文章：

肺腺癌术前预测脏层胸膜侵犯的肿瘤及瘤周影像组学研究

Tumoral and Peritumoral Radiomics for Preoperative Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma

原文发布日期：16 December 2025

DOI: 10.3390/cancers17244001

类型: Article

开放获取: 是

英文摘要：

Background:The presence of visceral pleural invasion (VPI) is associated with increased risk of recurrence and reduced overall survival following surgical resection. We aimed to develop machine learning (ML)-based classification models that integrate clinical variables and both tumoral and peritumoral radiomic features to predict VPI in patients with lung adenocarcinoma before surgery. Methods: We retrospectively enrolled 118 patients, including 80 (68%) without VPI and 38 (32%) with histologically confirmed VPI. All patients underwent preoperative contrast-enhanced CT scans. Tumor volumes were manually segmented, and isotropic expansions of 3, 5, and 10 mm were automatically generated to define peritumoral regions. The dataset was randomly split into training (70%) and validation (30%) cohorts. Radiomic features and clinical data were used to train multiple ML algorithms. Results: Pleural Tag Sign and the Worst Histotype were identified as the strongest clinical predictors of VPI. The combined model, integrating radiomics from the lesion and clinical variables, achieved the highest training accuracy of 0.88 (95% CI: 0.80–0.92) and validation accuracy of 0.83 (95% CI: 0.68–0.92). Conclusions: VPI is associated with detectable alterations in both tumoral and peritumoral microenvironment on contrast-enhanced CT. Incorporating radiomic features with clinical data enabled improved model performance compared to clinical-only models, yielding very good accuracies. This approach may support surgical planning and patient risk stratification. Further prospective studies are needed to validate these findings and assess their clinical impact.

摘要翻译： 

背景：脏层胸膜侵犯（VPI）的存在与手术切除后复发风险增加和总生存期降低相关。本研究旨在开发基于机器学习（ML）的分类模型，整合临床变量以及肿瘤内和瘤周影像组学特征，以在术前预测肺腺癌患者的VPI状态。方法：我们回顾性纳入118例患者，其中80例（68%）无VPI，38例（32%）经组织学证实存在VPI。所有患者均接受了术前增强CT扫描。手动分割肿瘤体积，并自动生成3、5和10毫米的各向同性扩展区域以定义瘤周区域。数据集被随机分为训练组（70%）和验证组（30%）。使用影像组学特征和临床数据训练多种ML算法。结果：胸膜牵拉征和最差组织学亚型被确定为VPI最强的临床预测因子。整合病灶影像组学特征和临床变量的组合模型在训练集中达到了最高的准确率0.88（95% CI：0.80–0.92），在验证集中准确率为0.83（95% CI：0.68–0.92）。结论：VPI与增强CT上肿瘤内和瘤周微环境的可检测改变相关。与仅使用临床数据的模型相比，结合影像组学特征与临床数据提高了模型性能，获得了非常好的预测准确率。该方法可能有助于手术规划和患者风险分层。需要进一步的前瞻性研究来验证这些发现并评估其临床影响。

原文链接：

Tumoral and Peritumoral Radiomics for Preoperative Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma