Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning

Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings. Ausführliche Beschreibung

Gespeichert in:

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

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Artificial intelligence Machine learning Oral leukoplakia Oral lichen planus Oral lichenoid lesions Oral potentially malignant disorders

Übergeordnetes Werk:	In: Journal of Big Data - SpringerOpen, 2015, 10(2023), 1, Seite 24
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:1 ; pages:24

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s40537-023-00714-7

Katalog-ID:	DOAJ088754766

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allfields	10.1186/s40537-023-00714-7 doi (DE-627)DOAJ088754766 (DE-599)DOAJf18f545ec3b54e3c9ea5ba566b9c6927 DE-627 ger DE-627 rakwb eng TK7885-7895 T58.5-58.64 QA75.5-76.95 John Adeoye verfasserin aut Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings. Artificial intelligence Machine learning Oral leukoplakia Oral lichen planus Oral lichenoid lesions Oral potentially malignant disorders Computer engineering. Computer hardware Information technology Electronic computers. Computer science Mohamad Koohi-Moghadam verfasserin aut Siu-Wai Choi verfasserin aut Li-Wu Zheng verfasserin aut Anthony Wing Ip Lo verfasserin aut Raymond King-Yin Tsang verfasserin aut Velda Ling Yu Chow verfasserin aut Abdulwarith Akinshipo verfasserin aut Peter Thomson verfasserin aut Yu-Xiong Su verfasserin aut In Journal of Big Data SpringerOpen, 2015 10(2023), 1, Seite 24 (DE-627)79213219X (DE-600)2780218-8 21961115 nnns volume:10 year:2023 number:1 pages:24 https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/article/f18f545ec3b54e3c9ea5ba566b9c6927 kostenfrei https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/toc/2196-1115 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 24
spelling	10.1186/s40537-023-00714-7 doi (DE-627)DOAJ088754766 (DE-599)DOAJf18f545ec3b54e3c9ea5ba566b9c6927 DE-627 ger DE-627 rakwb eng TK7885-7895 T58.5-58.64 QA75.5-76.95 John Adeoye verfasserin aut Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings. Artificial intelligence Machine learning Oral leukoplakia Oral lichen planus Oral lichenoid lesions Oral potentially malignant disorders Computer engineering. Computer hardware Information technology Electronic computers. Computer science Mohamad Koohi-Moghadam verfasserin aut Siu-Wai Choi verfasserin aut Li-Wu Zheng verfasserin aut Anthony Wing Ip Lo verfasserin aut Raymond King-Yin Tsang verfasserin aut Velda Ling Yu Chow verfasserin aut Abdulwarith Akinshipo verfasserin aut Peter Thomson verfasserin aut Yu-Xiong Su verfasserin aut In Journal of Big Data SpringerOpen, 2015 10(2023), 1, Seite 24 (DE-627)79213219X (DE-600)2780218-8 21961115 nnns volume:10 year:2023 number:1 pages:24 https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/article/f18f545ec3b54e3c9ea5ba566b9c6927 kostenfrei https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/toc/2196-1115 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 24
allfields_unstemmed	10.1186/s40537-023-00714-7 doi (DE-627)DOAJ088754766 (DE-599)DOAJf18f545ec3b54e3c9ea5ba566b9c6927 DE-627 ger DE-627 rakwb eng TK7885-7895 T58.5-58.64 QA75.5-76.95 John Adeoye verfasserin aut Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings. Artificial intelligence Machine learning Oral leukoplakia Oral lichen planus Oral lichenoid lesions Oral potentially malignant disorders Computer engineering. Computer hardware Information technology Electronic computers. Computer science Mohamad Koohi-Moghadam verfasserin aut Siu-Wai Choi verfasserin aut Li-Wu Zheng verfasserin aut Anthony Wing Ip Lo verfasserin aut Raymond King-Yin Tsang verfasserin aut Velda Ling Yu Chow verfasserin aut Abdulwarith Akinshipo verfasserin aut Peter Thomson verfasserin aut Yu-Xiong Su verfasserin aut In Journal of Big Data SpringerOpen, 2015 10(2023), 1, Seite 24 (DE-627)79213219X (DE-600)2780218-8 21961115 nnns volume:10 year:2023 number:1 pages:24 https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/article/f18f545ec3b54e3c9ea5ba566b9c6927 kostenfrei https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/toc/2196-1115 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 24
allfieldsGer	10.1186/s40537-023-00714-7 doi (DE-627)DOAJ088754766 (DE-599)DOAJf18f545ec3b54e3c9ea5ba566b9c6927 DE-627 ger DE-627 rakwb eng TK7885-7895 T58.5-58.64 QA75.5-76.95 John Adeoye verfasserin aut Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings. Artificial intelligence Machine learning Oral leukoplakia Oral lichen planus Oral lichenoid lesions Oral potentially malignant disorders Computer engineering. Computer hardware Information technology Electronic computers. Computer science Mohamad Koohi-Moghadam verfasserin aut Siu-Wai Choi verfasserin aut Li-Wu Zheng verfasserin aut Anthony Wing Ip Lo verfasserin aut Raymond King-Yin Tsang verfasserin aut Velda Ling Yu Chow verfasserin aut Abdulwarith Akinshipo verfasserin aut Peter Thomson verfasserin aut Yu-Xiong Su verfasserin aut In Journal of Big Data SpringerOpen, 2015 10(2023), 1, Seite 24 (DE-627)79213219X (DE-600)2780218-8 21961115 nnns volume:10 year:2023 number:1 pages:24 https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/article/f18f545ec3b54e3c9ea5ba566b9c6927 kostenfrei https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/toc/2196-1115 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 24
allfieldsSound	10.1186/s40537-023-00714-7 doi (DE-627)DOAJ088754766 (DE-599)DOAJf18f545ec3b54e3c9ea5ba566b9c6927 DE-627 ger DE-627 rakwb eng TK7885-7895 T58.5-58.64 QA75.5-76.95 John Adeoye verfasserin aut Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings. Artificial intelligence Machine learning Oral leukoplakia Oral lichen planus Oral lichenoid lesions Oral potentially malignant disorders Computer engineering. Computer hardware Information technology Electronic computers. Computer science Mohamad Koohi-Moghadam verfasserin aut Siu-Wai Choi verfasserin aut Li-Wu Zheng verfasserin aut Anthony Wing Ip Lo verfasserin aut Raymond King-Yin Tsang verfasserin aut Velda Ling Yu Chow verfasserin aut Abdulwarith Akinshipo verfasserin aut Peter Thomson verfasserin aut Yu-Xiong Su verfasserin aut In Journal of Big Data SpringerOpen, 2015 10(2023), 1, Seite 24 (DE-627)79213219X (DE-600)2780218-8 21961115 nnns volume:10 year:2023 number:1 pages:24 https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/article/f18f545ec3b54e3c9ea5ba566b9c6927 kostenfrei https://doi.org/10.1186/s40537-023-00714-7 kostenfrei https://doaj.org/toc/2196-1115 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 1 24
language	English
source	In Journal of Big Data 10(2023), 1, Seite 24 volume:10 year:2023 number:1 pages:24
sourceStr	In Journal of Big Data 10(2023), 1, Seite 24 volume:10 year:2023 number:1 pages:24
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial intelligence Machine learning Oral leukoplakia Oral lichen planus Oral lichenoid lesions Oral potentially malignant disorders Computer engineering. Computer hardware Information technology Electronic computers. Computer science
isfreeaccess_bool	true
container_title	Journal of Big Data
authorswithroles_txt_mv	John Adeoye @@aut@@ Mohamad Koohi-Moghadam @@aut@@ Siu-Wai Choi @@aut@@ Li-Wu Zheng @@aut@@ Anthony Wing Ip Lo @@aut@@ Raymond King-Yin Tsang @@aut@@ Velda Ling Yu Chow @@aut@@ Abdulwarith Akinshipo @@aut@@ Peter Thomson @@aut@@ Yu-Xiong Su @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	79213219X
id	DOAJ088754766
language_de	englisch
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callnumber-first	T - Technology
author	John Adeoye
spellingShingle	John Adeoye misc TK7885-7895 misc T58.5-58.64 misc QA75.5-76.95 misc Artificial intelligence misc Machine learning misc Oral leukoplakia misc Oral lichen planus misc Oral lichenoid lesions misc Oral potentially malignant disorders misc Computer engineering. Computer hardware misc Information technology misc Electronic computers. Computer science Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning
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topic_title	TK7885-7895 T58.5-58.64 QA75.5-76.95 Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning Artificial intelligence Machine learning Oral leukoplakia Oral lichen planus Oral lichenoid lesions Oral potentially malignant disorders
topic	misc TK7885-7895 misc T58.5-58.64 misc QA75.5-76.95 misc Artificial intelligence misc Machine learning misc Oral leukoplakia misc Oral lichen planus misc Oral lichenoid lesions misc Oral potentially malignant disorders misc Computer engineering. Computer hardware misc Information technology misc Electronic computers. Computer science
topic_unstemmed	misc TK7885-7895 misc T58.5-58.64 misc QA75.5-76.95 misc Artificial intelligence misc Machine learning misc Oral leukoplakia misc Oral lichen planus misc Oral lichenoid lesions misc Oral potentially malignant disorders misc Computer engineering. Computer hardware misc Information technology misc Electronic computers. Computer science
topic_browse	misc TK7885-7895 misc T58.5-58.64 misc QA75.5-76.95 misc Artificial intelligence misc Machine learning misc Oral leukoplakia misc Oral lichen planus misc Oral lichenoid lesions misc Oral potentially malignant disorders misc Computer engineering. Computer hardware misc Information technology misc Electronic computers. Computer science
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title	Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning
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title_full	Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning
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author_browse	John Adeoye Mohamad Koohi-Moghadam Siu-Wai Choi Li-Wu Zheng Anthony Wing Ip Lo Raymond King-Yin Tsang Velda Ling Yu Chow Abdulwarith Akinshipo Peter Thomson Yu-Xiong Su
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title_sort	predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning
callnumber	TK7885-7895
title_auth	Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning
abstract	Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings.
abstractGer	Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings.
abstract_unstemmed	Abstract Oral cancer may arise from oral leukoplakia and oral lichenoid mucositis (oral lichen planus and oral lichenoid lesions) subtypes of oral potentially malignant disorders. As not all patients will develop oral cancer in their lifetime, the availability of malignant transformation predictive platforms would assist in the individualized treatment planning and formulation of optimal follow-up regimens for these patients. Therefore, this study aims to compare and select optimal machine learning (ML)-based models for stratifying the malignant transformation status of patients with oral leukoplakia and oral lichenoid mucositis. One thousand one hundred and eighty-seven patients with oral leukoplakia and oral lichenoid mucositis treated at three tertiary health institutions in Hong Kong, Newcastle UK, and Lagos Nigeria were included in the study. Demographic, clinical, pathological, and treatment-based factors obtained at diagnosis and during follow-up were used to populate and compare forty-six machine learning-based models. These were implemented as a set of twenty-six predictors for centers with substantial data quantity and fifteen predictors for centers with insufficient data. Two best models were selected according to the number of variables. We found that the optimal ML-based risk models with twenty-six and fifteen predictors achieved an accuracy of 97% and 94% respectively following model testing. Upon external validation, both models achieved a sensitivity, specificity, and F1-score of 1, 0.88, and 0.67 on consecutive patients treated after the construction of the models. Furthermore, the 15-predictor ML model for centers with reduced data achieved a higher sensitivity for identifying oral leukoplakia and oral lichenoid mucositis patients that developed malignancies in other treatment settings compared to the binary oral epithelial dysplasia system for risk stratification (0.96 vs 0.82). These findings suggest that machine learning-based models could be useful potentially to stratify patients with oral leukoplakia and oral lichenoid mucositis according to their risk of malignant transformation in different settings.
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title_short	Predicting oral cancer risk in patients with oral leukoplakia and oral lichenoid mucositis using machine learning
url	https://doi.org/10.1186/s40537-023-00714-7 https://doaj.org/article/f18f545ec3b54e3c9ea5ba566b9c6927 https://doaj.org/toc/2196-1115
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author2	Mohamad Koohi-Moghadam Siu-Wai Choi Li-Wu Zheng Anthony Wing Ip Lo Raymond King-Yin Tsang Velda Ling Yu Chow Abdulwarith Akinshipo Peter Thomson Yu-Xiong Su
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