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Concordance of Treatment Recommendations for Metastatic Non-Small-Cell Lung Cancer Between Watson for Oncology System and Medical Team

Authors You HS, Gao CX, Wang HB, Luo SS, Chen SY, Dong YL, Lyu J, Tian T

Received 6 January 2020

Accepted for publication 26 February 2020

Published 16 March 2020 Volume 2020:12 Pages 1947—1958

DOI https://doi.org/10.2147/CMAR.S244932

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Chien-Feng Li


Hai-Sheng You,1,* Chun-Xia Gao,1,* Hai-Bin Wang,2 Sai-Sai Luo,1 Si-Ying Chen,1 Ya-Lin Dong,1 Jun Lyu,3 Tao Tian4

1Department of Pharmacy, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 2Hangzhou Cognitive N&T. Co., Ltd, Hangzhou, Zhengjiang, People’s Republic of China; 3Clinical Research Center, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 4Department of Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Tao Tian
Department of Oncology, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, Shaanxi 710061, People’s Republic of China
Tel +86-13572206784
Fax +86-29-85324086
Email tiantao0607@163.com
Jun Lyu
Clinical Research Center, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, Shaanxi 710061, People’s Republic of China
Tel +86 29 8532 3614
Fax +86 29 8532 3473
Email lujun2006@xjtu.edu.cn

Objective: The disease complexity of metastatic non-small-cell lung cancer (mNSCLC) makes it difficult for physicians to make clinical decisions efficiently and accurately. The Watson for Oncology (WFO) system of artificial intelligence might help physicians by providing fast and precise treatment regimens. This study measured the concordance of the medical treatment regimens of the WFO system and actual clinical regimens, with the aim of determining the suitability of WFO recommendations for Chinese patients with mNSCLC.
Methods: Retrospective data of mNSCLC patients were input to the WFO, which generated a treatment regimen (WFO regimen). The actual regimen was made by physicians in a medical team for patients (medical-team regimen). The factors influencing the consistency of the two treatment options were analyzed by univariate and multivariate analyses.
Results: The concordance rate was 85.16% between the WFO and medical-team regimens for mNSCLC patients. Logistic regression showed that the concordance differed significantly for various pathological types and gene mutations in two treatment regimens. Patients with adenocarcinoma had a lower rate of “recommended” regimen than those with squamous cell carcinoma. There was a statistically significant difference in EGFR-mutant patients for “not recommended” regimens with inconsistency rate of 18.75%. In conclusion, the WFO regimen has 85.16% consistency rate with medical-team regimen in our treatment center. The different pathological type and different gene mutation markedly influenced the agreement rate of the two treatment regimens.
Conclusion: WFO recommendations have high applicability to mNSCLC patients in our hospital. This study demonstrates that the valuable WFO system may assist the doctors better to determine the accurate and effective treatment regimens for mNSCLC patients in the Chinese medical setting.

Keywords: metastatic non-small-cell lung cancer, Watson for Oncology, concordance, artificial intelligence, treatment recommendations


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