Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders

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 tran...
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Gespeichert in:

Autor*in:	John Adeoye [verfasserIn] Mohamad Koohi-Moghadam [verfasserIn] Anthony Wing Ip Lo [verfasserIn] Raymond King-Yin Tsang [verfasserIn] Velda Ling Yu Chow [verfasserIn] Li-Wu Zheng [verfasserIn] Siu-Wai Choi [verfasserIn] Peter Thomson [verfasserIn] Yu-Xiong Su [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	artificial intelligence machine learning oral leukoplakia oral lichenoid lesions oral cancer

Übergeordnetes Werk:	In: Cancers - MDPI AG, 2010, 13(2021), 23, p 6054
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:23, p 6054

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/cancers13236054

Katalog-ID:	DOAJ060768681

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callnumber-first	R - Medicine
author	John Adeoye
spellingShingle	John Adeoye misc RC254-282 misc artificial intelligence misc machine learning misc oral leukoplakia misc oral lichenoid lesions misc oral cancer misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders
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topic_title	RC254-282 Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders artificial intelligence machine learning oral leukoplakia oral lichenoid lesions oral cancer
topic	misc RC254-282 misc artificial intelligence misc machine learning misc oral leukoplakia misc oral lichenoid lesions misc oral cancer misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc artificial intelligence misc machine learning misc oral leukoplakia misc oral lichenoid lesions misc oral cancer misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc artificial intelligence misc machine learning misc oral leukoplakia misc oral lichenoid lesions misc oral cancer misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders
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title_sort	deep learning predicts the malignant-transformation-free survival of oral potentially malignant disorders
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title_auth	Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders
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Deep Learning Predicts the Malignant-Transformation-Free Survival of Oral Potentially Malignant Disorders

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