Kim Min-Seok, Park Ha-Young, Kho Bo-Gun, Park Cheol-Kyu, Oh In-Jae, Kim Young-Chul, Kim Seok, Yun Ju-Sik, Song Sang-Yun, Na Kook-Joo, Jeong Jae-Uk, Yoon Mee Sun, Ahn Sung-Ja, Yoo Su Woong, Kang Sae-Ryung, Kwon Seong Young, Bom Hee-Seung, Jang Woo-Youl, Kim In-Young, Lee Jong-Eun, Jeong Won-Gi, Kim Yun-Hyeon, Lee Taebum, Choi Yoo-Duk
Lung and Esophageal Cancer Clinic, Chonnam National University, Hwasun Hospital, Hwasun, Republic of Korea.
Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea.
Transl Lung Cancer Res. 2020 Jun;9(3):507-514. doi: 10.21037/tlcr.2020.04.11.
IBM Watson for Oncology (WFO) is a cognitive computing system helping physicians quickly identify key information in a patient's medical record, surface relevant evidence, and explore treatment options. This study assessed the possibility of using WFO for clinical treatment in lung cancer patients.
We evaluated the level of agreement between WFO and multidisciplinary team (MDT) for lung cancer. From January to December 2018, newly diagnosed lung cancer cases in Chonnam National University Hwasun Hospital were retrospectively examined using WFO version 18.4 according to four treatment categories (surgery, radiotherapy, chemoradiotherapy, and palliative care). Treatment recommendations were considered concordant if the MDT recommendations were designated 'recommended' by WFO. Concordance between MDT and WFO was analyzed by Cohen's kappa value.
In total, 405 (male 340, female 65) cases with different histology (adenocarcinoma 157, squamous cell carcinoma 132, small cell carcinoma 94, others 22 cases) were enrolled. Concordance between MDT and WFO occurred in 92.4% (k=0.881, P<0.001) of all cases, and concordance differed according to clinical stages. The strength of agreement was very good in stage IV non-small cell lung carcinoma (NSCLC) (100%, k=1.000) and extensive disease small cell lung carcinoma (SCLC) (100%, k=1.000). In stage I NSCLC, the agreement strength was good (92.4%, k=0.855). The concordance was moderate in stage III NSCLC (80.8%, k=0.622) and relatively low in stage II NSCLC (83.3%, k=0.556) and limited disease SCLC (84.6%, k=0.435). There were discordant cases in surgery (7/57, 12.3%), radiotherapy (2/12, 16.7%), and chemoradiotherapy (15/129, 11.6%), but no discordance in metastatic disease patients.
Treatment recommendations made by WFO and MDT were highly concordant for lung cancer cases especially in metastatic stage. However, WFO was just an assisting tool in stage I-III NSCLC and limited disease SCLC; so, patient-doctor relationship and shared decision making may be more important in this stage.
IBM Watson for Oncology(WFO)是一种认知计算系统,可帮助医生快速识别患者病历中的关键信息,呈现相关证据并探索治疗方案。本研究评估了将WFO用于肺癌患者临床治疗的可能性。
我们评估了WFO与肺癌多学科团队(MDT)之间的一致性水平。2018年1月至12月,对全南国立大学和顺医院新诊断的肺癌病例,根据四种治疗类别(手术、放疗、放化疗和姑息治疗),使用18.4版WFO进行回顾性检查。如果MDT的建议被WFO指定为“推荐”,则治疗建议被视为一致。通过Cohen's kappa值分析MDT与WFO之间的一致性。
共纳入405例(男性340例,女性65例)不同组织学类型的病例(腺癌157例、鳞状细胞癌132例、小细胞癌94例、其他22例)。MDT与WFO之间的一致性在所有病例中占92.4%(k = 0.881,P < 0.001),且一致性根据临床分期有所不同。在IV期非小细胞肺癌(NSCLC)(100%,k = 1.000)和广泛期小细胞肺癌(SCLC)(100%,k = 1.000)中,一致性强度非常好。在I期NSCLC中,一致性强度良好(92.4%,k = 0.855)。在III期NSCLC中一致性为中等(80.8%,k = 0.622),在II期NSCLC(83.3%,k = 0.556)和局限期SCLC(84.6%,k = 0.435)中相对较低。在手术(7/57,12.3%)、放疗(2/12,16.7%)和放化疗(15/129,11.6%)中有不一致的病例,但在转移性疾病患者中没有不一致的情况。
WFO和MDT给出的治疗建议在肺癌病例中高度一致,尤其是在转移期。然而,在I - III期NSCLC和局限期SCLC中,WFO只是一个辅助工具;因此,在这个阶段医患关系和共同决策可能更为重要。