Radiomics of Vascular Structures in Pulmonary Ground-Glass Nodules: A Predictor of Invasiveness : Radiomics of Vascular Structures in GGNs for Tumor Invasiveness Prediction.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Wuling Wang, Xuan Qi, Yongsheng He, Hongkai Yang, Dong Qi, Zhen Tang, Qiong Chen
{"title":"Radiomics of Vascular Structures in Pulmonary Ground-Glass Nodules: A Predictor of Invasiveness : Radiomics of Vascular Structures in GGNs for Tumor Invasiveness Prediction.","authors":"Wuling Wang, Xuan Qi, Yongsheng He, Hongkai Yang, Dong Qi, Zhen Tang, Qiong Chen","doi":"10.2174/0115734056385352250410053810","DOIUrl":null,"url":null,"abstract":"<p><p>Objective The global incidence of lung cancer highlights the need for improved assessment of nodule characteristics to enhance early detection of lung adenocarcinoma presenting as ground-glass nodules (GGNs). This study investigated the applicability of radiomics features of vascular structures within GGNs for predicting invasiveness of GGNs. Methods In total, 165 pathologically confirmed pulmonary GGNs were retrospectively analyzed. The nodules were classified into preinvasive and invasive groups and randomly categorized into training and validation sets in a 7:3 ratio. Four models were constructed and evaluated: radiomics-GGN, radiomics-vascular, clinical-radiomics-GGN, and clinical-radiomics-vascular. The predictive performance of these models was assessed using receiver operating characteristic curves, decision curve analysis, calibration curves, and DeLong's test. Results Significant differences were observed between the preinvasive and invasive groups in terms of age, nodule length, average diameter, morphology, and lobulation sign (P = 0.006, 0.038, 0.046, 0.049, and 0.002, respectively). In the radiomics-GGN model, the support vector machine (SVM) approach outperformed logistic regression (LR), achieving an area under the curve (AUC) of 0.958 in the training set and 0.763 in the validation set. Similarly, in the radiomics-vascular model, the SVM approach outperformed LR. Furthermore, the clinical-radiomics-vascular model demonstrated superior predictive performance compared with the clinical-radiomics-GGN model, with an AUC of 0.918 in the training set and 0.864 in the validation set. DeLong's test indicated significant differences in predicting the invasiveness of pulmonary nodules between the clinical-radiomics-vascular model and the clinical-radiomics-GGN model, both in the training and validation sets (P < 0.01). Conclusion The radiomics models based on internal vascular structures of GGNs outperformed those based on GGNs alone, suggesting that incorporating vascular radiomics analysis can improve the noninvasive assessment of GGN invasiveness, thereby aiding in clinical decision-making and guiding biopsy selection and treatment planning.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Medical Imaging Reviews","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0115734056385352250410053810","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Objective The global incidence of lung cancer highlights the need for improved assessment of nodule characteristics to enhance early detection of lung adenocarcinoma presenting as ground-glass nodules (GGNs). This study investigated the applicability of radiomics features of vascular structures within GGNs for predicting invasiveness of GGNs. Methods In total, 165 pathologically confirmed pulmonary GGNs were retrospectively analyzed. The nodules were classified into preinvasive and invasive groups and randomly categorized into training and validation sets in a 7:3 ratio. Four models were constructed and evaluated: radiomics-GGN, radiomics-vascular, clinical-radiomics-GGN, and clinical-radiomics-vascular. The predictive performance of these models was assessed using receiver operating characteristic curves, decision curve analysis, calibration curves, and DeLong's test. Results Significant differences were observed between the preinvasive and invasive groups in terms of age, nodule length, average diameter, morphology, and lobulation sign (P = 0.006, 0.038, 0.046, 0.049, and 0.002, respectively). In the radiomics-GGN model, the support vector machine (SVM) approach outperformed logistic regression (LR), achieving an area under the curve (AUC) of 0.958 in the training set and 0.763 in the validation set. Similarly, in the radiomics-vascular model, the SVM approach outperformed LR. Furthermore, the clinical-radiomics-vascular model demonstrated superior predictive performance compared with the clinical-radiomics-GGN model, with an AUC of 0.918 in the training set and 0.864 in the validation set. DeLong's test indicated significant differences in predicting the invasiveness of pulmonary nodules between the clinical-radiomics-vascular model and the clinical-radiomics-GGN model, both in the training and validation sets (P < 0.01). Conclusion The radiomics models based on internal vascular structures of GGNs outperformed those based on GGNs alone, suggesting that incorporating vascular radiomics analysis can improve the noninvasive assessment of GGN invasiveness, thereby aiding in clinical decision-making and guiding biopsy selection and treatment planning.

肺磨玻璃结节血管结构的放射组学:侵袭性的预测因子:ggn血管结构的放射组学用于肿瘤侵袭性预测。
目的肺癌的全球发病率凸显了改进结节特征评估的必要性,以提高对以磨玻璃结节(ggn)为表现的肺腺癌的早期发现。本研究探讨了ggn血管结构放射组学特征在预测ggn侵袭性方面的适用性。方法回顾性分析165例经病理证实的肺部ggn。将结节分为侵袭前组和侵袭组,并按7:3的比例随机分为训练组和验证组。构建并评估了四个模型:放射组学- ggn、放射组学-血管、临床-放射组学- ggn和临床-放射组学-血管。采用受试者工作特征曲线、决策曲线分析、校准曲线和DeLong检验对这些模型的预测性能进行评估。结果侵袭前组与侵袭前组在年龄、结节长度、平均直径、形态、分叶征象等方面差异均有统计学意义(P值分别为0.006、0.038、0.046、0.049、0.002)。在radiomics-GGN模型中,支持向量机(SVM)方法优于逻辑回归(LR),在训练集和验证集的曲线下面积(AUC)分别为0.958和0.763。同样,在放射组学-血管模型中,SVM方法优于LR。此外,临床-放射组学-血管模型的预测性能优于临床-放射组学- ggn模型,训练集的AUC为0.918,验证集的AUC为0.864。DeLong的试验表明,临床-放射组学-血管模型与临床-放射组学- ggn模型在预测肺结节侵袭性方面,无论是在训练集还是验证集上,都存在显著差异(P < 0.01)。结论基于GGN内部血管结构的放射组学模型优于单独基于GGN的放射组学模型,表明结合血管放射组学分析可以提高GGN侵袭性的无创评估,从而帮助临床决策,指导活检选择和治疗计划。
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
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信