Concordance Study Between IBM Watson for Oncology and Real Clinical Practice for Cervical Cancer Patients in China: A Retrospective Analysis.

Abstract:

:Watson for Oncology (WFO) is a artificial intelligence clinical decision-support system with evidence-based treatment options for oncologists. WFO has been gradually used in China, but limited reports on whether WFO is suitable for Chinese patients. This study aims to investigate the concordance of treatment options between WFO and real clinical practice for Cervical cancer patients retrospectively. We retrospectively enrolled 300 cases of cervical cancer patients. WFO provides treatment options for 246 supported cases. Real clinical practice were defined as concordant if treatment options were designated "recommended" or "for consideration" by WFO. Concordance of treatment option between WFO and real clinical practice was analyzed statistically. The treatment concordance between WFO and real clinical practice occurred in 72.8% (179/246) of cervical cancer cases. Logistic regression analysis showed that rural registration residences, advanced age, poor ECOG performance status, stages II-IV disease have a remarkable impact on consistency. The main reasons attributed to the 27.2% (67/246) of the discordant cases were the substitution of nedaplatin for cisplatin, reimbursement plan of bevacizumab, surgical preference, and absence of neoadjuvant/adjuvant chemotherapy and PD-1/PD-L1 antibodies recommendations. WFO recommendations were in 72.8% of concordant with real clinical practice for cervical cancer patients in China. However, several localization and individual factors limit its wider application. So, WFO could be an essential tool but it cannot currently replace oncologists. To be rapidly and fully apply to cervical cancer patients in China, accelerate localization and improvement were needed for WFO.

journal_name

Front Genet

journal_title

Frontiers in genetics

authors

Zou FW,Tang YF,Liu CY,Ma JA,Hu CH

doi

10.3389/fgene.2020.00200

subject

Has Abstract

pub_date

2020-03-24 00:00:00

pages

200

issn

1664-8021

journal_volume

11

pub_type

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