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Lavine  \nReceived: 18 February 2025  \nRevised: 18 April 2025  \nAccepted: 30 April 2025  \nPublished: 6 May 2025  \nCitation: Zheng, Z.; Liu, Y.; Feng, L.; Liu, P.; Song, H.; Wang, L.; Huang, F. Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer. Sensors 2025, 25, 2932. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s25092932  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Informatization Construction and Management Department, Huaqiao University, Quanzhou 362021, China; [zhzy@hqu.edu.cn](zhzy@hqu.edu.cn)  \n2 School of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China; [liuyeauto@163.com](liuyeauto@163.com) (Y.L.); [songhs@nwnu.edu.cn](songhs@nwnu.edu.cn) (H.S.)  \n3 College of Medicine, Huaqiao University, Quanzhou 362021, China; [lxfengvon@gmail.com](lxfengvon@gmail.com)  \n4 School of Engineering, Huaqiao University, Quanzhou 362021, China; [pzliu@hqu.edu.cn](pzliu@hqu.edu.cn)  \n5 Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China  \n6 Radiology Department, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China  \n* [Correspondence: wanglin362000@163.com](Correspondence: wanglin362000@163.com) (L.W.); [hf-dt@163.com](hf-dt@163.com) (F.H.)  \nAbstract: This magnetic resonance imaging multimodal fusion study aims to automate the staging of endometrial cancer using deep learning and to compare the diagnostic performance of deep learning with that of radiologists in the staging of endometrial cancer. This study retrospectively investigated 122 patients with pathologically confirmed early EC from January 1, 2025 to December 31, 2021 . Of these patients, 68 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA, and 54 were in FIGO stage IB. Based on the Swin transformer model and its proprietary SW-MSA (shift window multiple self-coherence) module, magnetic resonance imaging (MRI) images in each of the three planes (sagittal, coronal, and transverse) are cropped, enhanced, and classified, and fusion experiments in the three planes are performed simultaneously. Selecting one plane for the experiment, the accuracy of IA and IB classification was 0.988 in the sagittal, 0.96 in the coronal, and 0.94 in the transverse position, and classification accuracy after the fusion of three planes reached 1 . Finally, the automatic classification method based on the Swin transformer hasan accuracy of 1, a recall of 1, and a specificity of 1 for early EC classification. In this study, the multimodal fusion approach accurately classified early EC. It was comparable to what a radiologist would perform and simpler and more precise than previous methods that required segmenting followed by staging.  \nKeywords: endometrial cancer; transformer; deep learning; MRI multi-position; early staging  \n1. Introduction  \nEndometrial cancer arises from the endometrium (the lining of the uterus or womb) [1] . It results from abnormal growth of cells that can invade or spread to other parts of the body. The first sign is most often vaginal bleeding not associated with a menstrual period [2] . Other symptoms include pain with urination, pain during sexual intercourse, or pelvic pain [3] . Endometrial cancer occurs most commonly after menopause. It is the fourth most commonly diagnosed cancer in women and the second most c","cbCaikfM6vAKA8Vl","https://ap.wps.com/l/cbCaikfM6vAKA8Vl","pdf",4364567,16,"English","# Introduction\n## Clinical background and significance\n## Early-stage classification (FIGO IA/IB)\n## Treatment and prognostic role of accurate staging","[{\"question\":\"What problem does the study address in endometrial cancer?\",\"answer\":\"The study targets automated early staging of endometrial cancer and aims to improve staging accuracy using multimodal MRI fusion with deep learning.\"},{\"question\":\"Which model architecture is used for the imaging fusion and classification?\",\"answer\":\"It uses a Swin transformer model with a proprietary SW-MSA (shift window multiple self-coherence) module to fuse MRI information and classify early EC.\"},{\"question\":\"How does the fusion method perform compared with single-plane experiments and prior pipelines?\",\"answer\":\"Single-plane accuracy varies by plane (sagittal 0.988, coronal 0.96, transverse 0.94), while fusion reaches 1.0 for accuracy, recall, and specificity. The approach is also reported as comparable to radiologists and simpler/more precise than methods requiring segmentation followed by staging.\"}]","Multimodal MRI Image Fusion for Early Automatic Staging of Endometrial Cancer | PDF",1790194345]