Nomogram based on radiomics and CT features for predicting visceral pleural invasion of invasive adenocarcinoma ≤ 2 cm: A multicenter study

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-06-12 DOI:10.1016/j.ejrad.2025.112227

Rikun Huang , Chunli Zhao , Jinhan Yang , Bingfeng Lu , Yi Dai , Miaomiao Lin , Xiang Zhao , Haipeng Huang , Xiaoyu Pan , Liling Lu , Lina Chen , Kai Li

{"title":"Nomogram based on radiomics and CT features for predicting visceral pleural invasion of invasive adenocarcinoma ≤ 2 cm: A multicenter study","authors":"Rikun Huang , Chunli Zhao , Jinhan Yang , Bingfeng Lu , Yi Dai , Miaomiao Lin , Xiang Zhao , Haipeng Huang , Xiaoyu Pan , Liling Lu , Lina Chen , Kai Li","doi":"10.1016/j.ejrad.2025.112227","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>To explore the value of a nomogram based on radiomics and computed tomography (CT) features for preoperative prediction of visceral pleural invasion (VPI) of subpleural, small (≤2 cm) invasive adenocarcinoma (IAC) of the lung.</div></div><div><h3>Methods</h3><div>For this retrospective study, 457 cases of invasive lung adenocarcinoma ≤ 2 cm were collected from three tertiary hospitals in Guangxi and used in a training group (n = 254), validation group (n = 112), and test group (n = 91). Risk factors for IAC VPI were screened by univariate and multivariate logistic regression analyses, and a CT model was constructed. Radiomics features of regions representing the gross tumor area (GTA), peritumor area (PTA), and gross peritumor area (GPTA) were extracted from CT images, and the optimal feature subsets based on radiomics score were selected to construct three radiomics models. A combination model was then constructed from the radiomics model with the optimal radiomics score and the CT model and visualized by nomogram. Model performance was analyzed by receiver operating characteristic curve analysis and DeLong test.</div></div><div><h3>Results</h3><div>Pleural indentation (<em>P <</em> 0.05), pleural thickening (<em>P <</em> 1e-04), and tumor diameter (<em>P <</em> 0.001) were identified as risk factors of the CT model for predicting VPI of IAC. Among 1226 radiomics features, 5, 13, and 12 optimal features were selected for the GTA, PTA, and GPTA models, respectively, and the area under the curve (AUC) values did not differ among these models. Based on AUC values, the CT model and GPTA model features were combined to construct the predictive nomogram. Compared with the individual models, the nomogram exhibited better accuracy, specificity, and AUC values (AUC values for training, verification, and test groups were 0.86, 0.84, and 0.86, respectively). Calibration curve and decision curve analyses showed that the nomogram outperformed traditional CT features and radiomics studies, and could offer greater clinical benefit.</div></div><div><h3>Conclusions</h3><div>The developed nomogram combining CT and radiomics features shows high diagnostic value for VPI prediction of IAC of the lung.</div></div>","PeriodicalId":12063,"journal":{"name":"European Journal of Radiology","volume":"190 ","pages":"Article 112227"},"PeriodicalIF":3.2000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Radiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0720048X25003134","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Objective

To explore the value of a nomogram based on radiomics and computed tomography (CT) features for preoperative prediction of visceral pleural invasion (VPI) of subpleural, small (≤2 cm) invasive adenocarcinoma (IAC) of the lung.

Methods

For this retrospective study, 457 cases of invasive lung adenocarcinoma ≤ 2 cm were collected from three tertiary hospitals in Guangxi and used in a training group (n = 254), validation group (n = 112), and test group (n = 91). Risk factors for IAC VPI were screened by univariate and multivariate logistic regression analyses, and a CT model was constructed. Radiomics features of regions representing the gross tumor area (GTA), peritumor area (PTA), and gross peritumor area (GPTA) were extracted from CT images, and the optimal feature subsets based on radiomics score were selected to construct three radiomics models. A combination model was then constructed from the radiomics model with the optimal radiomics score and the CT model and visualized by nomogram. Model performance was analyzed by receiver operating characteristic curve analysis and DeLong test.

Results

Pleural indentation (P < 0.05), pleural thickening (P < 1e-04), and tumor diameter (P < 0.001) were identified as risk factors of the CT model for predicting VPI of IAC. Among 1226 radiomics features, 5, 13, and 12 optimal features were selected for the GTA, PTA, and GPTA models, respectively, and the area under the curve (AUC) values did not differ among these models. Based on AUC values, the CT model and GPTA model features were combined to construct the predictive nomogram. Compared with the individual models, the nomogram exhibited better accuracy, specificity, and AUC values (AUC values for training, verification, and test groups were 0.86, 0.84, and 0.86, respectively). Calibration curve and decision curve analyses showed that the nomogram outperformed traditional CT features and radiomics studies, and could offer greater clinical benefit.

Conclusions

The developed nomogram combining CT and radiomics features shows high diagnostic value for VPI prediction of IAC of the lung.

查看原文本刊更多论文

基于放射组学和CT特征的Nomogram预测≤2 cm的浸润性腺癌内脏性胸膜浸润：一项多中心研究

目的探讨胸膜下小型（≤2 cm）浸润性肺腺癌（IAC）胸膜下肺脏性胸膜浸润（VPI）的术前预测价值。方法回顾性分析广西三所三级医院457例≤2 cm的浸润性肺腺癌患者，分为训练组（254例）、验证组（112例）和试验组（91例）。通过单因素和多因素logistic回归分析筛选IAC VPI的危险因素，并建立CT模型。从CT图像中提取总肿瘤面积（GTA）、肿瘤周围面积（PTA）和总肿瘤周围面积（GPTA）区域的放射组学特征，并根据放射组学评分选择最优特征子集，构建三个放射组学模型。然后将获得最佳放射组学评分的放射组学模型与CT模型构建组合模型，并用nomogram进行可视化。通过接收机工作特性曲线分析和德隆试验对模型性能进行了分析。结果胸膜压痕(P <；0.05)，胸膜增厚(P <；1e-04)，肿瘤直径(P <；0.001)被认为是预测IAC VPI的CT模型的危险因素。在1226个放射组学特征中，GTA、PTA和GPTA模型分别选择了5个、13个和12个最优特征，这些模型的曲线下面积（AUC）值没有差异。根据AUC值，结合CT模型和GPTA模型特征，构建预测模态图。与单个模型相比，模态图具有更好的准确性、特异性和AUC值（训练组、验证组和试验组的AUC值分别为0.86、0.84和0.86）。校正曲线和决策曲线分析表明，nomographic优于传统的CT特征和放射组学研究，可以提供更大的临床效益。结论建立的影像学结合CT和放射组学特征对VPI预测肺IAC有较高的诊断价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.