ArticleCancers2026
Multimodal Large Language Models for Prognostic Prediction in Cervical Cancer Treated with Definitive Chemoradiotherapy: An Exploratory Study of Systematic Multimodal Data Integration.
Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND/
objectivesAbout 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.
methodsIn 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).
resultsForty 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 (
conclusionsA 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.
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