Deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders

Adeoye, John, Koohi-Moghadam, Mohamad, Lo, Anthony Wing Ip, Tsang, Raymond King-Yin, Chow, Velda Ling Yu, Zheng, Li-Wu, Choi, Siu-Wai, Thomson, Peter, and Su, Yu-Xiong (2021) Deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders. Cancers, 13 (23). 6054.

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Abstract

Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms—Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)—and one standard statistical method—Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index—0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions.

Item ID: 71054
Item Type: Article (Research - C1)
ISSN: 2072-6694
Keywords: artificial intelligence; machine learning; oral leukoplakia; oral lichenoid lesions; oral cancer
Copyright Information: Copyright: © 2021 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/licenses/by/4.0/)
Date Deposited: 06 Dec 2021 23:26
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3203 Dentistry > 320305 Oral and maxillofacial surgery @ 50%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3211 Oncology and carcinogenesis > 321109 Predictive and prognostic markers @ 50%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200104 Prevention of human diseases and conditions @ 50%
20 HEALTH > 2001 Clinical health > 200105 Treatment of human diseases and conditions @ 50%
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