Tong Zhang, Jialiang Ren, Hui Wu, Yang Gao, He Hu, Yushan Jia, Wenjia Wang
{"title":"Multiparametric MRI radiomics predicts overall survival in hepatocellular carcinoma.","authors":"Tong Zhang, Jialiang Ren, Hui Wu, Yang Gao, He Hu, Yushan Jia, Wenjia Wang","doi":"10.1177/02841851251324572","DOIUrl":null,"url":null,"abstract":"<p><p>BackgroundThe prognosis for hepatocellular carcinoma (HCC) is unfavorable, primarily attributable to the high incidence of recurrence.PurposeTo assess the prognostic value of multiparametric magnetic resonance imaging (mp-MRI) based on radiomic features for overall survival (OS) in patients with HCC.Material and MethodsPatients who underwent abdominal mp-MRI examination before hepatectomy in our hospital between January 2016 and December 2019 were retrospectively collected and divided into a training group and a verification group at a ratio of 7:3. The patients' images, clinical parameters, and semantic features were collected. A three-dimensional volume of interest was delineated and radiomics features were screened. Independent predictors of clinical imaging were screened and combined with radiomics features to construct a combinatorial model and draw a nomogram. The predictive efficacy of the model was evaluated.ResultsThe Harrell's C-index values were 0.737 and 0.711 for the clinical imaging model and 0.705 and 0.704 for the full sequence model in the training group and validation group, respectively. The combinatorial model had higher efficiency, and the C-index values in the training group and the validation group were 0.779 and 0.756, respectively. The survival curve showed that the low-risk group defined by the radiomics signature had significantly better OS than the high-risk group (3-year OS: 61.54% vs. 30.77%; <i>P</i> < 0.05).ConclusionThe combined model can predict the OS of patients with HCC non-invasively before surgical resection and can be used as a clinical tool to guide individualized treatment.</p>","PeriodicalId":7143,"journal":{"name":"Acta radiologica","volume":" ","pages":"2841851251324572"},"PeriodicalIF":1.1000,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta radiologica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/02841851251324572","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
BackgroundThe prognosis for hepatocellular carcinoma (HCC) is unfavorable, primarily attributable to the high incidence of recurrence.PurposeTo assess the prognostic value of multiparametric magnetic resonance imaging (mp-MRI) based on radiomic features for overall survival (OS) in patients with HCC.Material and MethodsPatients who underwent abdominal mp-MRI examination before hepatectomy in our hospital between January 2016 and December 2019 were retrospectively collected and divided into a training group and a verification group at a ratio of 7:3. The patients' images, clinical parameters, and semantic features were collected. A three-dimensional volume of interest was delineated and radiomics features were screened. Independent predictors of clinical imaging were screened and combined with radiomics features to construct a combinatorial model and draw a nomogram. The predictive efficacy of the model was evaluated.ResultsThe Harrell's C-index values were 0.737 and 0.711 for the clinical imaging model and 0.705 and 0.704 for the full sequence model in the training group and validation group, respectively. The combinatorial model had higher efficiency, and the C-index values in the training group and the validation group were 0.779 and 0.756, respectively. The survival curve showed that the low-risk group defined by the radiomics signature had significantly better OS than the high-risk group (3-year OS: 61.54% vs. 30.77%; P < 0.05).ConclusionThe combined model can predict the OS of patients with HCC non-invasively before surgical resection and can be used as a clinical tool to guide individualized treatment.
期刊介绍:
Acta Radiologica publishes articles on all aspects of radiology, from clinical radiology to experimental work. It is known for articles based on experimental work and contrast media research, giving priority to scientific original papers. The distinguished international editorial board also invite review articles, short communications and technical and instrumental notes.