Effect of an Artificial Intelligence Clinical Decision Support System on Treatment Decisions for Complex Breast Cancer.


Journal

JCO clinical cancer informatics
ISSN: 2473-4276
Titre abrégé: JCO Clin Cancer Inform
Pays: United States
ID NLM: 101708809

Informations de publication

Date de publication:
09 2020
Historique:
entrez: 24 9 2020
pubmed: 25 9 2020
medline: 1 9 2021
Statut: ppublish

Résumé

To examine the impact of a clinical decision support system (CDSS) on breast cancer treatment decisions and adherence to National Comprehensive Cancer Center (NCCN) guidelines. A cross-sectional observational study was conducted involving 1,977 patients at high risk for recurrent or metastatic breast cancer from the Chinese Society of Clinical Oncology. Ten oncologists provided blinded treatment recommendations for an average of 198 patients before and after viewing therapeutic options offered by the CDSS. Univariable and bivariable analyses of treatment changes were performed, and multivariable logistic regressions were estimated to examine the effects of physician experience (years), patient age, and receptor subtype/TNM stage. Treatment decisions changed in 105 (5%) of 1,977 patients and were concentrated in those with hormone receptor (HR)-positive disease or stage IV disease in the first-line therapy setting (73% and 58%, respectively). Logistic regressions showed that decision changes were more likely in those with HR-positive cancer (odds ratio [OR], 1.58; Use of an artificial intelligence-based CDSS had a significant impact on treatment decisions and NCCN guideline adherence in HR-positive breast cancers. Although cases of stage IV disease in the first-line therapy setting were also more likely to be changed, the effect was not statistically significant (

Identifiants

pubmed: 32970484
doi: 10.1200/CCI.20.00018
pmc: PMC7529515
doi:

Types de publication

Journal Article Observational Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

824-838

Références

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Auteurs

Fengrui Xu (F)

Department of Breast Cancer, Academy of Military Medical Sciences, Beijing, People's Republic of China.

Martín-J Sepúlveda (MJ)

IBM Research, Yorktown Heights, NY.

Zefei Jiang (Z)

Department of Breast Cancer, Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China.

Haibo Wang (H)

Department of Breast Cancer Center, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.

Jianbin Li (J)

Department of Breast Cancer, Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China.

Zhenzhen Liu (Z)

Department of Breast Cancer Center, Henan Cancer Hospital, Zhengzhou, People's Republic of China.

Yongmei Yin (Y)

Department of Breast Cancer, Jiangsu Province Hospital, Nanjing, People's Republic of China.

Edward H Shortliffe (EH)

Department of Biomedical Informatics, Columbia University, New York, NY.

Min Yan (M)

Department of Breast Cancer Center, Henan Cancer Hospital, Zhengzhou, People's Republic of China.

Yuhua Song (Y)

Department of Breast Cancer Center, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.

Cuizhi Geng (C)

Department of Breast Surgery, Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.

Jinhai Tang (J)

Department of Breast Cancer, Jiangsu Province Hospital, Nanjing, People's Republic of China.

Anita M Preininger (AM)

IBM Watson Health, Cambridge, MA.

Kyu Rhee (K)

IBM Watson Health, Cambridge, MA.

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Classifications MeSH