| Literature DB >> 36138331 |
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.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