{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T17:05:37Z","timestamp":1789232737794,"version":"build-2803163510"},"reference-count":36,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"crossref","award":["23Y11907400"],"award-info":[{"award-number":["23Y11907400"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the National Science Foundation for Young Scientists of China","award":["82202143"],"award-info":[{"award-number":["82202143"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Hepatocellular carcinoma (HCC) is the most common primary liver cancer with high mortality. TP53 is the most frequently mutated tumor-suppressor gene in HCC, and P53 mutations are associated with poor differentiation, vascular invasion, and lower survival. Preoperative noninvasive detection of P53-mutated HCC is critical for treatment planning and prognostic assessment. This study aimed to develop and validate a deep learning (DL) model for predicting P53 mutation status in HCC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective two-center study, 373 patients with pathologically confirmed HCC were enrolled from two institutions (Center 1: n=320; Center 2: n=53). The Center 1 cohort was randomly partitioned into a training cohort (n=256) and an internal test cohort (n=64). Patients from Center 2 constituted an independent external test cohort (n=53). We employed a 2.5D ResNet34 architecture to process three-channel dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Separate deep learning models were constructed for intratumoral and peritumoral regions using both single-phase and multiphase (arterial, portal venous, and delayed) fusion sequences. Significant clinical variables were selected to build a Clinical model, which was subsequently integrated with the optimal deep learning model to create a Combined model. Model performance was assessed by the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multiphase fusion models consistently outperformed their single-phase models. The peritumoral (5mm) fusion model (IntraPeri5mm_Fusion) outperformed the intratumoral fusion model, achieving AUCs of 0.881 (95% CI: 0.836\u20130.926), 0.772 (95% CI: 0.651\u20130.893), and 0.757 (95% CI: 0.624\u20130.891) on the training cohort, internal test cohort, and external test cohort, respectively. The Combined model yielded further incremental improvement, with AUCs reaching 0.886 (95% CI: 0.842\u20130.930), 0.805 (95% CI: 0.691\u20130.918), and 0.793 (95% CI: 0.668\u20130.918) across the respective cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed 2.5D DL model based on DCE-MRI effectively predicts P53-mutated HCC. Integrating peritumoral features with clinical variables yields the best performance, offering a promising noninvasive tool for prognostic stratification and individualized therapeutic planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15330338261488956","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:56:39Z","timestamp":1788936999000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["An MRI-Based Intratumoral and Peritumoral 2.5D Deep Learning Model for Predicting P53-Mutated Hepatocellular Carcinoma: A Two-Center Study"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9759-6984","authenticated-orcid":false,"given":"Jingfei","family":"Weng","sequence":"first","affiliation":[{"name":"The Affiliated Traditional Chinese Medicine Hospital"},{"name":"Southwest Medical 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