Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study
BackgroundEndometrial cancer (EC) is one of the most prevalent malignancies affecting the female reproductive system. It poses significant health risks to women and imposes a substantial economic burden on healthcare systems. Early and accurate diagnosis is critical for improving patient outcomes. W...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1600242/full |
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| author | Cuiyan Lin Wanming Chen Jichuang Lai Jieyi Huang Xiaolu Ye Sijia Chen Xinmin Guo Yichun Yang |
| author_facet | Cuiyan Lin Wanming Chen Jichuang Lai Jieyi Huang Xiaolu Ye Sijia Chen Xinmin Guo Yichun Yang |
| author_sort | Cuiyan Lin |
| collection | DOAJ |
| description | BackgroundEndometrial cancer (EC) is one of the most prevalent malignancies affecting the female reproductive system. It poses significant health risks to women and imposes a substantial economic burden on healthcare systems. Early and accurate diagnosis is critical for improving patient outcomes. While traditional diagnostic methods rely on clinical evaluation and imaging, there is growing interest in leveraging artificial intelligence, particularly deep learning (DL), to enhance diagnostic accuracy.MethodsThis study developed a DL-based predictive model integrating multimodal ultrasound features and clinical risk factors to improve early EC diagnosis. A retrospective, multicenter analysis was conducted using 1,443 multimodal ultrasound images—including two-dimensional (2D) and color Doppler images—from 611 patients, of whom 132 were diagnosed with EC and 479 were non-EC cases. Clinical risk factors such as body mass index (BMI), menopausal status, irregular vaginal bleeding, and hypertension were identified as significant predictors (P < 0.05) and incorporated into a clinical model. Separate DL models were trained on 2D and color Doppler ultrasound images, and their performance was evaluated individually and in combination with the clinical model.ResultsThe area under the receiver operating characteristic curve (AUC) for the clinical model was 0.772 (95% CI: 0.690–0.854). The 2D and color Doppler DL models achieved AUCs of 0.792 (95% CI: 0.719–0.864) and 0.813 (95% CI: 0.745–0.881), respectively. When combined with the clinical model, the merged model demonstrated superior predictive performance. In the external validation cohort, the merged model achieved an AUC of 0.892 (95% CI: 0.846–0.938), indicating high diagnostic accuracy.ConclusionsThe integration of multimodal ultrasound imaging and clinical risk factors using DL significantly enhances the accuracy of endometrial cancer diagnosis. The merged model demonstrated strong generalizability in external validation, underscoring its potential clinical utility. Future studies should focus on larger, prospective multicenter trials to further validate these findings and explore the implementation of this approach in personalized patient care. |
| format | Article |
| id | doaj-art-818a50e74f27490bbcf5151d12dabc76 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-818a50e74f27490bbcf5151d12dabc762025-08-20T03:33:43ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-07-011510.3389/fonc.2025.16002421600242Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter studyCuiyan Lin0Wanming Chen1Jichuang Lai2Jieyi Huang3Xiaolu Ye4Sijia Chen5Xinmin Guo6Yichun Yang7Department of Ultrasound, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, ChinaDepartment of Ultrasound, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, ChinaDepartment of Ultrasound, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, ChinaDepartment of Ultrasound, The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, ChinaThe First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Ultrasound, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, ChinaDepartment of Ultrasound, Guangzhou Red Cross Hospital, Guangzhou, Guangdong, ChinaThe First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, ChinaBackgroundEndometrial cancer (EC) is one of the most prevalent malignancies affecting the female reproductive system. It poses significant health risks to women and imposes a substantial economic burden on healthcare systems. Early and accurate diagnosis is critical for improving patient outcomes. While traditional diagnostic methods rely on clinical evaluation and imaging, there is growing interest in leveraging artificial intelligence, particularly deep learning (DL), to enhance diagnostic accuracy.MethodsThis study developed a DL-based predictive model integrating multimodal ultrasound features and clinical risk factors to improve early EC diagnosis. A retrospective, multicenter analysis was conducted using 1,443 multimodal ultrasound images—including two-dimensional (2D) and color Doppler images—from 611 patients, of whom 132 were diagnosed with EC and 479 were non-EC cases. Clinical risk factors such as body mass index (BMI), menopausal status, irregular vaginal bleeding, and hypertension were identified as significant predictors (P < 0.05) and incorporated into a clinical model. Separate DL models were trained on 2D and color Doppler ultrasound images, and their performance was evaluated individually and in combination with the clinical model.ResultsThe area under the receiver operating characteristic curve (AUC) for the clinical model was 0.772 (95% CI: 0.690–0.854). The 2D and color Doppler DL models achieved AUCs of 0.792 (95% CI: 0.719–0.864) and 0.813 (95% CI: 0.745–0.881), respectively. When combined with the clinical model, the merged model demonstrated superior predictive performance. In the external validation cohort, the merged model achieved an AUC of 0.892 (95% CI: 0.846–0.938), indicating high diagnostic accuracy.ConclusionsThe integration of multimodal ultrasound imaging and clinical risk factors using DL significantly enhances the accuracy of endometrial cancer diagnosis. The merged model demonstrated strong generalizability in external validation, underscoring its potential clinical utility. Future studies should focus on larger, prospective multicenter trials to further validate these findings and explore the implementation of this approach in personalized patient care.https://www.frontiersin.org/articles/10.3389/fonc.2025.1600242/fullendometrial cancerpredictive modelultrasound imagingclinical risk factorsdeep learning |
| spellingShingle | Cuiyan Lin Wanming Chen Jichuang Lai Jieyi Huang Xiaolu Ye Sijia Chen Xinmin Guo Yichun Yang Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study Frontiers in Oncology endometrial cancer predictive model ultrasound imaging clinical risk factors deep learning |
| title | Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study |
| title_full | Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study |
| title_fullStr | Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study |
| title_full_unstemmed | Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study |
| title_short | Integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging: a multicenter study |
| title_sort | integrating deep learning and clinical characteristics for early prediction of endometrial cancer using multimodal ultrasound imaging a multicenter study |
| topic | endometrial cancer predictive model ultrasound imaging clinical risk factors deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1600242/full |
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