Artificial Intelligence in Decision-Making for Colorectal Cancer Treatment Strategy: An Observational Study of Implementing Watson for Oncology in a 250-Case Cohort

Batuer Aikemu; Pei Xue; Hiju Hong; Hongtao Jia; Chenxing Wang; Shuchun Li; Ling Huang; Xiaoyi Ding; Huan Zhang; Gang Cai; Aiguo Lu; Li Xie; Hao Li; Minhua Zheng; Jing Sun

doi:10.3389/fonc.2020.594182

Artificial Intelligence in Decision-Making for Colorectal Cancer Treatment Strategy: An Observational Study of Implementing Watson for Oncology in a 250-Case Cohort

Front Oncol. 2021 Feb 4:10:594182. doi: 10.3389/fonc.2020.594182. eCollection 2020.

Authors

Batuer Aikemu^{1

2}, Pei Xue^{1

2}, Hiju Hong^{1

2}, Hongtao Jia^{1

2}, Chenxing Wang^{1

2}, Shuchun Li^{1

2}, Ling Huang^{1

2}, Xiaoyi Ding³, Huan Zhang³, Gang Cai⁴, Aiguo Lu^{1

2}, Li Xie⁵, Hao Li⁶, Minhua Zheng^{1

2}, Jing Sun^{1

2}

Affiliations

¹ Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
³ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁵ Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶ Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Background: Personalized and novel evidence-based clinical treatment strategy consulting for colorectal cancer has been available through various artificial intelligence (AI) supporting systems such as Watson for Oncology (WFO) from IBM. However, the potential effects of this supporting tool in cancer care have not been thoroughly explored in real-world studies. This research aims to investigate the concordance between treatment recommendations for colorectal cancer patients made by WFO and a multidisciplinary team (MDT) at a major comprehensive gastrointestinal cancer center.

Methods: In this prospective study, both WFO and the blinded MDT's treatment recommendations were provided concurrently for enrolled colorectal cancers of stages II to IV between March 2017 and January 2018 at Shanghai Minimally Invasive Surgery Center. Concordance was achieved if the cancer team's decisions were listed in the "recommended" or "for consideration" classification in WFO. A review was carried out after 100 cases for all non-concordant patients to explain the inconsistency, and corresponding feedback was given to WFO's database. The concordance of the subsequent cases was analyzed to evaluate both the performance and learning ability of WFO.

Results: Overall, 250 patients met the inclusion criteria and were recruited in the study. Eighty-one were diagnosed with colon cancer and 189 with rectal cancer. The concordances for colon cancer, rectal cancer, or overall were all 91%. The overall rates were 83, 94, and 88% in subgroups of stages II, III, and IV. When categorized by treatment strategy, concordances were 97, 93, 89, 87, and 100% for neoadjuvant, surgery, adjuvant, first line, and second line treatment groups, respectively. After analyzing the main factors causing discordance, relative updates were made in the database accordingly, which led to the concordance curve rising in most groups compared with the initial rates.

Conclusion: Clinical recommendations made by WFO and the cancer team were highly matched for colorectal cancer. Patient age, cancer stage, and the consideration of previous therapy details had a significant influence on concordance. Addressing these perspectives will facilitate the use of the cancer decision-support systems to help oncologists achieve the promise of precision medicine.

Keywords: Watson for Oncology; artificial intelligence; colorectal cancer; concordance analysis; multidisciplinary team.