Analyzing breast cancer invasive disease event classification through explainable artificial intelligence

IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an E...
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Autor*in:	Raffaella Massafra [verfasserIn] Annarita Fanizzi [verfasserIn] Nicola Amoroso [verfasserIn] Samantha Bove [verfasserIn] Maria Colomba Comes [verfasserIn] Domenico Pomarico [verfasserIn] Vittorio Didonna [verfasserIn] Sergio Diotaiuti [verfasserIn] Luisa Galati [verfasserIn] Francesco Giotta [verfasserIn] Daniele La Forgia [verfasserIn] Agnese Latorre [verfasserIn] Angela Lombardi [verfasserIn] Annalisa Nardone [verfasserIn] Maria Irene Pastena [verfasserIn] Cosmo Maurizio Ressa [verfasserIn] Lucia Rinaldi [verfasserIn] Pasquale Tamborra [verfasserIn] Alfredo Zito [verfasserIn] Angelo Virgilio Paradiso [verfasserIn] Roberto Bellotti [verfasserIn] Vito Lorusso [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	invasive disease events breast cancer explainable AI 10-year follow up 5-year follow up

Übergeordnetes Werk:	In: Frontiers in Medicine - Frontiers Media S.A., 2014, 10(2023)
Übergeordnetes Werk:	volume:10 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fmed.2023.1116354

Katalog-ID:	DOAJ08119496X

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520			\|a IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice
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allfields	10.3389/fmed.2023.1116354 doi (DE-627)DOAJ08119496X (DE-599)DOAJ6a6b097c0dac4ec68f7e132a2023c0ef DE-627 ger DE-627 rakwb eng R5-920 Raffaella Massafra verfasserin aut Analyzing breast cancer invasive disease event classification through explainable artificial intelligence 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice invasive disease events breast cancer explainable AI 10-year follow up 5-year follow up Medicine (General) Annarita Fanizzi verfasserin aut Nicola Amoroso verfasserin aut Nicola Amoroso verfasserin aut Samantha Bove verfasserin aut Maria Colomba Comes verfasserin aut Domenico Pomarico verfasserin aut Domenico Pomarico verfasserin aut Vittorio Didonna verfasserin aut Sergio Diotaiuti verfasserin aut Luisa Galati verfasserin aut Francesco Giotta verfasserin aut Daniele La Forgia verfasserin aut Agnese Latorre verfasserin aut Angela Lombardi verfasserin aut Annalisa Nardone verfasserin aut Maria Irene Pastena verfasserin aut Cosmo Maurizio Ressa verfasserin aut Lucia Rinaldi verfasserin aut Pasquale Tamborra verfasserin aut Alfredo Zito verfasserin aut Angelo Virgilio Paradiso verfasserin aut Roberto Bellotti verfasserin aut Roberto Bellotti verfasserin aut Vito Lorusso verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 10(2023) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:10 year:2023 https://doi.org/10.3389/fmed.2023.1116354 kostenfrei https://doaj.org/article/6a6b097c0dac4ec68f7e132a2023c0ef kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2023.1116354/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
spelling	10.3389/fmed.2023.1116354 doi (DE-627)DOAJ08119496X (DE-599)DOAJ6a6b097c0dac4ec68f7e132a2023c0ef DE-627 ger DE-627 rakwb eng R5-920 Raffaella Massafra verfasserin aut Analyzing breast cancer invasive disease event classification through explainable artificial intelligence 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice invasive disease events breast cancer explainable AI 10-year follow up 5-year follow up Medicine (General) Annarita Fanizzi verfasserin aut Nicola Amoroso verfasserin aut Nicola Amoroso verfasserin aut Samantha Bove verfasserin aut Maria Colomba Comes verfasserin aut Domenico Pomarico verfasserin aut Domenico Pomarico verfasserin aut Vittorio Didonna verfasserin aut Sergio Diotaiuti verfasserin aut Luisa Galati verfasserin aut Francesco Giotta verfasserin aut Daniele La Forgia verfasserin aut Agnese Latorre verfasserin aut Angela Lombardi verfasserin aut Annalisa Nardone verfasserin aut Maria Irene Pastena verfasserin aut Cosmo Maurizio Ressa verfasserin aut Lucia Rinaldi verfasserin aut Pasquale Tamborra verfasserin aut Alfredo Zito verfasserin aut Angelo Virgilio Paradiso verfasserin aut Roberto Bellotti verfasserin aut Roberto Bellotti verfasserin aut Vito Lorusso verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 10(2023) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:10 year:2023 https://doi.org/10.3389/fmed.2023.1116354 kostenfrei https://doaj.org/article/6a6b097c0dac4ec68f7e132a2023c0ef kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2023.1116354/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
allfields_unstemmed	10.3389/fmed.2023.1116354 doi (DE-627)DOAJ08119496X (DE-599)DOAJ6a6b097c0dac4ec68f7e132a2023c0ef DE-627 ger DE-627 rakwb eng R5-920 Raffaella Massafra verfasserin aut Analyzing breast cancer invasive disease event classification through explainable artificial intelligence 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice invasive disease events breast cancer explainable AI 10-year follow up 5-year follow up Medicine (General) Annarita Fanizzi verfasserin aut Nicola Amoroso verfasserin aut Nicola Amoroso verfasserin aut Samantha Bove verfasserin aut Maria Colomba Comes verfasserin aut Domenico Pomarico verfasserin aut Domenico Pomarico verfasserin aut Vittorio Didonna verfasserin aut Sergio Diotaiuti verfasserin aut Luisa Galati verfasserin aut Francesco Giotta verfasserin aut Daniele La Forgia verfasserin aut Agnese Latorre verfasserin aut Angela Lombardi verfasserin aut Annalisa Nardone verfasserin aut Maria Irene Pastena verfasserin aut Cosmo Maurizio Ressa verfasserin aut Lucia Rinaldi verfasserin aut Pasquale Tamborra verfasserin aut Alfredo Zito verfasserin aut Angelo Virgilio Paradiso verfasserin aut Roberto Bellotti verfasserin aut Roberto Bellotti verfasserin aut Vito Lorusso verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 10(2023) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:10 year:2023 https://doi.org/10.3389/fmed.2023.1116354 kostenfrei https://doaj.org/article/6a6b097c0dac4ec68f7e132a2023c0ef kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2023.1116354/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
allfieldsGer	10.3389/fmed.2023.1116354 doi (DE-627)DOAJ08119496X (DE-599)DOAJ6a6b097c0dac4ec68f7e132a2023c0ef DE-627 ger DE-627 rakwb eng R5-920 Raffaella Massafra verfasserin aut Analyzing breast cancer invasive disease event classification through explainable artificial intelligence 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice invasive disease events breast cancer explainable AI 10-year follow up 5-year follow up Medicine (General) Annarita Fanizzi verfasserin aut Nicola Amoroso verfasserin aut Nicola Amoroso verfasserin aut Samantha Bove verfasserin aut Maria Colomba Comes verfasserin aut Domenico Pomarico verfasserin aut Domenico Pomarico verfasserin aut Vittorio Didonna verfasserin aut Sergio Diotaiuti verfasserin aut Luisa Galati verfasserin aut Francesco Giotta verfasserin aut Daniele La Forgia verfasserin aut Agnese Latorre verfasserin aut Angela Lombardi verfasserin aut Annalisa Nardone verfasserin aut Maria Irene Pastena verfasserin aut Cosmo Maurizio Ressa verfasserin aut Lucia Rinaldi verfasserin aut Pasquale Tamborra verfasserin aut Alfredo Zito verfasserin aut Angelo Virgilio Paradiso verfasserin aut Roberto Bellotti verfasserin aut Roberto Bellotti verfasserin aut Vito Lorusso verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 10(2023) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:10 year:2023 https://doi.org/10.3389/fmed.2023.1116354 kostenfrei https://doaj.org/article/6a6b097c0dac4ec68f7e132a2023c0ef kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2023.1116354/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
allfieldsSound	10.3389/fmed.2023.1116354 doi (DE-627)DOAJ08119496X (DE-599)DOAJ6a6b097c0dac4ec68f7e132a2023c0ef DE-627 ger DE-627 rakwb eng R5-920 Raffaella Massafra verfasserin aut Analyzing breast cancer invasive disease event classification through explainable artificial intelligence 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice invasive disease events breast cancer explainable AI 10-year follow up 5-year follow up Medicine (General) Annarita Fanizzi verfasserin aut Nicola Amoroso verfasserin aut Nicola Amoroso verfasserin aut Samantha Bove verfasserin aut Maria Colomba Comes verfasserin aut Domenico Pomarico verfasserin aut Domenico Pomarico verfasserin aut Vittorio Didonna verfasserin aut Sergio Diotaiuti verfasserin aut Luisa Galati verfasserin aut Francesco Giotta verfasserin aut Daniele La Forgia verfasserin aut Agnese Latorre verfasserin aut Angela Lombardi verfasserin aut Annalisa Nardone verfasserin aut Maria Irene Pastena verfasserin aut Cosmo Maurizio Ressa verfasserin aut Lucia Rinaldi verfasserin aut Pasquale Tamborra verfasserin aut Alfredo Zito verfasserin aut Angelo Virgilio Paradiso verfasserin aut Roberto Bellotti verfasserin aut Roberto Bellotti verfasserin aut Vito Lorusso verfasserin aut In Frontiers in Medicine Frontiers Media S.A., 2014 10(2023) (DE-627)789482991 (DE-600)2775999-4 2296858X nnns volume:10 year:2023 https://doi.org/10.3389/fmed.2023.1116354 kostenfrei https://doaj.org/article/6a6b097c0dac4ec68f7e132a2023c0ef kostenfrei https://www.frontiersin.org/articles/10.3389/fmed.2023.1116354/full kostenfrei https://doaj.org/toc/2296-858X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023
language	English
source	In Frontiers in Medicine 10(2023) volume:10 year:2023
sourceStr	In Frontiers in Medicine 10(2023) volume:10 year:2023
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	invasive disease events breast cancer explainable AI 10-year follow up 5-year follow up Medicine (General)
isfreeaccess_bool	true
container_title	Frontiers in Medicine
authorswithroles_txt_mv	Raffaella Massafra @@aut@@ Annarita Fanizzi @@aut@@ Nicola Amoroso @@aut@@ Samantha Bove @@aut@@ Maria Colomba Comes @@aut@@ Domenico Pomarico @@aut@@ Vittorio Didonna @@aut@@ Sergio Diotaiuti @@aut@@ Luisa Galati @@aut@@ Francesco Giotta @@aut@@ Daniele La Forgia @@aut@@ Agnese Latorre @@aut@@ Angela Lombardi @@aut@@ Annalisa Nardone @@aut@@ Maria Irene Pastena @@aut@@ Cosmo Maurizio Ressa @@aut@@ Lucia Rinaldi @@aut@@ Pasquale Tamborra @@aut@@ Alfredo Zito @@aut@@ Angelo Virgilio Paradiso @@aut@@ Roberto Bellotti @@aut@@ Vito Lorusso @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	789482991
id	DOAJ08119496X
language_de	englisch
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callnumber-first	R - Medicine
author	Raffaella Massafra
spellingShingle	Raffaella Massafra misc R5-920 misc invasive disease events misc breast cancer misc explainable AI misc 10-year follow up misc 5-year follow up misc Medicine (General) Analyzing breast cancer invasive disease event classification through explainable artificial intelligence
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topic_title	R5-920 Analyzing breast cancer invasive disease event classification through explainable artificial intelligence invasive disease events breast cancer explainable AI 10-year follow up 5-year follow up
topic	misc R5-920 misc invasive disease events misc breast cancer misc explainable AI misc 10-year follow up misc 5-year follow up misc Medicine (General)
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title	Analyzing breast cancer invasive disease event classification through explainable artificial intelligence
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title_full	Analyzing breast cancer invasive disease event classification through explainable artificial intelligence
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author_browse	Raffaella Massafra Annarita Fanizzi Nicola Amoroso Samantha Bove Maria Colomba Comes Domenico Pomarico Vittorio Didonna Sergio Diotaiuti Luisa Galati Francesco Giotta Daniele La Forgia Agnese Latorre Angela Lombardi Annalisa Nardone Maria Irene Pastena Cosmo Maurizio Ressa Lucia Rinaldi Pasquale Tamborra Alfredo Zito Angelo Virgilio Paradiso Roberto Bellotti Vito Lorusso
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title_sort	analyzing breast cancer invasive disease event classification through explainable artificial intelligence
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title_auth	Analyzing breast cancer invasive disease event classification through explainable artificial intelligence
abstract	IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice
abstractGer	IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice
abstract_unstemmed	IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice
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title_short	Analyzing breast cancer invasive disease event classification through explainable artificial intelligence
url	https://doi.org/10.3389/fmed.2023.1116354 https://doaj.org/article/6a6b097c0dac4ec68f7e132a2023c0ef https://www.frontiersin.org/articles/10.3389/fmed.2023.1116354/full https://doaj.org/toc/2296-858X
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author2	Annarita Fanizzi Nicola Amoroso Samantha Bove Maria Colomba Comes Domenico Pomarico Vittorio Didonna Sergio Diotaiuti Luisa Galati Francesco Giotta Daniele La Forgia Agnese Latorre Angela Lombardi Annalisa Nardone Maria Irene Pastena Cosmo Maurizio Ressa Lucia Rinaldi Pasquale Tamborra Alfredo Zito Angelo Virgilio Paradiso Roberto Bellotti Vito Lorusso
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