You Hai-Sheng, Gao Chun-Xia, Wang Hai-Bin, Luo Sai-Sai, Chen Si-Ying, Dong Ya-Lin, Lyu Jun, Tian Tao
Department of Pharmacy, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
Hangzhou Cognitive N&T. Co., Ltd, Hangzhou, Zhengjiang, People's Republic of China.
Cancer Manag Res. 2020 Mar 16;12:1947-1958. doi: 10.2147/CMAR.S244932. eCollection 2020.
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.
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.
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.
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.
转移性非小细胞肺癌（mNSCLC）的疾病复杂性使得医生难以高效且准确地做出临床决策。人工智能的肿瘤学沃森（WFO）系统或许能通过提供快速且精准的治疗方案来帮助医生。本研究测量了WFO系统的医学治疗方案与实际临床方案的一致性，旨在确定WFO推荐方案对中国mNSCLC患者的适用性。
将mNSCLC患者的回顾性数据输入WFO，其生成一个治疗方案（WFO方案）。实际方案由医疗团队中的医生为患者制定（医疗团队方案）。通过单因素和多因素分析来分析影响这两种治疗方案一致性的因素。
mNSCLC患者的WFO方案与医疗团队方案的一致性率为85.16%。逻辑回归显示，两种治疗方案中不同的病理类型和基因突变的一致性存在显著差异。腺癌患者的“推荐”方案比例低于鳞状细胞癌患者。在表皮生长因子受体（EGFR）突变患者中，“不推荐”方案存在统计学显著差异，不一致率为18.75%。总之，在我们的治疗中心，WFO方案与医疗团队方案的一致性率为85.16%。不同的病理类型和不同的基因突变显著影响了两种治疗方案的一致率。
WFO推荐方案在我院对mNSCLC患者具有较高的适用性。本研究表明，有价值的WFO系统可能会更好地帮助医生在中国医疗环境中为mNSCLC患者确定准确有效的治疗方案。
