{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T16:20:10Z","timestamp":1788193210350,"version":"build-2803163510"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lymphovascular space invasion (LVSI) is an important pathological feature associated with tumor aggressiveness and adverse prognosis in cervical cancer. However, reliable preoperative prediction of LVSI remains a challenge. This study aimed to develop and validate an MRI-based radiomics model incorporating intratumoral and peritumoral features for LVSI prediction and systematically evaluate the optimal peritumoral extent.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>In this single-center study comprising both retrospective and prospective cohorts, 204 patients with pathologically confirmed cervical cancer were randomly divided into a training cohort (n = 142) and a test cohort (n = 62). Radiomics features were extracted from the intratumoral and peritumoral regions with expansion distances of 1, 3, and 5\u00a0mm. Feature selection was performed using intraclass correlation coefficient (ICC) analysis, minimum redundancy maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) analysis. Six machine learning algorithms, including logistic regression [LR], support vector machine, random forest, ExtraTrees, LightGBM, and multilayer perceptron, were used to construct the predictive models. The model performance was evaluated using receiver operating characteristic analysis, calibration curves, and decision curve analysis. SHapley Additive Explanations (SHAP) were applied to interpret the optimal models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among all ROI configurations, the Intra+P1 model demonstrated the best overall performance, particularly when it was combined with LR. The LR-based Intra+P1 model achieved an AUC of 0.866 (95% CI: 0.807\u20130.926) in the training cohort and 0.843 (95% CI: 0.731\u20130.955) in the test cohort respectively. Increasing the peritumoral expansion from 1\u00a0mm to 3\u00a0mm or 5\u00a0mm did not further improve predictive performance. The addition of clinical variables (tumor diameter and SCC level) did not provide a significant incremental predictive value. Calibration and decision curve analyses demonstrated good agreement and favorable clinical utility of the model. SHAP analysis showed that both intratumoral and peritumoral features contributed substantially to the model prediction, with texture features playing a dominant role.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This interpretable MRI-based radiomics model facilitates accurate preoperative prediction of LVSI in cervical cancer. A narrowly defined 1-mm peritumoral region provides the most informative complementary information, underscoring the importance of the tumor-invading front. This approach offers a noninvasive tool for risk stratification and may support individualized treatment decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1859587","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T16:03:05Z","timestamp":1788192185000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An interpretable MRI radiomics approach for preoperative prediction of lymphovascular space invasion in cervical cancer using optimal peritumoral region"],"prefix":"10.3389","volume":"16","author":[{"given":"Xianyan","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Radiology, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanfang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengbin","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Affiliated People\u2019s Hospital of Fujian University of Traditional Chinese Medicine","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.30667","article-title":"Cervical cancer: a global health crisis","volume":"123","author":"Small","year":"2017","journal-title":"Cancer"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(18)32470-x","article-title":"Cervical cancer","volume":"393","author":"Cohen","year":"2019","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1136\/ijgc-2019-000849","article-title":"Prognostic significance of lymphovascular space invasion for stage Ia1 and Ia2 cervical cancer","volume":"30","author":"Margolis","year":"2020","journal-title":"Int J Gynecol Cancer"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.suronc.2021.101566","article-title":"Variability in the identification of lymphovascular space invasion for early stage cervical cancer","volume":"38","author":"Andikyan","year":"2021","journal-title":"Surg Oncol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.ygyno.2021.06.002","article-title":"The role of semiquantitative evaluation of lympho-vascular space invasion in early stage cervical cancer patients","volume":"162","author":"Ronsini","year":"2021","journal-title":"Gynecol Oncol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2023.0062","article-title":"Nccn guidelines\u00ae insights: Cervical cancer, version 1.2024","volume":"21","author":"Abu-Rustum","year":"2023","journal-title":"J Natl Compr Canc Netw"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.637794","article-title":"Preoperative prediction of lymphovascular space invasion in cervical cancer with radiomics -based nomogram","volume":"11","author":"Du","year":"2021","journal-title":"Front Oncol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08326-6","article-title":"Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer","volume":"32","author":"Zhou","year":"2022","journal-title":"Eur Radiol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"key":"B11","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat Commun"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26531","article-title":"Mr-based radiomics nomogram of cervical cancer in prediction of the lymph-vascular space invasion preoperatively","volume":"49","author":"Li","year":"2019","journal-title":"J Magn Reson Imaging"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2024-009616","article-title":"Intratumoral and peritumoral radiomics of mris predicts pathologic complete response to neoadjuvant chemoimmunotherapy in patients with head and neck squamous cell carcinoma","volume":"12","author":"Lin","year":"2024","journal-title":"J Immunother Cancer"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-10042-2","article-title":"Intratumoral and peritumoral mri radiomics nomogram for predicting parametrial invasion in patients with early-stage cervical adenocarcinoma and adenosquamous carcinoma","volume":"34","author":"Xiao","year":"2024","journal-title":"Eur Radiol"},{"key":"B16","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1007\/s11547-025-01969-1","article-title":"Predictive value of tumoral and peritumoral radiomic features in neoadjuvant chemotherapy response for breast cancer: a retrospective study","volume":"130","author":"Pesapane","year":"2025","journal-title":"Radiol Med"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.2196\/71091","article-title":"Imaging-based ai for predicting lymphovascular space invasion in cervical cancer: systematic review and meta-analysis","volume":"27","author":"She","year":"2025","journal-title":"J Med Internet Res"},{"key":"B18","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s00432-025-06253-3","article-title":"Associations between mri radiomics analysis and tumor-micro milieu in uterine cervical cancer","volume":"151","author":"Meyer","year":"2025","journal-title":"J Cancer Res Clin Oncol"},{"key":"B19","doi-asserted-by":"publisher","first-page":"26754","DOI":"10.1038\/s41598-025-12162-9","article-title":"Preoperative mri-based radiomics analysis of intra- and peritumoral regions for predicting cd3 expression in early cervical cancer","volume":"15","author":"Zhang","year":"2025","journal-title":"Sci Rep"},{"key":"B20","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/s13244-023-01405-w","article-title":"Intra- and peri-tumoral mri radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer","volume":"14","author":"Zhang","year":"2023","journal-title":"Insights Imaging"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.5152\/dir.2022.20657","article-title":"Multi-parametric mri-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer","volume":"28","author":"Cui","year":"2022","journal-title":"Diagn Interv Radiol"},{"key":"B22","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1186\/s12905-024-03001-6","article-title":"Deep-learning-based radiomics of intratumoral and peritumoral mri images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Womens Health"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1252074","article-title":"Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study","volume":"13","author":"Wang","year":"2023","journal-title":"Front Oncol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1578119","article-title":"Multiparametric magnetic resonance imaging-based comprehensive model on prediction of lymphovascular space invasion in cervical cancer","volume":"15","author":"Yang","year":"2025","journal-title":"Front Oncol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1093\/bjr\/tqaf248","article-title":"Multiparametric mri radiomics models for preoperative assessment of lymph vascular space invasion status in early-stage cervical cancer: a 2-centre retrospective study","volume":"99","author":"Deng","year":"2026","journal-title":"Br J Radiol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"B27","doi-asserted-by":"publisher","first-page":"356","DOI":"10.3390\/bioengineering12040356","article-title":"Explainable ai for chronic kidney disease prediction in medical iot: integrating gans and few-shot learning","volume":"12","author":"Rezk","year":"2025","journal-title":"Bioengineering (Basel)"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2024.115063","article-title":"Risk factors for lymph node metastasis in women with figo 2018 ia cervical cancer with a horizontal spread of > 7 mm","volume":"212","author":"Wenzel","year":"2024","journal-title":"Eur J Cancer"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2024.09.022","article-title":"The prognosis of stage ia cervical cancer: subgroup analysis of the sccan study","volume":"191","author":"Ng","year":"2024","journal-title":"Gynecol Oncol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.3390\/cancers17081320","article-title":"Adjuvant therapy in \"intermediate-risk\" early-stage cervical cancer: to treat or not to treat? systematic review and meta-analysis","volume":"17","author":"Ripepi","year":"2025","journal-title":"Cancers (Basel)"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1859587\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T16:03:06Z","timestamp":1788192186000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1859587\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":30,"alternative-id":["10.3389\/fonc.2026.1859587"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1859587","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]},"article-number":"1859587"}}