Next-generation AI framework for comprehensive oral leukoplakia evaluation and management
Abstract Oral potentially malignant disorder poses a significant risk of malignant transformation, particularly in cases with epithelial dysplasia (OED). Current OED assessment methods are invasive and lack reliable decision-support tools for cancer risk evaluation and follow-up optimization. This s...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01885-8 |
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| _version_ | 1849761115779629056 |
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| author | JingWen Li YaFang Zhou MengJing Zhang John Adeoye Jane JingYa Pu MiMi Zhou ChuanXia Liu LiJie Fan Colman McGrath Dian Zhang LiWu Zheng |
| author_facet | JingWen Li YaFang Zhou MengJing Zhang John Adeoye Jane JingYa Pu MiMi Zhou ChuanXia Liu LiJie Fan Colman McGrath Dian Zhang LiWu Zheng |
| author_sort | JingWen Li |
| collection | DOAJ |
| description | Abstract Oral potentially malignant disorder poses a significant risk of malignant transformation, particularly in cases with epithelial dysplasia (OED). Current OED assessment methods are invasive and lack reliable decision-support tools for cancer risk evaluation and follow-up optimization. This study developed and validated OMMT-PredNet, a fully automated multimodal deep learning framework requiring no manual ROI annotation, for non-invasive OED identification and time-dependent cancer risk prediction. Utilizing data from 649 histopathologically confirmed leukoplakia cases across multiple institutions (2003–2024), including 598 cases in the primary cohort and 51 in the external validation set, the model integrated paired high-resolution clinical images and medical records. OMMT-PredNet achieved an AUC of 0.9592 (95% CI: 0.9491–0.9693) for cancer risk prediction and 0.9219 (95% CI: 0.9088–0.9349) for OED identification, with high specificity (MT: 0.9490; OED: 0.9182) and precision (MT: 0.9442; OED: 0.9303). Calibration and decision curve analyses confirmed clinical applicability, while external validation demonstrated robustness. This multidimensional model effectively predicts OED and cancer risk, highlighting its global applicability in enhancing oral cancer screening and improving patient outcomes. |
| format | Article |
| id | doaj-art-5b0abcc6757d48b19d58ec1d17b69bf7 |
| institution | DOAJ |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-5b0abcc6757d48b19d58ec1d17b69bf72025-08-20T03:06:08ZengNature Portfolionpj Digital Medicine2398-63522025-08-018111010.1038/s41746-025-01885-8Next-generation AI framework for comprehensive oral leukoplakia evaluation and managementJingWen Li0YaFang Zhou1MengJing Zhang2John Adeoye3Jane JingYa Pu4MiMi Zhou5ChuanXia Liu6LiJie Fan7Colman McGrath8Dian Zhang9LiWu Zheng10Division of Oral & Maxillofacial Surgery, Faculty of Dentistry, University of Hong KongDepartment of Computer Science and Software Engineering, Shenzhen UniversityDepartment of Computer Science and Software Engineering, Shenzhen UniversityDivision of Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong KongDivision of Oral & Maxillofacial Surgery, Faculty of Dentistry, University of Hong KongStomatology Hospital, Zhejiang University School of MedicineStomatology Hospital, Zhejiang University School of MedicineStomatology Hospital, Zhejiang University School of MedicineDivision of Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong KongDepartment of Computer Science and Software Engineering, Shenzhen UniversityDivision of Oral & Maxillofacial Surgery, Faculty of Dentistry, University of Hong KongAbstract Oral potentially malignant disorder poses a significant risk of malignant transformation, particularly in cases with epithelial dysplasia (OED). Current OED assessment methods are invasive and lack reliable decision-support tools for cancer risk evaluation and follow-up optimization. This study developed and validated OMMT-PredNet, a fully automated multimodal deep learning framework requiring no manual ROI annotation, for non-invasive OED identification and time-dependent cancer risk prediction. Utilizing data from 649 histopathologically confirmed leukoplakia cases across multiple institutions (2003–2024), including 598 cases in the primary cohort and 51 in the external validation set, the model integrated paired high-resolution clinical images and medical records. OMMT-PredNet achieved an AUC of 0.9592 (95% CI: 0.9491–0.9693) for cancer risk prediction and 0.9219 (95% CI: 0.9088–0.9349) for OED identification, with high specificity (MT: 0.9490; OED: 0.9182) and precision (MT: 0.9442; OED: 0.9303). Calibration and decision curve analyses confirmed clinical applicability, while external validation demonstrated robustness. This multidimensional model effectively predicts OED and cancer risk, highlighting its global applicability in enhancing oral cancer screening and improving patient outcomes.https://doi.org/10.1038/s41746-025-01885-8 |
| spellingShingle | JingWen Li YaFang Zhou MengJing Zhang John Adeoye Jane JingYa Pu MiMi Zhou ChuanXia Liu LiJie Fan Colman McGrath Dian Zhang LiWu Zheng Next-generation AI framework for comprehensive oral leukoplakia evaluation and management npj Digital Medicine |
| title | Next-generation AI framework for comprehensive oral leukoplakia evaluation and management |
| title_full | Next-generation AI framework for comprehensive oral leukoplakia evaluation and management |
| title_fullStr | Next-generation AI framework for comprehensive oral leukoplakia evaluation and management |
| title_full_unstemmed | Next-generation AI framework for comprehensive oral leukoplakia evaluation and management |
| title_short | Next-generation AI framework for comprehensive oral leukoplakia evaluation and management |
| title_sort | next generation ai framework for comprehensive oral leukoplakia evaluation and management |
| url | https://doi.org/10.1038/s41746-025-01885-8 |
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