Li Jianbin, Yuan Yang, Bian Li, Lin Qiang, Yang Hua, Ma Li, Xin Ling, Li Feng, Zhang Shaohua, Wang Tao, Liu Yinhua, Jiang Zefei
Department of Oncology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China.
Department of Medical Molecular Biology, Beijing Institute of Biotechnology, Academy of Military Medical Sciences, Beijing.
Heliyon. 2023 May 5;9(5):e16059. doi: 10.1016/j.heliyon.2023.e16059. eCollection 2023 May.
We are building a clinical decision support system (CSCO AI) for breast cancer patients to improve the efficiency of clinical decision-making. We aimed to assess cancer treatment regimens given by CSCO AI and different levels of clinicians.
400 breast cancer patients were screened from the CSCO database. Clinicians with similar levels were randomly assigned one of the volumes (200 cases). CSCO AI was asked to assess all cases. Three reviewers were independently asked to evaluate the regimens from clinicians and CSCO AI. Regimens were masked before evaluation. The primary outcome was the proportion of high-level conformity (HLC).
The overall concordance between clinicians and CSCO AI was 73.9% (3621/4900). It was 78.8% (2757/3500) in the early-stage, higher than that in the metastatic stage (61.7% [864/1400], p < 0.001). The concordance was 90.7% (635/700) and 56.4% (395/700) in adjuvant radiotherapy and second-line therapy respectively. HLC in CSCO AI was 95.8% (95%CI:94.0%-97.6%), significantly higher than that in clinicians (90.8%, 95%CI:89.8%-91.8%). Considering professions, the HLC of surgeons was 85.9%, lower than that of CSCO AI (OR = 0.25,95%CI: 0.16-0.41). The most significant difference in HLC was in first-line therapy (OR = 0.06, 95%CI:0.01-0.41). When clinicians were divided according to their levels, there was no statistical significance between CSCO AI and higher level clinicians.
Decision from CSCO AI for breast cancer was superior than most clinicians did except in second-line therapy. The improvements in process outcomes suggest that CSCO AI can be widely used in clinical practice.
我们正在构建一个针对乳腺癌患者的临床决策支持系统（CSCO AI），以提高临床决策效率。我们旨在评估CSCO AI及不同级别临床医生给出的癌症治疗方案。
从CSCO数据库中筛选出400例乳腺癌患者。将水平相似的临床医生随机分配到其中一组病例（200例）。要求CSCO AI评估所有病例。独立邀请三位评审员评估临床医生和CSCO AI给出的治疗方案。评估前对治疗方案进行了盲法处理。主要结局指标是高度一致性（HLC）比例。
临床医生与CSCO AI之间的总体一致性为73.9%（3621/4900）。早期为78.8%（2757/3500），高于转移期（61.7%[864/1400]，p<0.001）。辅助放疗和二线治疗的一致性分别为90.7%（635/700）和56.4%（395/700）。CSCO AI的HLC为95.8%（95%CI：94.0%-97.6%），显著高于临床医生（90.8%，95%CI：89.8%-91.8%）。考虑职业因素，外科医生的HLC为85.9%，低于CSCO AI（OR=0.25，95%CI：0.16-0.41）。HLC差异最显著的是一线治疗（OR=0.06，95%CI：0.01-0.41）。当根据临床医生的级别进行划分时，CSCO AI与高级别临床医生之间无统计学差异。
除二线治疗外，CSCO AI针对乳腺癌做出的决策优于大多数临床医生。过程结局的改善表明CSCO AI可广泛应用于临床实践。
