TY - JOUR
T1 - Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction
AU - Worldwide Esophageal Cancer Collaboration Investigators
AU - Rice, Thomas W.
AU - Lu, Min
AU - Ishwaran, Hemant
AU - Blackstone, Eugene H.
N1 - Funding Information:
This work was supported by the International Society for Diseases of the Esophagus (ISDE); the Daniel and Karen Lee Chair in Thoracic Surgery at Cleveland Clinic (TWR); the Drs. Sidney and Becca Fleischer Heart and Vascular Education Chair (EHB); Clinical and Translational Science Collaborative at the Case Western Reserve University School of Medicine from the National Institutes of Health (NIH) National Center for Advancing Translational Sciences and NIH Roadmap for Medical Research (grant number UL1TR000439); National Institute of General Medical Sciences (grant number R01GM125072 (HI)); and the Gus P. Karos Registry Fund at Cleveland Clinic. These funding sources played no role in the collection, analysis, or interpretation of data; writing of the report; or decision to submit the article for publication. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the ISDE, Cleveland Clinic, or NIH. The authors thank Marie Semple, MPH, for statistical programming, Carolyn Apperson-Hansen, MStat, for assistance with Institutional Review Board and data-use agreements, and Gina Ventre, MFA, and Tess Parry, BS, for manuscript editing.
Funding Information:
This work was supported by the International Society for Diseases of the Esophagus (ISDE); the Daniel and Karen Lee Chair in Thoracic Surgery at Cleveland Clinic (TWR); the Drs. Sidney and Becca Fleischer Heart and Vascular Education Chair (EHB); Clinical and Translational Science Collaborative at the Case Western Reserve University School of Medicine from the National Institutes of Health (NIH) National Center for Advancing Translational Sciences and NIH Roadmap for Medical Research (grant number UL1TR000439 ); National Institute of General Medical Sciences (grant number R01GM125072 (HI)); and the Gus P. Karos Registry Fund at Cleveland Clinic . These funding sources played no role in the collection, analysis, or interpretation of data; writing of the report; or decision to submit the article for publication. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the ISDE, Cleveland Clinic, or NIH.
PY - 2019/12
Y1 - 2019/12
N2 - Introduction: To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for individual patients with adenocarcinoma of the esophagus and esophagogastric junction—information not available from randomized trials—a machine-learning analysis was performed using worldwide real-world data on patients undergoing different therapies for this rare adenocarcinoma. Methods: Using random forest technology in a sequential analysis, we (1) identified eligibility for each of four therapies among 13,365 patients: esophagectomy alone (n = 6649), neoadjuvant therapy (n = 4706), esophagectomy and adjuvant therapy (n = 998), and neoadjuvant and adjuvant therapy (n = 1022); (2) performed survival analyses incorporating interactions of patient and cancer characteristics with therapy; (3) determined optimal therapy as that predicted to maximize lifetime within 10 years (restricted mean survival time; RMST) for each patient; and (4) compared lifetime gained from optimal versus actual therapies. Results: Actual therapy was optimal in 61% of those receiving esophagectomy alone; neoadjuvant therapy was optimal for 36% receiving neoadjuvant therapy. Many patients were predicted to benefit from postoperative adjuvant therapy. Total RMST for actual therapy received was 58,825 years. Had patients received optimal therapy, total RMST was predicted to be 62,982 years, a 7% gain. Conclusions: Average treatment effect for adenocarcinoma of the esophagus yields only crude evidence-based therapy guidelines. However, patient response to therapy is widely variable, and survival after data-driven predicted optimal therapy often differs from actual therapy received. Therapy must address an individual patient's cancer and clinical characteristics to provide precision surgical therapy for adenocarcinoma of the esophagus and esophagogastric junction.
AB - Introduction: To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for individual patients with adenocarcinoma of the esophagus and esophagogastric junction—information not available from randomized trials—a machine-learning analysis was performed using worldwide real-world data on patients undergoing different therapies for this rare adenocarcinoma. Methods: Using random forest technology in a sequential analysis, we (1) identified eligibility for each of four therapies among 13,365 patients: esophagectomy alone (n = 6649), neoadjuvant therapy (n = 4706), esophagectomy and adjuvant therapy (n = 998), and neoadjuvant and adjuvant therapy (n = 1022); (2) performed survival analyses incorporating interactions of patient and cancer characteristics with therapy; (3) determined optimal therapy as that predicted to maximize lifetime within 10 years (restricted mean survival time; RMST) for each patient; and (4) compared lifetime gained from optimal versus actual therapies. Results: Actual therapy was optimal in 61% of those receiving esophagectomy alone; neoadjuvant therapy was optimal for 36% receiving neoadjuvant therapy. Many patients were predicted to benefit from postoperative adjuvant therapy. Total RMST for actual therapy received was 58,825 years. Had patients received optimal therapy, total RMST was predicted to be 62,982 years, a 7% gain. Conclusions: Average treatment effect for adenocarcinoma of the esophagus yields only crude evidence-based therapy guidelines. However, patient response to therapy is widely variable, and survival after data-driven predicted optimal therapy often differs from actual therapy received. Therapy must address an individual patient's cancer and clinical characteristics to provide precision surgical therapy for adenocarcinoma of the esophagus and esophagogastric junction.
KW - Artificial intelligence
KW - Machine learning
KW - Real-world data
KW - Survival analysis
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U2 - 10.1016/j.jtho.2019.08.004
DO - 10.1016/j.jtho.2019.08.004
M3 - Article
C2 - 31442498
AN - SCOPUS:85073067603
VL - 14
SP - 2164
EP - 2175
JO - Journal of Thoracic Oncology
JF - Journal of Thoracic Oncology
SN - 1556-0864
IS - 12
ER -