{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:59:32Z","timestamp":1792789172362,"version":"4.5.0"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Municipal Science & Technology Commission","award":["Z251100004625026"],"award-info":[{"award-number":["Z251100004625026"]}],"id":[{"id":"https:\/\/ror.org\/034k14f91","id-type":"ROR","asserted-by":"crossref"}]},{"award":["Z251100004625026"],"award-info":[{"award-number":["Z251100004625026"]}],"id":[{"id":"https:\/\/ror.org\/034k14f91","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: About 25\u201335% of patients with cervical cancer treated with definitive concurrent chemoradiotherapy (CCRT) relapse within five years, and non-imaging clinical factors stratify their risk only modestly. We evaluated whether a general-purpose multimodal large language model (MLLM), used without fine-tuning, could estimate recurrence risk in this setting. Methods: In this retrospective single-center study, 82 patients treated with definitive radiotherapy (79\/82 with concurrent platinum-based chemotherapy) who had a complete pretreatment MRI report were analyzed. Gemini 3.1 Pro generated a structured report from pelvic MRI images and, separately, estimated recurrence\/metastasis risk from clinical, laboratory, treatment and imaging data. Because treatment cycles and overall treatment time actually completed were included, this was a retrospective treatment-complete risk assessment rather than a strictly pretreatment prediction. Five prompting strategies differing only in their inputs\u2014an ablation of input modalities\u2014were each run three times; AI reports were graded against paired human reports on a 14-item rubric by an independent model (Claude Opus 4.6). Results: Forty patients (48.8%) relapsed. Discrimination rose from an AUC of 0.704 with a clinical baseline to 0.790 with the full multimodal input (95% CI 0.687\u20130.883; \u0394AUC +0.085). This gain was significant on our primary two-sided bootstrap test after Holm correction for ten pairwise comparisons (p = 0.032; a DeLong sensitivity analysis supported some but not all the imaging-benefit comparisons). Adding MRI report text significantly improved the AUC over the clinical baseline, and no statistically significant difference was detected between the human-report and AI-report strategies; this was not an equivalence or non-inferiority test; and the further lymph-node increment was not statistically significant. The AI reports scored 68.8% against an LLM judge on the rubric, with apparent overcalling of parametrial invasion and an inability to assess lymph nodes within the supplied field of view; all strategies underestimated absolute risk (E\/O 0.78\u20130.83). Conclusions: A non-fine-tuned MLLM can integrate multimodal data into a prognostic estimate, but its automatically generated MRI reports are frequently discordant with radiologist reports in specific ways and require expert review and external validation before clinical use.<\/jats:p>","DOI":"10.3390\/cancers18193136","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:35:59Z","timestamp":1790595359000},"page":"3136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Large Language Models for Prognostic Prediction in Cervical Cancer Treated with Definitive Chemoradiotherapy: An Exploratory Study of Systematic Multimodal Data Integration"],"prefix":"10.3390","volume":"18","author":[{"given":"Zhaoqi","family":"Gu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Qizhen","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Weiping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Yidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Ke","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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