Advances in the Application of Multimodal Models in the Diagnosis and Treatment of Endometrial Cancer

doi:10.12280/gjfckx.20260018

Abstract

Abstract:

Endometrial cancer is one of the most common gynecological malignancies, characterized by significant clinicopathological and molecular heterogeneity. Traditional single-modality information struggles to comprehensively reflect tumor biology and patient prognosis. With the rapid development of artificial intelligence technology, multimodal models provide a novel direction for precision diagnosis and treatment of endometrial cancer by integrating different types of data such as imaging, pathology, clinical information, and genomics, and performing joint analysis with advanced algorithms. This article provides an overview of the basic concepts, development processes, common fusion strategies, and data preprocessing methods for different modalities in multimodal models. It focuses on summarizing their application advances in preoperative evaluation and postoperative prognosis prediction for endometrial cancer, including preoperative differentiation of benign from malignant lesions, early-stage diagnosis, myometrial invasion assessment, and postoperative recurrence risk stratification. However, this field still faces challenges, including significant data heterogeneity, insufficient external validation, and limited interpretability. Future efforts should involve multicenter, prospective studies and enhanced standardization to promote the standardized application of multimodal models in the precise diagnosis and treatment of endometrial cancer.

Key words: Endometrial neoplasms, Carcinoma, Multimodal model, Artificial intelligence, Precision medicine, Prognosis, Therapy

DENG Yuan-yuan, RUAN Xiao-hong, ZHANG Xin, WEI Ji-hong. Advances in the Application of Multimodal Models in the Diagnosis and Treatment of Endometrial Cancer[J]. Journal of International Obstetrics and Gynecology, 2026, 53(4): 374-378.

Figures/Tables 1

References 36

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模态数据	数据来源	主要预处理步骤	常用编码模型
临床与文本数据	结构化数据（人口统计学、肿瘤分期等）、非结构化文本（病理报告、放射学报告等）	缺失值处理、数据标准化、异常值校正、文本清洗与分词、向量化表示	GatorTron、Qwen3、Med-Gemma、Llama-3.2
病理学数据	病理WSI	染色归一化、图像块切分、背景与伪影剔除、特征提取、多图像块聚合	UNI、UNI2-h、Virchow2
影像学数据	CT、MRI、B超等影像	空间标准化与重采样、分割、强度标准化、去噪	RadImageNet
基因组数据	基因表达、变异等基因组特征	质量控制、低表达或低变异过滤、缺失值处理、归一化、降维	SeNMo

模态数据	数据来源	主要预处理步骤	常用编码模型
临床与文本数据	结构化数据（人口统计学、肿瘤分期等）、非结构化文本（病理报告、放射学报告等）	缺失值处理、数据标准化、异常值校正、文本清洗与分词、向量化表示	GatorTron、Qwen3、Med-Gemma、Llama-3.2
病理学数据	病理WSI	染色归一化、图像块切分、背景与伪影剔除、特征提取、多图像块聚合	UNI、UNI2-h、Virchow2
影像学数据	CT、MRI、B超等影像	空间标准化与重采样、分割、强度标准化、去噪	RadImageNet
基因组数据	基因表达、变异等基因组特征	质量控制、低表达或低变异过滤、缺失值处理、归一化、降维	SeNMo