Literature DB >> 32130123

Concordance Between Watson for Oncology and a Multidisciplinary Clinical Decision-Making Team for Gastric Cancer and the Prognostic Implications: Retrospective Study.

Yulong Tian1, Xiaodong Liu1, Zixuan Wang2, Shougen Cao1, Zimin Liu3, Qinglian Ji4, Zequn Li1, Yuqi Sun1, Xin Zhou1, Daosheng Wang1, Yanbing Zhou1.   

Abstract

BACKGROUND: With the increasing number of cancer treatments, the emergence of multidisciplinary teams (MDTs) provides patients with personalized treatment options. In recent years, artificial intelligence (AI) has developed rapidly in the medical field. There has been a gradual tendency to replace traditional diagnosis and treatment with AI. IBM Watson for Oncology (WFO) has been proven to be useful for decision-making in breast cancer and lung cancer, but to date, research on gastric cancer is limited.
OBJECTIVE: This study compared the concordance of WFO with MDT and investigated the impact on patient prognosis.
METHODS: This study retrospectively analyzed eligible patients (N=235) with gastric cancer who were evaluated by an MDT, received corresponding recommended treatment, and underwent follow-up. Thereafter, physicians inputted the information of all patients into WFO manually, and the results were compared with the treatment programs recommended by the MDT. If the MDT treatment program was classified as "recommended" or "considered" by WFO, we considered the results concordant. All patients were divided into a concordant group and a nonconcordant group according to whether the WFO and MDT treatment programs were concordant. The prognoses of the two groups were analyzed.
RESULTS: The overall concordance of WFO and the MDT was 54.5% (128/235) in this study. The subgroup analysis found that concordance was less likely in patients with human epidermal growth factor receptor 2 (HER2)-positive tumors than in patients with HER2-negative tumors (P=.02). Age, Eastern Cooperative Oncology Group performance status, differentiation type, and clinical stage were not found to affect concordance. Among all patients, the survival time was significantly better in concordant patients than in nonconcordant patients (P<.001). Multivariate analysis revealed that concordance was an independent prognostic factor of overall survival in patients with gastric cancer (hazard ratio 0.312 [95% CI 0.187-0.521]).
CONCLUSIONS: The treatment recommendations made by WFO and the MDT were mostly concordant in gastric cancer patients. If the WFO options are updated to include local treatment programs, the concordance will greatly improve. The HER2 status of patients with gastric cancer had a strong effect on the likelihood of concordance. Generally, survival was better in concordant patients than in nonconcordant patients. ©Yulong Tian, Xiaodong Liu, Zixuan Wang, Shougen Cao, Zimin Liu, Qinglian Ji, Zequn Li, Yuqi Sun, Xin Zhou, Daosheng Wang, Yanbing Zhou. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.02.2020.

Entities:  

Keywords:  Watson for Oncology; artificial intelligence; concordance; gastric cancer; multidisciplinary team

Year:  2020        PMID: 32130123     DOI: 10.2196/14122

Source DB:  PubMed          Journal:  J Med Internet Res        ISSN: 1438-8871            Impact factor:   5.428


  6 in total

Review 1.  Strategies for Testing Intervention Matching Schemes in Cancer.

Authors:  Nicholas J Schork; Laura H Goetz; James Lowey; Jeffrey Trent
Journal:  Clin Pharmacol Ther       Date:  2020-07-24       Impact factor: 6.875

2.  Concordance Between Watson for Oncology and Multidisciplinary Teams in Colorectal Cancer: Prognostic Implications and Predicting Concordance.

Authors:  Chenchen Mao; Xinxin Yang; Ce Zhu; Jingxuan Xu; Yaojun Yu; Xian Shen; Yingpeng Huang
Journal:  Front Oncol       Date:  2020-12-23       Impact factor: 6.244

3.  Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity.

Authors:  Lotte Keikes; Milan Kos; Xander A A M Verbeek; Thijs Van Vegchel; Iris D Nagtegaal; Max J Lahaye; Alejandra Méndez Romero; Sandra De Bruijn; Henk M W Verheul; Heidi Rütten; Cornelis J A Punt; Pieter J Tanis; Martijn G H Van Oijen
Journal:  Int J Qual Health Care       Date:  2021-04-03       Impact factor: 2.038

4.  Physicians' Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle-Income Countries.

Authors:  Srinivas Emani; Angela Rui; Hermano Alexandre Lima Rocha; Rubina F Rizvi; Sergio Ferreira Juaçaba; Gretchen Purcell Jackson; David W Bates
Journal:  JMIR Cancer       Date:  2022-04-07

Review 5.  Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

Authors:  Chang Seok Bang; Jae Jun Lee; Gwang Ho Baik
Journal:  J Med Internet Res       Date:  2020-09-16       Impact factor: 5.428

6.  Artificial Intelligence in Decision-Making for Colorectal Cancer Treatment Strategy: An Observational Study of Implementing Watson for Oncology in a 250-Case Cohort.

Authors:  Batuer Aikemu; Pei Xue; Hiju Hong; Hongtao Jia; Chenxing Wang; Shuchun Li; Ling Huang; Xiaoyi Ding; Huan Zhang; Gang Cai; Aiguo Lu; Li Xie; Hao Li; Minhua Zheng; Jing Sun
Journal:  Front Oncol       Date:  2021-02-04       Impact factor: 6.244

  6 in total

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