Literature DB >> 36138331

Watson for oncology decision system for treatment consistency study in breast cancer.

Yaobang Liu1, Xingfa Huo2, Qi Li3, Yishuang Li4, Guoshuang Shen2, Miaozhou Wang2, Dengfeng Ren2, Fuxing Zhao2, Zhen Liu2, Jiuda Zhao5, Xinlan Liu6.   

Abstract

The Watson for Oncology (WFO) decision system has been rolled out in many cancers. However, the consistency of treatment for breast cancer is still unclear in relatively economically disadvantaged areas. Patients with postoperative adjuvant stage (January 2017 to December 2017) and advanced-stage breast cancer (January 2014 to December 2018) in northwest of China were included in this study. Patient information was imported to make treatment decisions using Watson version 19.20 analysis and subsequently compared with clinician decisions and analyzed for influencing factors. A total of 263 patients with postoperative adjuvant breast cancer and 200 with advanced breast cancer were included in this study. The overall treatment modality for WFO was in 80.2% and 50.5% agreement with clinicians in the adjuvant and advanced-stage population, respectively. In adjuvant treatment after breast cancer surgery, menopausal status (odds ratio (OR) = 2.89, P = 0.012, 95% CI, 1.260-6.630), histological grade (OR = 0.22, P = 0.019, 95% CI, 0.061-0.781) and tumor stage (OR = 0.22, P = 0.042, 95% CI, 0.050-0.943) were independent factors affecting the concordance between the two stages. In the first-line treatment of advanced breast cancer, hormone receptor status was a factor influencing the consistency of treatment (χ2 = 14.728, P < 0.001). There was good agreement between the WFOs and clinicians' treatment decisions in postoperative adjuvant breast cancer, but poor agreement was observed in patients with advanced breast cancer.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Entities:  

Keywords:  Artificial intelligence; Breast cancer; Concordance; Watson for oncology

Year:  2022        PMID: 36138331     DOI: 10.1007/s10238-022-00896-z

Source DB:  PubMed          Journal:  Clin Exp Med        ISSN: 1591-8890            Impact factor:   5.057


  3 in total

1.  Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.

Authors:  Hyuna Sung; Jacques Ferlay; Rebecca L Siegel; Mathieu Laversanne; Isabelle Soerjomataram; Ahmedin Jemal; Freddie Bray
Journal:  CA Cancer J Clin       Date:  2021-02-04       Impact factor: 508.702

Review 2.  Breast cancer.

Authors:  Sibylle Loibl; Philip Poortmans; Monica Morrow; Carsten Denkert; Giuseppe Curigliano
Journal:  Lancet       Date:  2021-04-01       Impact factor: 79.321

3.  A new era of oncology through artificial intelligence.

Authors:  Alessandra Curioni-Fontecedro
Journal:  ESMO Open       Date:  2017-05-15
  3 in total

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