{
  "abstract": "Background/objectives\n\nAbout 25-35% 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\n\nIn 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-an ablation of input modalities-were 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\n\nForty 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 0.687-0.883; ΔAUC +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 0.78-0.83).Conclusions\n\nA 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhaoqi Gu"
    },
    {
      "affs": [
        0
      ],
      "name": "Chen Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Qizhen Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Weiping Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yidong Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Ke Hu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b9e1vqa3g7zqx.bib",
  "cited_by": 0,
  "doi": "10.3390/cancers18193136",
  "fulltext_url": "https://europepmc.org/articles/PMC13664468",
  "id": "b9e1vqa3g7zqx",
  "issns": [
    "2072-6694"
  ],
  "landing_url": "https://europepmc.org/article/MED/42882831",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7214636705",
  "pdf_url": "https://europepmc.org/articles/PMC13664468?pdf=render",
  "pmcid": "PMC13664468",
  "pmid": "42882831",
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b9e1vqa3g7zqx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42882831",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42882831"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Multimodal Large Language Models for Prognostic Prediction in Cervical Cancer Treated with Definitive Chemoradiotherapy: An Exploratory Study of Systematic Multimodal Data Integration.",
  "title_text": "Multimodal Large Language Models for Prognostic Prediction in Cervical Cancer Treated with Definitive Chemoradiotherapy: An Exploratory Study of Systematic Multimodal Data Integration.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/b9e1vqa3g7zqx",
  "venue": "Cancers",
  "versions": []
}