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Application Progress of MRI Histogram Analysis in Endometrial Cancer and Cervical Cancer
MA Shuangyan  CHEN Yuhui  LI Lintang  BI Qiu  REN Lixiang  GONG Zhimei  LIU Hua 

DOI:10.12015/issn.1674-8034.2026.01.030.


[Abstract] Endometrial carcinoma (EC) and cervical carcinoma (CC) are two common gynecological malignancies, with incidence and mortality rates increasing year by year. And in recent years they have tended to occur in younger patients. Early detection and timely treatment are crucial for improving patient survival and preserving fertility. Conventional MRI serves as the primary imaging modality for preoperative assessment, treatment monitoring, and prognostic evaluation in EC and CC. It holds significant value in the morphological assessment of tumors. However, its capability for quantitatively evaluating tumor heterogeneity and microscopic pathological features remains relatively limited. MRI histogram analysis is an image processing technique based on pixel distribution, which can provide more quantitative information and can reflect the biological characteristics of tumors more objectively and comprehensively. Currently there are fewer research reviews about histogram analysis of different MRI parameters in EC and CC, which lacks a systematic and comprehensive combing and in-depth analysis. Therefore, this paper summarizes the research progress of histogram analysis of various MRI parameters in the diagnosis, staging, histopathological features, efficacy and prognosis assessment of EC and CC. We also analysis the current challenges and look forward to the future direction of the research, in order to provide new ideas for future research. We conclude that there are critical challenges in the current research: insufficient standardization of research methods, single-center small-sample designs and insufficient multimodal image-clinical phenotype correlation models, resulting in limited stability of histogram features. In the future, it is necessary to develop an assessment system that integrates multimodal MRI, multicenter large-sample data and artificial intelligence-enhanced MRI histogram technology, with the aim of promoting intelligent diagnosis and treatment of EC and CC with imaging.
[Keywords] endometrial cancer；cervical cancer；magnetic resonance imaging；histogram analysis

MA Shuangyan1, 2   CHEN Yuhui2*   LI Lintang3   BI Qiu2   REN Lixiang2   GONG Zhimei2   LIU Hua1, 2  

1 College of Medicine, Kunming University of Science and Technology, Kunming 650000, China

2 Department of MRI, the Affiliated Hospital of Kunming University of Science and Technology, the First People's Hospital of Yunnan Province, Kunming 650032, China

3 Department of Radiology, Xinping Yi Dai Autonomous County People's Hospital, Yuxi 653499, China

Corresponding author: CHEN Y H, E-mail: yuhuichen1221@163.com

Conflicts of interest   None.

Received  2025-07-30
Accepted  2025-11-10
DOI: 10.12015/issn.1674-8034.2026.01.030
DOI:10.12015/issn.1674-8034.2026.01.030.

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