingsheng, Liu Ying
Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin, China.
Department of Lung Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; , Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin, China.
Cancer Imaging. 2025 Aug 21;25(1):105. doi: 10.1186/s40644-025-00928-3.
BACKGROUND: Accurate preoperative assessment of occult lymph node metastasis (OLNM) plays a crucial role in informing therapeutic decision-making for lung cancer patients. Computed tomography (CT) is the most widely used imaging modality for preoperative work-up. The aim of this study was to develop and validate a CT-based machine learning model integrating intra-and peri-tumoral features to predict OLNM in lung cancer patients. METHODS: Eligible patients with peripheral lung cancer confirmed by radical surgical excision with systematic lymphadenectomy were retrospectively recruited from January 2019 to December 2021. 1688 radiomics features were obtained from each manually segmented VOI which was composed of gross tumor volume (GTV) covering the boundary of entire tumor and three peritumoral volumes (PTV3, PTV6 and PTV9) that capture the region outside the tumor. A clinical-radiomics model incorporating radiomics signature, independent clinical factors and CT semantic features was established via multivariable logistic regression analysis and presented as a nomogram. Model performance was evaluated by discrimination, calibration, and clinical utility. RESULTS: Overall, 591 patients were recruited in the training cohort and 253 in the validation cohort. The radiomics signature of PTV9 showed superior diagnostic performance compared to PTV3 and PTV6 models. Integrating GPTV radiomics signature (incorporating Rad-score of GTV and PTV9) with clinical risk factor of serum CEA levels and CT imaging features of lobulation sign and tumor-pleura relationship demonstrated favorable accuracy in predicting OLNM in the training cohort (AUC, 0.819; 95% CI: 0.780-0.857) and validation cohort (AUC, 0.801; 95% CI: 0.741-0.860). The predictive performance of the clinical-radiomics model demonstrated statistically significant superiority over that of the clinical model in both cohorts (all p < 0.05). CONCLUSIONS: The clinical-radiomics model was able to serve as a noninvasive preoperative prediction tool for personalized risk assessment of OLNM in peripheral lung cancer patients.
背景：隐匿性淋巴结转移（OLNM）的准确术前评估对肺癌患者的治疗决策起着至关重要的作用。计算机断层扫描（CT）是术前检查中使用最广泛的成像方式。本研究的目的是开发并验证一种基于CT的机器学习模型，该模型整合肿瘤内和肿瘤周围特征以预测肺癌患者的OLNM。 方法：回顾性招募2019年1月至2021年12月期间经根治性手术切除并系统淋巴结清扫确诊为周围型肺癌的符合条件患者。从每个手动分割的感兴趣体积（VOI）中获取1688个放射组学特征，该VOI由覆盖整个肿瘤边界的大体肿瘤体积（GTV）和三个肿瘤周围体积（PTV3、PTV6和PTV9）组成，后者捕获肿瘤外区域。通过多变量逻辑回归分析建立了一个包含放射组学特征、独立临床因素和CT语义特征的临床放射组学模型，并以列线图形式呈现。通过区分度、校准度和临床实用性评估模型性能。 结果：总体而言，训练队列招募了591例患者，验证队列招募了253例患者。与PTV3和PTV6模型相比，PTV9的放射组学特征显示出更好的诊断性能。将GPTV放射组学特征（结合GTV和PTV9的Rad评分）与血清CEA水平的临床风险因素以及分叶征和肿瘤-胸膜关系的CT成像特征相结合，在训练队列（AUC，0.819；95%CI：0.780-0.857）和验证队列（AUC，0.801；95%CI：0.741-0.860）中预测OLNM具有良好的准确性。临床放射组学模型的预测性能在两个队列中均显示出比临床模型具有统计学意义的优越性（所有p<0.05）。 结论：临床放射组学模型能够作为一种非侵入性术前预测工具，用于周围型肺癌患者OLNM的个性化风险评估。
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