Introduction:
To facilitate initial clinical decision 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 4 therapies among 13,365 patients:
esophagectomy alone (n=6,649), neoadjuvant therapy (n=4,706), esophagectomy
and adjuvant therapy (n=998), and neoadjuvant and adjuvant therapy
(n=1,022); 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.