A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study.
Ruixin Yan, Siyuan Qin, Jiajia Xu, Weili Zhao, Peijin Xin, Xiaoying Xing, Ning Lang
{"title":"A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study.","authors":"Ruixin Yan, Siyuan Qin, Jiajia Xu, Weili Zhao, Peijin Xin, Xiaoying Xing, Ning Lang","doi":"10.1186/s40644-024-00743-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC.</p><p><strong>Methods: </strong>Patients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2D<sub>intra</sub> and 3D<sub>intra</sub>), peritumoral (2D<sub>peri</sub> and 3D<sub>peri</sub>), and combined models (2D<sub>intra + peri</sub> and 3D<sub>intra + peri</sub>) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong's test.</p><p><strong>Results: </strong>No significant differences in AUC were observed between the 2D<sub>intra</sub> and 3D<sub>intra</sub> models, or the 2D<sub>peri</sub> and 3D<sub>peri</sub> models in all prediction tasks (P > 0.05). Significant difference was observed between the 3D<sub>intra</sub> and 3D<sub>peri</sub> models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3D<sub>intra + peri</sub> models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3D<sub>intra</sub> model in both the training and validation cohorts (P < 0.05).</p><p><strong>Conclusions: </strong>Comparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293005/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40644-024-00743-2","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC.
Methods: Patients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2Dintra and 3Dintra), peritumoral (2Dperi and 3Dperi), and combined models (2Dintra + peri and 3Dintra + peri) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong's test.
Results: No significant differences in AUC were observed between the 2Dintra and 3Dintra models, or the 2Dperi and 3Dperi models in all prediction tasks (P > 0.05). Significant difference was observed between the 3Dintra and 3Dperi models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3Dintra + peri models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3Dintra model in both the training and validation cohorts (P < 0.05).
Conclusions: Comparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.
Cancer ImagingONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍:
Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology.
The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include:
Breast Imaging
Chest
Complications of treatment
Ear, Nose & Throat
Gastrointestinal
Hepatobiliary & Pancreatic
Imaging biomarkers
Interventional
Lymphoma
Measurement of tumour response
Molecular functional imaging
Musculoskeletal
Neuro oncology
Nuclear Medicine
Paediatric.