{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T13:33:35Z","timestamp":1782135215154,"version":"3.54.5"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Shanghai Nature Science Funding","award":["25ZR1401036"],"award-info":[{"award-number":["25ZR1401036"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01631-2","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T14:39:46Z","timestamp":1754318386000},"page":"1547-1557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and Validation of an Explainable MRI-Based Habitat Radiomics Model for Predicting p53-Abnormal Endometrial Cancer: A Multicentre Feasibility Study"],"prefix":"10.1007","volume":"39","author":[{"given":"Wentao","family":"Jin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guofu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3782-1411","authenticated-orcid":false,"given":"He","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"1631_CR1","doi-asserted-by":"crossref","unstructured":"Crosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N: Endometrial cancer. Lancet 399: 1412-1428, 2022","DOI":"10.1016\/S0140-6736(22)00323-3"},{"key":"1631_CR2","doi-asserted-by":"crossref","unstructured":"Jamieson A, Thompson EF, Huvila J, Gilks CB, McAlpine JN: p53abn endometrial cancer: understanding the most aggressive endometrial cancers in the era of molecular classification. Int J Gynecol Cancer 31: 907-913, 2021","DOI":"10.1136\/ijgc-2020-002256"},{"key":"1631_CR3","doi-asserted-by":"crossref","unstructured":"Soslow RA, Tornos C, Park KJ, Malpica A, Matias-Guiu X, Oliva E, Parkash V, Carlson J, McCluggage WG, Gilks CB: Endometrial carcinoma diagnosis: use of FIGO grading and genomic subcategories in clinical practice: recommendations of the international society of gynecological pathologists. Int J Gynecol Pathol 38: S64-S74, 2019","DOI":"10.1097\/PGP.0000000000000518"},{"key":"1631_CR4","doi-asserted-by":"crossref","unstructured":"Besharat AR, Giannini A, Caserta D: Pathogenesis and treatments of endometrial carcinoma. Clin Exp Obstet Gynecol 50: 229, 2023","DOI":"10.31083\/j.ceog5011229"},{"key":"1631_CR5","doi-asserted-by":"crossref","unstructured":"D\u2019Oria O, Giannini A, Besharat AR, Caserta D: Management of endometrial cancer: molecular identikit and tailored therapeutic approach. Clin Exp Obstet Gynecol 50: 210, 2023","DOI":"10.31083\/j.ceog5010210"},{"key":"1631_CR6","doi-asserted-by":"crossref","unstructured":"Jamieson A, Thompson EF, Huvila J, Leung S, Lum A, Morin C, Ennour-Idrissi K, Sebastianelli A, Renaud MC, Gregoire J, Huntsman DG, Gilks CB, Plante M, Grondin K, McAlpine JN: Endometrial carcinoma molecular subtype correlates with the presence of lymph node metastases. Gynecol Oncol 165: 376-384, 2022","DOI":"10.1016\/j.ygyno.2022.01.025"},{"key":"1631_CR7","doi-asserted-by":"crossref","unstructured":"Raffone A, Travaglino A, Raimondo D, Neola D, Renzulli F, Santoro A, Insabato L, Casadio P, Zannoni GF, Zullo F, Mollo A, Seracchioli R: Prognostic value of myometrial invasion and TCGA groups of endometrial carcinoma. Gynecol Oncol 162: 401-406, 2021","DOI":"10.1016\/j.ygyno.2021.05.029"},{"key":"1631_CR8","doi-asserted-by":"crossref","unstructured":"Travaglino A, Raffone A, Mascolo M, Guida M, Insabato L, Zannoni GF, Zullo F: Clear cell endometrial carcinoma and the TCGA classification. Histopathology 76: 336-338, 2020","DOI":"10.1111\/his.13976"},{"key":"1631_CR9","doi-asserted-by":"crossref","unstructured":"Murali R, Soslow RA, Weigelt B: Classification of endometrial carcinoma: more than two types. Lancet Oncol 15: e268-e278, 2014","DOI":"10.1016\/S1470-2045(13)70591-6"},{"key":"1631_CR10","doi-asserted-by":"crossref","unstructured":"Maheshwari E, Nougaret S, Stein EB, Rauch GM, Hwang KP, Stafford RJ, Klopp AH, Soliman PT, Maturen KE, Rockall AG, Lee SI, Sadowski EA, Venkatesan AM: Update on MRI in evaluation and treatment of endometrial cancer. Radiographics 42: 2112-2130, 2022","DOI":"10.1148\/rg.220070"},{"key":"1631_CR11","doi-asserted-by":"crossref","unstructured":"Nougaret S, Horta M, Sala E, Lakhman Y, Thomassin-Naggara I, Kido A, Masselli G, Bharwani N, Sadowski E, Ertmer A, Otero-Garcia M, Kubik-Huch RA, Cunha TM, Rockall A, Forstner R: Endometrial cancer MRI staging: updated guidelines of the European Society of Urogenital Radiology. Eur Radiol 29: 792-805, 2019","DOI":"10.1007\/s00330-018-5515-y"},{"key":"1631_CR12","doi-asserted-by":"crossref","unstructured":"Lefebvre TL, Ciga O, Bhatnagar SR, Ueno Y, Saif S, Winter-Reinhold E, Dohan A, Soyer P, Forghani R, Siddiqi K, Seuntjens J, Reinhold C, Savadjiev P: Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI. Diagn Interv Imaging 104: 142-152, 2023","DOI":"10.1016\/j.diii.2022.10.007"},{"key":"1631_CR13","doi-asserted-by":"crossref","unstructured":"Xie C, Yang P, Zhang X, Xu L, Wang X, Li X, Zhang L, Xie R, Yang L, Jing Z, Zhang H, Ding L, Kuang Y, Niu T, Wu S: Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy. EBioMedicine 44: 289-297, 2019","DOI":"10.1016\/j.ebiom.2019.05.023"},{"key":"1631_CR14","doi-asserted-by":"crossref","unstructured":"Ma X, Shen M, He Y, Ma F, Liu J, Zhang G, Qiang J: The role of volumetric ADC histogram analysis in preoperatively evaluating the tumour subtype and grade of endometrial cancer. Eur J Radiol 140: 109745, 2021","DOI":"10.1016\/j.ejrad.2021.109745"},{"key":"1631_CR15","doi-asserted-by":"crossref","unstructured":"Chen X, Wang Y, Shen M, Yang B, Zhou Q, Yi Y, Liu W, Zhang G, Yang G, Zhang H: Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution. Eur Radiol 30: 4985-4994, 2020","DOI":"10.1007\/s00330-020-06870-1"},{"key":"1631_CR16","doi-asserted-by":"crossref","unstructured":"Yan BC, Li Y, Ma FH, Feng F, Sun MH, Lin GW, Zhang GF, Qiang JW: Preoperative assessment for high-risk endometrial cancer by developing an MRI- and clinical-based radiomics nomogram: a multicenter study. J Magn Reson Imaging 52: 1872-1882, 2020","DOI":"10.1002\/jmri.27289"},{"key":"1631_CR17","doi-asserted-by":"crossref","unstructured":"Di Donato V, Kontopantelis E, Cuccu I, Sgamba L, Golia D\u2019Aug\u00e8 T, Pernazza A, Della Rocca C, Manganaro L, Catalano C, Perniola G, Palaia I, Tomao F, Giannini A, Muzii L, Bogani G: Magnetic resonance imaging-radiomics in endometrial cancer: a systematic review and meta-analysis. Int J Gynecol Cancer 33: 1070-1076, 2023","DOI":"10.1136\/ijgc-2023-004313"},{"key":"1631_CR18","doi-asserted-by":"crossref","unstructured":"Kim M, Park JE, Kim HS, Kim N, Park SY, Kim YH, Kim JH: Spatiotemporal habitats from multiparametric physiologic MRI distinguish tumor progression from treatment-related change in post-treatment glioblastoma. Eur Radiol 31: 6374-6383, 2021","DOI":"10.1007\/s00330-021-07718-y"},{"key":"1631_CR19","doi-asserted-by":"crossref","unstructured":"Lee DH, Park JE, Kim N, Park SY, Kim YH, Cho YH, Kim HS: Tumor habitat analysis by magnetic resonance imaging distinguishes tumor progression from radiation necrosis in brain metastases after stereotactic radiosurgery. Eur Radiol 32: 497-507, 2022","DOI":"10.1007\/s00330-021-08204-1"},{"key":"1631_CR20","doi-asserted-by":"crossref","unstructured":"Verma R, Correa R, Hill VB, Statsevych V, Bera K, Beig N, Mahammedi A, Madabhushi A, Ahluwalia M, Tiwari P: Tumor habitat-derived radiomic features at pretreatment MRI that are prognostic for progression-free survival in glioblastoma are associated with key morphologic attributes at histopathologic examination: a feasibility study. Radiol Artif Intell 2: e190168, 2020","DOI":"10.1148\/ryai.2020190168"},{"key":"1631_CR21","doi-asserted-by":"crossref","unstructured":"Huvila J, Pors J, Thompson EF, Gilks CB: Endometrial carcinoma: molecular subtypes, precursors and the role of pathology in early diagnosis. J Pathol 253: 355-365, 2021","DOI":"10.1002\/path.5608"},{"key":"1631_CR22","doi-asserted-by":"crossref","unstructured":"Kather JN, Heij LR, Grabsch HI, Loeffler C, Echle A, Muti HS, Krause J, Niehues JM, Sommer KAJ, Bankhead P, Kooreman LFS, Schulte JJ, Cipriani NA, Buelow RD, Boor P, Ortiz-Br\u00fcchle NN, Hanby AM, Speirs V, Kochanny S, Patnaik A, Srisuwananukorn A, Brenner H, Hoffmeister M, van den Brandt PA, J\u00e4ger D, Trautwein C, Pearson AT, Luedde T: Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer 1: 789-799, 2020","DOI":"10.1038\/s43018-020-0087-6"},{"key":"1631_CR23","doi-asserted-by":"crossref","unstructured":"Jeong SY, Park JE, Kim N, Kim HS: Hypovascular cellular tumor in primary central nervous system lymphoma is associated with treatment resistance: tumor habitat analysis using physiologic MRI. AJNR Am J Neuroradiol 43: 40-47, 2022","DOI":"10.3174\/ajnr.A7351"},{"key":"1631_CR24","doi-asserted-by":"crossref","unstructured":"Verma R, Hill VB, Statsevych V, Bera K, Correa R, Leo P, Ahluwalia M, Madabhushi A, Tiwari P: Stable and discriminatory radiomic features from the tumor and its habitat associated with progression-free survival in glioblastoma: a multi-institutional study. AJNR Am J Neuroradiol 43: 1115-1123, 2022","DOI":"10.3174\/ajnr.A7591"},{"key":"1631_CR25","doi-asserted-by":"crossref","unstructured":"Cho HH, Kim H, Nam SY, Lee JE, Han BK, Ko EY, Choi JS, Park H, Ko ES: Measurement of perfusion heterogeneity within tumor habitats on magnetic resonance imaging and its association with prognosis in breast cancer patients. Cancers 14: 1858, 2022","DOI":"10.3390\/cancers14081858"},{"key":"1631_CR26","doi-asserted-by":"crossref","unstructured":"Wang X, Xu C, Grzegorzek M, Sun H: Habitat radiomics analysis of pet\/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival. Front Physiol 13: 948767, 2022","DOI":"10.3389\/fphys.2022.948767"},{"key":"1631_CR27","doi-asserted-by":"crossref","unstructured":"Gaustad JV, Hauge A, Wegner CS, Simonsen TG, Lund KV, Hansem LMK, Rofstad EK: DCE-MRI of tumor hypoxia and hypoxia-associated aggressiveness. Cancers 12: 1979, 2020","DOI":"10.3390\/cancers12071979"},{"key":"1631_CR28","doi-asserted-by":"crossref","unstructured":"Shih IL, Yen RF, Chen CA, Cheng WF, Chen BB, Chang YH, Cheng MF, Shih TT: PET\/MRI in cervical cancer: associations between imaging biomarkers and tumor stage, disease progression, and overall survival. J Magn Reson Imaging 53: 305-318, 2021","DOI":"10.1002\/jmri.27311"},{"key":"1631_CR29","doi-asserted-by":"crossref","unstructured":"Syed AK, Whisenant JG, Barnes SL, Sorace AG, Yankeelov TE: Multiparametric analysis of longitudinal quantitative MRI data to Identify distinct tumor habitats in preclinical models of breast cancer. Cancers 12: 1682, 2020","DOI":"10.3390\/cancers12061682"},{"key":"1631_CR30","doi-asserted-by":"crossref","unstructured":"Dextraze K, Saha A, Kim D, Narang S, Lehrer M, Rao A, Narang S, Rao D, Ahmed S, Madhugiri V, Fuller CD, Kim MM, Krishnan S, Rao G, Rao A: Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma. Oncotarget 8: 112992-113001, 2017","DOI":"10.18632\/oncotarget.22947"},{"key":"1631_CR31","doi-asserted-by":"crossref","unstructured":"Tabassum M, Suman AA, Suero Molina E, Pan E, Di Ieva A, Liu S: Radiomics and machine learning in brain tumors and their habitat: a systematic review. Cancers 15: 3845, 2023","DOI":"10.3390\/cancers15153845"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01631-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01631-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01631-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:23:08Z","timestamp":1776874988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01631-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,4]]},"references-count":31,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["1631"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01631-2","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,4]]},"assertion":[{"value":"16 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"This is an observational study. The Obstetrics and Gynecology Hospital of Fudan University Research Ethics Committee has confirmed that no ethical approval is required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors affirm that human research participants provided informed consent for publication of the images in Figs.\u00a0\n                      \n                      ,\n                      \n                      , and\n                      \n                      .","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}