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Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board

BACKGROUND: IBM Watson for Oncology (WFO) is a cognitive computing system helping physicians quickly identify key information in a patient’s medical record, surface relevant evidence, and explore treatment options. This study assessed the possibility of using WFO for clinical treatment in lung cance...

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Autores principales: Kim, Min-Seok, Park, Ha-Young, Kho, Bo-Gun, Park, Cheol-Kyu, Oh, In-Jae, Kim, Young-Chul, Kim, Seok, Yun, Ju-Sik, Song, Sang-Yun, Na, Kook-Joo, Jeong, Jae-Uk, Yoon, Mee Sun, Ahn, Sung-Ja, Yoo, Su Woong, Kang, Sae-Ryung, Kwon, Seong Young, Bom, Hee-Seung, Jang, Woo-Youl, Kim, In-Young, Lee, Jong-Eun, Jeong, Won-Gi, Kim, Yun-Hyeon, Lee, Taebum, Choi, Yoo-Duk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354125/
https://www.ncbi.nlm.nih.gov/pubmed/32676314
http://dx.doi.org/10.21037/tlcr.2020.04.11
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author Kim, Min-Seok
Park, Ha-Young
Kho, Bo-Gun
Park, Cheol-Kyu
Oh, In-Jae
Kim, Young-Chul
Kim, Seok
Yun, Ju-Sik
Song, Sang-Yun
Na, Kook-Joo
Jeong, Jae-Uk
Yoon, Mee Sun
Ahn, Sung-Ja
Yoo, Su Woong
Kang, Sae-Ryung
Kwon, Seong Young
Bom, Hee-Seung
Jang, Woo-Youl
Kim, In-Young
Lee, Jong-Eun
Jeong, Won-Gi
Kim, Yun-Hyeon
Lee, Taebum
Choi, Yoo-Duk
author_facet Kim, Min-Seok
Park, Ha-Young
Kho, Bo-Gun
Park, Cheol-Kyu
Oh, In-Jae
Kim, Young-Chul
Kim, Seok
Yun, Ju-Sik
Song, Sang-Yun
Na, Kook-Joo
Jeong, Jae-Uk
Yoon, Mee Sun
Ahn, Sung-Ja
Yoo, Su Woong
Kang, Sae-Ryung
Kwon, Seong Young
Bom, Hee-Seung
Jang, Woo-Youl
Kim, In-Young
Lee, Jong-Eun
Jeong, Won-Gi
Kim, Yun-Hyeon
Lee, Taebum
Choi, Yoo-Duk
author_sort Kim, Min-Seok
collection PubMed
description BACKGROUND: IBM Watson for Oncology (WFO) is a cognitive computing system helping physicians quickly identify key information in a patient’s medical record, surface relevant evidence, and explore treatment options. This study assessed the possibility of using WFO for clinical treatment in lung cancer patients. METHODS: We evaluated the level of agreement between WFO and multidisciplinary team (MDT) for lung cancer. From January to December 2018, newly diagnosed lung cancer cases in Chonnam National University Hwasun Hospital were retrospectively examined using WFO version 18.4 according to four treatment categories (surgery, radiotherapy, chemoradiotherapy, and palliative care). Treatment recommendations were considered concordant if the MDT recommendations were designated ‘recommended’ by WFO. Concordance between MDT and WFO was analyzed by Cohen’s kappa value. RESULTS: In total, 405 (male 340, female 65) cases with different histology (adenocarcinoma 157, squamous cell carcinoma 132, small cell carcinoma 94, others 22 cases) were enrolled. Concordance between MDT and WFO occurred in 92.4% (k=0.881, P<0.001) of all cases, and concordance differed according to clinical stages. The strength of agreement was very good in stage IV non-small cell lung carcinoma (NSCLC) (100%, k=1.000) and extensive disease small cell lung carcinoma (SCLC) (100%, k=1.000). In stage I NSCLC, the agreement strength was good (92.4%, k=0.855). The concordance was moderate in stage III NSCLC (80.8%, k=0.622) and relatively low in stage II NSCLC (83.3%, k=0.556) and limited disease SCLC (84.6%, k=0.435). There were discordant cases in surgery (7/57, 12.3%), radiotherapy (2/12, 16.7%), and chemoradiotherapy (15/129, 11.6%), but no discordance in metastatic disease patients. CONCLUSIONS: Treatment recommendations made by WFO and MDT were highly concordant for lung cancer cases especially in metastatic stage. However, WFO was just an assisting tool in stage I–III NSCLC and limited disease SCLC; so, patient-doctor relationship and shared decision making may be more important in this stage.
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spelling pubmed-73541252020-07-15 Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board Kim, Min-Seok Park, Ha-Young Kho, Bo-Gun Park, Cheol-Kyu Oh, In-Jae Kim, Young-Chul Kim, Seok Yun, Ju-Sik Song, Sang-Yun Na, Kook-Joo Jeong, Jae-Uk Yoon, Mee Sun Ahn, Sung-Ja Yoo, Su Woong Kang, Sae-Ryung Kwon, Seong Young Bom, Hee-Seung Jang, Woo-Youl Kim, In-Young Lee, Jong-Eun Jeong, Won-Gi Kim, Yun-Hyeon Lee, Taebum Choi, Yoo-Duk Transl Lung Cancer Res Original Article BACKGROUND: IBM Watson for Oncology (WFO) is a cognitive computing system helping physicians quickly identify key information in a patient’s medical record, surface relevant evidence, and explore treatment options. This study assessed the possibility of using WFO for clinical treatment in lung cancer patients. METHODS: We evaluated the level of agreement between WFO and multidisciplinary team (MDT) for lung cancer. From January to December 2018, newly diagnosed lung cancer cases in Chonnam National University Hwasun Hospital were retrospectively examined using WFO version 18.4 according to four treatment categories (surgery, radiotherapy, chemoradiotherapy, and palliative care). Treatment recommendations were considered concordant if the MDT recommendations were designated ‘recommended’ by WFO. Concordance between MDT and WFO was analyzed by Cohen’s kappa value. RESULTS: In total, 405 (male 340, female 65) cases with different histology (adenocarcinoma 157, squamous cell carcinoma 132, small cell carcinoma 94, others 22 cases) were enrolled. Concordance between MDT and WFO occurred in 92.4% (k=0.881, P<0.001) of all cases, and concordance differed according to clinical stages. The strength of agreement was very good in stage IV non-small cell lung carcinoma (NSCLC) (100%, k=1.000) and extensive disease small cell lung carcinoma (SCLC) (100%, k=1.000). In stage I NSCLC, the agreement strength was good (92.4%, k=0.855). The concordance was moderate in stage III NSCLC (80.8%, k=0.622) and relatively low in stage II NSCLC (83.3%, k=0.556) and limited disease SCLC (84.6%, k=0.435). There were discordant cases in surgery (7/57, 12.3%), radiotherapy (2/12, 16.7%), and chemoradiotherapy (15/129, 11.6%), but no discordance in metastatic disease patients. CONCLUSIONS: Treatment recommendations made by WFO and MDT were highly concordant for lung cancer cases especially in metastatic stage. However, WFO was just an assisting tool in stage I–III NSCLC and limited disease SCLC; so, patient-doctor relationship and shared decision making may be more important in this stage. AME Publishing Company 2020-06 /pmc/articles/PMC7354125/ /pubmed/32676314 http://dx.doi.org/10.21037/tlcr.2020.04.11 Text en 2020 Translational Lung Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Kim, Min-Seok
Park, Ha-Young
Kho, Bo-Gun
Park, Cheol-Kyu
Oh, In-Jae
Kim, Young-Chul
Kim, Seok
Yun, Ju-Sik
Song, Sang-Yun
Na, Kook-Joo
Jeong, Jae-Uk
Yoon, Mee Sun
Ahn, Sung-Ja
Yoo, Su Woong
Kang, Sae-Ryung
Kwon, Seong Young
Bom, Hee-Seung
Jang, Woo-Youl
Kim, In-Young
Lee, Jong-Eun
Jeong, Won-Gi
Kim, Yun-Hyeon
Lee, Taebum
Choi, Yoo-Duk
Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
title Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
title_full Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
title_fullStr Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
title_full_unstemmed Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
title_short Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
title_sort artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354125/
https://www.ncbi.nlm.nih.gov/pubmed/32676314
http://dx.doi.org/10.21037/tlcr.2020.04.11
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