一项多中心队列研究:结合传统放射组学和深度学习特征的综合模型预测适合治疗性消融的肝细胞癌早期复发

IF 1 4区 医学 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yong-Hai Li, Gui-Xiang Qian, Yu Zhu, Xue-di Lei, Lei Tang, Xiang-Yi Bu, Ming-Tong Wei, Wei-Dong Jia
{"title":"一项多中心队列研究:结合传统放射组学和深度学习特征的综合模型预测适合治疗性消融的肝细胞癌早期复发","authors":"Yong-Hai Li, Gui-Xiang Qian, Yu Zhu, Xue-di Lei, Lei Tang, Xiang-Yi Bu, Ming-Tong Wei, Wei-Dong Jia","doi":"10.1097/RCT.0000000000001764","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving prognosis. This study aimed to develop and validate a model (DLRR) that incorporates deep learning radiomics and traditional radiomics features to predict ER following curative ablation for HCC.</p><p><strong>Methods: </strong>We retrospectively analysed the data of 288 eligible patients from 3 hospitals-1 primary cohort (center 1, n=222) and 2 external test cohorts (center 2, n=32 and center 3, n=34)-from April 2008 to March 2022. 3D ResNet-18 and PyRadiomics were applied to extract features from contrast-enhanced computed tomography (CECT) images. The 3-step (ICC-LASSO-RFE) method was used for feature selection, and 6 machine learning methods were used to construct models. Performance was compared through the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. Calibration and clinical applicability were assessed through calibration curves and decision curve analysis (DCA), respectively. Kaplan-Meier (K-M) curves were generated to stratify patients based on progression-free survival (PFS) and overall survival (OS).</p><p><strong>Results: </strong>The DLRR model had the best performance, with AUCs of 0.981, 0.910, and 0.851 in the training, internal validation, and external validation sets, respectively. In addition, the calibration curve and DCA curve revealed that the DLRR model had good calibration ability and clinical applicability. The K-M curve indicated that the DLRR model provided risk stratification for progression-free survival (PFS) and overall survival (OS) in HCC patients.</p><p><strong>Conclusions: </strong>The DLRR model noninvasively and efficiently predicts ER after curative ablation in HCC patients, which helps to categorize the risk in patients to formulate precise diagnosis and treatment plans and management strategies for patients and to improve the prognosis.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Integrated Model Combined Conventional Radiomics and Deep Learning Features to Predict Early Recurrence of Hepatocellular Carcinoma Eligible for Curative Ablation: A Multicenter Cohort Study.\",\"authors\":\"Yong-Hai Li, Gui-Xiang Qian, Yu Zhu, Xue-di Lei, Lei Tang, Xiang-Yi Bu, Ming-Tong Wei, Wei-Dong Jia\",\"doi\":\"10.1097/RCT.0000000000001764\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving prognosis. This study aimed to develop and validate a model (DLRR) that incorporates deep learning radiomics and traditional radiomics features to predict ER following curative ablation for HCC.</p><p><strong>Methods: </strong>We retrospectively analysed the data of 288 eligible patients from 3 hospitals-1 primary cohort (center 1, n=222) and 2 external test cohorts (center 2, n=32 and center 3, n=34)-from April 2008 to March 2022. 3D ResNet-18 and PyRadiomics were applied to extract features from contrast-enhanced computed tomography (CECT) images. The 3-step (ICC-LASSO-RFE) method was used for feature selection, and 6 machine learning methods were used to construct models. Performance was compared through the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. Calibration and clinical applicability were assessed through calibration curves and decision curve analysis (DCA), respectively. Kaplan-Meier (K-M) curves were generated to stratify patients based on progression-free survival (PFS) and overall survival (OS).</p><p><strong>Results: </strong>The DLRR model had the best performance, with AUCs of 0.981, 0.910, and 0.851 in the training, internal validation, and external validation sets, respectively. In addition, the calibration curve and DCA curve revealed that the DLRR model had good calibration ability and clinical applicability. The K-M curve indicated that the DLRR model provided risk stratification for progression-free survival (PFS) and overall survival (OS) in HCC patients.</p><p><strong>Conclusions: </strong>The DLRR model noninvasively and efficiently predicts ER after curative ablation in HCC patients, which helps to categorize the risk in patients to formulate precise diagnosis and treatment plans and management strategies for patients and to improve the prognosis.</p>\",\"PeriodicalId\":15402,\"journal\":{\"name\":\"Journal of Computer Assisted Tomography\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2025-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer Assisted Tomography\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/RCT.0000000000001764\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Assisted Tomography","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/RCT.0000000000001764","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

目的:肝细胞癌是最常见的原发性肝脏恶性肿瘤。消融治疗是早期HCC的一线治疗方法之一。准确预测早期复发对于制定准确的治疗方案和改善预后至关重要。本研究旨在开发和验证一种模型(DLRR),该模型结合了深度学习放射组学和传统放射组学特征,以预测HCC根治性消融后的ER。方法:我们回顾性分析了2008年4月至2022年3月期间来自3家医院的288例符合条件的患者的数据——1个主要队列(中心1,n=222)和2个外部测试队列(中心2,n=32和中心3,n=34)。应用3D ResNet-18和PyRadiomics从对比增强计算机断层扫描(CECT)图像中提取特征。采用3-step (ICC-LASSO-RFE)方法进行特征选择,采用6种机器学习方法构建模型。通过受试者工作特征曲线下面积(AUC)、净重分类改善(NRI)和综合识别改善(IDI)指数对其性能进行比较。分别通过校准曲线和决策曲线分析(DCA)评估校准和临床适用性。生成Kaplan-Meier (K-M)曲线,根据无进展生存期(PFS)和总生存期(OS)对患者进行分层。结果:DLRR模型在训练集、内部验证集和外部验证集上的auc分别为0.981、0.910和0.851,具有最佳性能。此外,校正曲线和DCA曲线显示DLRR模型具有良好的校正能力和临床适用性。K-M曲线表明DLRR模型为HCC患者的无进展生存期(PFS)和总生存期(OS)提供了风险分层。结论:DLRR模型无创、高效地预测HCC患者根治性消融后ER,有助于对患者进行风险分类,为患者制定精准的诊疗方案和管理策略,改善预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Integrated Model Combined Conventional Radiomics and Deep Learning Features to Predict Early Recurrence of Hepatocellular Carcinoma Eligible for Curative Ablation: A Multicenter Cohort Study.

Objective: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving prognosis. This study aimed to develop and validate a model (DLRR) that incorporates deep learning radiomics and traditional radiomics features to predict ER following curative ablation for HCC.

Methods: We retrospectively analysed the data of 288 eligible patients from 3 hospitals-1 primary cohort (center 1, n=222) and 2 external test cohorts (center 2, n=32 and center 3, n=34)-from April 2008 to March 2022. 3D ResNet-18 and PyRadiomics were applied to extract features from contrast-enhanced computed tomography (CECT) images. The 3-step (ICC-LASSO-RFE) method was used for feature selection, and 6 machine learning methods were used to construct models. Performance was compared through the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. Calibration and clinical applicability were assessed through calibration curves and decision curve analysis (DCA), respectively. Kaplan-Meier (K-M) curves were generated to stratify patients based on progression-free survival (PFS) and overall survival (OS).

Results: The DLRR model had the best performance, with AUCs of 0.981, 0.910, and 0.851 in the training, internal validation, and external validation sets, respectively. In addition, the calibration curve and DCA curve revealed that the DLRR model had good calibration ability and clinical applicability. The K-M curve indicated that the DLRR model provided risk stratification for progression-free survival (PFS) and overall survival (OS) in HCC patients.

Conclusions: The DLRR model noninvasively and efficiently predicts ER after curative ablation in HCC patients, which helps to categorize the risk in patients to formulate precise diagnosis and treatment plans and management strategies for patients and to improve the prognosis.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.50
自引率
0.00%
发文量
230
审稿时长
4-8 weeks
期刊介绍: The mission of Journal of Computer Assisted Tomography is to showcase the latest clinical and research developments in CT, MR, and closely related diagnostic techniques. We encourage submission of both original research and review articles that have immediate or promissory clinical applications. Topics of special interest include: 1) functional MR and CT of the brain and body; 2) advanced/innovative MRI techniques (diffusion, perfusion, rapid scanning); and 3) advanced/innovative CT techniques (perfusion, multi-energy, dose-reduction, and processing).
×
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