{"title":"MRI-based intratumoral and peritumoral radiomics for assessing deep myometrial invasion in patients with early-stage endometrioid adenocarcinoma.","authors":"Jing Yang, Yang Liu, Xiaolong Liu, Yaoxin Wang, Xianhong Wang, Conghui Ai, Qiu Bi, Ying Zhao","doi":"10.3389/fonc.2024.1474427","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the effectiveness of magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics models for predicting deep myometrial invasion (DMI) of early-stage endometrioid adenocarcinoma (EAC).</p><p><strong>Methods: </strong>The data of 459 EAC patients from three centers were retrospectively collected. Radiomics features were extracted separately from the intratumoral and peritumoral regions expanded by 0 mm, 5 mm, and 10 mm on unimodal and multimodal MRI. Then, various radiomics models were developed and validated, and the optimal model was confirmed. Integrated models were constructed by ensemble and stacking algorithms based on the above radiomics models. The models' performance was evaluated using the area under the curve (AUC).</p><p><strong>Results: </strong>The multimodal MRI-based radiomics model, which included both intratumoral and peritumoral regions expanded by 5 mm, was the optimal radiomics model, with an AUC of 0.74 in the validation group. When the same integrated algorithm was utilized, the integrated models with 5-mm expansion presented higher AUCs than those with 0-mm and 10-mm expansion in the validation group. The performance of the stacking model and ensemble model with 5-mm expansion was similar, and their AUCs were 0.74 and 0.75, respectively.</p><p><strong>Conclusion: </strong>The multimodal radiomics model from the intratumoral and peritumoral regions expanded by 5 mm has the potential to improve the performance for detecting DMI of early-stage EAC. The integrated models are of little value in increasing the prediction.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"14 ","pages":"1474427"},"PeriodicalIF":3.5000,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774896/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fonc.2024.1474427","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: To evaluate the effectiveness of magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics models for predicting deep myometrial invasion (DMI) of early-stage endometrioid adenocarcinoma (EAC).
Methods: The data of 459 EAC patients from three centers were retrospectively collected. Radiomics features were extracted separately from the intratumoral and peritumoral regions expanded by 0 mm, 5 mm, and 10 mm on unimodal and multimodal MRI. Then, various radiomics models were developed and validated, and the optimal model was confirmed. Integrated models were constructed by ensemble and stacking algorithms based on the above radiomics models. The models' performance was evaluated using the area under the curve (AUC).
Results: The multimodal MRI-based radiomics model, which included both intratumoral and peritumoral regions expanded by 5 mm, was the optimal radiomics model, with an AUC of 0.74 in the validation group. When the same integrated algorithm was utilized, the integrated models with 5-mm expansion presented higher AUCs than those with 0-mm and 10-mm expansion in the validation group. The performance of the stacking model and ensemble model with 5-mm expansion was similar, and their AUCs were 0.74 and 0.75, respectively.
Conclusion: The multimodal radiomics model from the intratumoral and peritumoral regions expanded by 5 mm has the potential to improve the performance for detecting DMI of early-stage EAC. The integrated models are of little value in increasing the prediction.
期刊介绍:
Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.