4.6 Article

Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction

Journal

JOURNAL OF THORACIC ONCOLOGY
Volume 14, Issue 12, Pages 2164-2175

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.jtho.2019.08.004

Keywords

Real-world data; Machine learning; Artificial intelligence; Survival analysis

Funding

  1. International Society for Diseases of the Esophagus (ISDE)
  2. Daniel and Karen Lee Chair in Thoracic Surgery at Cleveland Clinic
  3. Drs. Sidney and Becca Fleischer Heart and Vascular Education Chair
  4. 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
  5. NIH Roadmap for Medical Research [UL1TR000439]
  6. National Institute of General Medical Sciences [R01GM125072]
  7. Gus P. Karos Registry Fund at Cleveland Clinic

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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. (C) 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

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