Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features

Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study...
Ausführliche Beschreibung

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Autor*in:	Kamyab-Hesari, Kambiz [verfasserIn] Azhari, Vahidehsadat Ahmadzade, Ali Asadi Amoli, Fahimeh Najafi, Anahita Hasanzadeh, Alireza Beikmarzehei, Alireza

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Sebaceous hyperplasia Sebaceoma Sebaceous adenoma Sebaceous carcinoma Sebaceous neoplasms

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Diagnostic pathology - [S.l.] : BioMed Central, 2006, 18(2023), 1 vom: 07. Aug.
Übergeordnetes Werk:	volume:18 ; year:2023 ; number:1 ; day:07 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s13000-023-01378-w

Katalog-ID:	SPR052664414

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520			\|a Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model.
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allfields	10.1186/s13000-023-01378-w doi (DE-627)SPR052664414 (SPR)s13000-023-01378-w-e DE-627 ger DE-627 rakwb eng Kamyab-Hesari, Kambiz verfasserin aut Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model. Sebaceous hyperplasia (dpeaa)DE-He213 Sebaceoma (dpeaa)DE-He213 Sebaceous adenoma (dpeaa)DE-He213 Sebaceous carcinoma (dpeaa)DE-He213 Sebaceous neoplasms (dpeaa)DE-He213 Azhari, Vahidehsadat aut Ahmadzade, Ali aut Asadi Amoli, Fahimeh aut Najafi, Anahita aut Hasanzadeh, Alireza aut Beikmarzehei, Alireza aut Enthalten in Diagnostic pathology [S.l.] : BioMed Central, 2006 18(2023), 1 vom: 07. Aug. (DE-627)503328960 (DE-600)2210518-9 1746-1596 nnns volume:18 year:2023 number:1 day:07 month:08 https://dx.doi.org/10.1186/s13000-023-01378-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 1 07 08
spelling	10.1186/s13000-023-01378-w doi (DE-627)SPR052664414 (SPR)s13000-023-01378-w-e DE-627 ger DE-627 rakwb eng Kamyab-Hesari, Kambiz verfasserin aut Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model. Sebaceous hyperplasia (dpeaa)DE-He213 Sebaceoma (dpeaa)DE-He213 Sebaceous adenoma (dpeaa)DE-He213 Sebaceous carcinoma (dpeaa)DE-He213 Sebaceous neoplasms (dpeaa)DE-He213 Azhari, Vahidehsadat aut Ahmadzade, Ali aut Asadi Amoli, Fahimeh aut Najafi, Anahita aut Hasanzadeh, Alireza aut Beikmarzehei, Alireza aut Enthalten in Diagnostic pathology [S.l.] : BioMed Central, 2006 18(2023), 1 vom: 07. Aug. (DE-627)503328960 (DE-600)2210518-9 1746-1596 nnns volume:18 year:2023 number:1 day:07 month:08 https://dx.doi.org/10.1186/s13000-023-01378-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 1 07 08
allfields_unstemmed	10.1186/s13000-023-01378-w doi (DE-627)SPR052664414 (SPR)s13000-023-01378-w-e DE-627 ger DE-627 rakwb eng Kamyab-Hesari, Kambiz verfasserin aut Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model. Sebaceous hyperplasia (dpeaa)DE-He213 Sebaceoma (dpeaa)DE-He213 Sebaceous adenoma (dpeaa)DE-He213 Sebaceous carcinoma (dpeaa)DE-He213 Sebaceous neoplasms (dpeaa)DE-He213 Azhari, Vahidehsadat aut Ahmadzade, Ali aut Asadi Amoli, Fahimeh aut Najafi, Anahita aut Hasanzadeh, Alireza aut Beikmarzehei, Alireza aut Enthalten in Diagnostic pathology [S.l.] : BioMed Central, 2006 18(2023), 1 vom: 07. Aug. (DE-627)503328960 (DE-600)2210518-9 1746-1596 nnns volume:18 year:2023 number:1 day:07 month:08 https://dx.doi.org/10.1186/s13000-023-01378-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 1 07 08
allfieldsGer	10.1186/s13000-023-01378-w doi (DE-627)SPR052664414 (SPR)s13000-023-01378-w-e DE-627 ger DE-627 rakwb eng Kamyab-Hesari, Kambiz verfasserin aut Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model. Sebaceous hyperplasia (dpeaa)DE-He213 Sebaceoma (dpeaa)DE-He213 Sebaceous adenoma (dpeaa)DE-He213 Sebaceous carcinoma (dpeaa)DE-He213 Sebaceous neoplasms (dpeaa)DE-He213 Azhari, Vahidehsadat aut Ahmadzade, Ali aut Asadi Amoli, Fahimeh aut Najafi, Anahita aut Hasanzadeh, Alireza aut Beikmarzehei, Alireza aut Enthalten in Diagnostic pathology [S.l.] : BioMed Central, 2006 18(2023), 1 vom: 07. Aug. (DE-627)503328960 (DE-600)2210518-9 1746-1596 nnns volume:18 year:2023 number:1 day:07 month:08 https://dx.doi.org/10.1186/s13000-023-01378-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 1 07 08
allfieldsSound	10.1186/s13000-023-01378-w doi (DE-627)SPR052664414 (SPR)s13000-023-01378-w-e DE-627 ger DE-627 rakwb eng Kamyab-Hesari, Kambiz verfasserin aut Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model. Sebaceous hyperplasia (dpeaa)DE-He213 Sebaceoma (dpeaa)DE-He213 Sebaceous adenoma (dpeaa)DE-He213 Sebaceous carcinoma (dpeaa)DE-He213 Sebaceous neoplasms (dpeaa)DE-He213 Azhari, Vahidehsadat aut Ahmadzade, Ali aut Asadi Amoli, Fahimeh aut Najafi, Anahita aut Hasanzadeh, Alireza aut Beikmarzehei, Alireza aut Enthalten in Diagnostic pathology [S.l.] : BioMed Central, 2006 18(2023), 1 vom: 07. Aug. (DE-627)503328960 (DE-600)2210518-9 1746-1596 nnns volume:18 year:2023 number:1 day:07 month:08 https://dx.doi.org/10.1186/s13000-023-01378-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_168 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 1 07 08
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topic_facet	Sebaceous hyperplasia Sebaceoma Sebaceous adenoma Sebaceous carcinoma Sebaceous neoplasms
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author	Kamyab-Hesari, Kambiz
spellingShingle	Kamyab-Hesari, Kambiz misc Sebaceous hyperplasia misc Sebaceoma misc Sebaceous adenoma misc Sebaceous carcinoma misc Sebaceous neoplasms Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features
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topic_title	Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features Sebaceous hyperplasia (dpeaa)DE-He213 Sebaceoma (dpeaa)DE-He213 Sebaceous adenoma (dpeaa)DE-He213 Sebaceous carcinoma (dpeaa)DE-He213 Sebaceous neoplasms (dpeaa)DE-He213
topic	misc Sebaceous hyperplasia misc Sebaceoma misc Sebaceous adenoma misc Sebaceous carcinoma misc Sebaceous neoplasms
topic_unstemmed	misc Sebaceous hyperplasia misc Sebaceoma misc Sebaceous adenoma misc Sebaceous carcinoma misc Sebaceous neoplasms
topic_browse	misc Sebaceous hyperplasia misc Sebaceoma misc Sebaceous adenoma misc Sebaceous carcinoma misc Sebaceous neoplasms
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title	Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features
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title_full	Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features
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author_browse	Kamyab-Hesari, Kambiz Azhari, Vahidehsadat Ahmadzade, Ali Asadi Amoli, Fahimeh Najafi, Anahita Hasanzadeh, Alireza Beikmarzehei, Alireza
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title_sort	machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features
title_auth	Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features
abstract	Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model. © The Author(s) 2023
abstractGer	Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model. © The Author(s) 2023
abstract_unstemmed	Background This observational study aims to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model. © The Author(s) 2023
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title_short	Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features
url	https://dx.doi.org/10.1186/s13000-023-01378-w
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author2	Azhari, Vahidehsadat Ahmadzade, Ali Asadi Amoli, Fahimeh Najafi, Anahita Hasanzadeh, Alireza Beikmarzehei, Alireza
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up_date	2024-07-03T13:53:55.466Z
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Methods This cross-sectional study was conducted on Iranian patients with sebaceous tumors from two hospitals between March 2015 and March 2019. Pathology slides were reviewed by two pathologists and the architectural and cytological attributes were recorded. Multiple decision tree models were trained using 5-fold cross validation to determine the most important predictor variables and to develop a simple prediction model. Results This study assessed the characteristics of 123 sebaceous tumors. Histopathological findings, including pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features, including highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma and sebaceous adenoma cases had features like high mitotic activity, which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: basaloid cell count, peripheral basaloid cell layers, tumor margin, nuclear size, and chromatin. Conclusions This study implemented a machine learning modeling approach to help optimally categorize sebaceous lesions based on architectural and nuclear features. 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Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features

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