Optimizing adjuvant treatment decisions for stage t2 rectal cancer based on mesorectal node size: a decision analysis.

RATIONALE AND OBJECTIVES

The aim of this study was to optimize treatment decisions for patients with suspected stage T2 rectal cancer on the basis of mesorectal lymph node size at magnetic resonance imaging.

MATERIALS AND METHODS

A decision-analytic model was developed to predict outcomes for patients with stage T2 rectal cancer at magnetic resonance imaging. Node-positive patients were assumed to benefit from chemoradiation prior to surgery. Imperfect magnetic resonance imaging performance for primary cancer and mesorectal nodal staging was incorporated. Five triage strategies were considered for administering preoperative chemoradiation: treat all patients; treat for any mesorectal node >3, >5, and >7 mm in size; and treat no patients. If nodal metastases or unsuspected stage T3 disease went untreated preoperatively, postoperative chemoradiation was needed, resulting in poorer outcomes. For each strategy, rates of acute and long-term chemoradiation toxicity and of 5-year local recurrence were computed. Effects of input parameter uncertainty were evaluated in sensitivity analysis.

RESULTS

The optimal strategy depended on the outcome prioritized. Acute and long-term chemoradiation toxicity rates were minimized by triaging only patients with nodes >7 mm to preoperative chemoradiation (18.9% and 10.8%, respectively). A treat-all strategy minimized the 5-year local recurrence rate (5.6%). A 7-mm nodal triage threshold increased the 5-year local recurrence rate to 8.0%; when no patients were treated preoperatively, the local recurrence rate was 10.1%. With improved primary tumor staging, all outcomes could be further optimized.

CONCLUSIONS

Mesorectal nodal size thresholds for preoperative chemoradiation should depend on the outcome prioritized: higher size thresholds reduce chemoradiation toxicity but increase recurrence rates. Improvements in nodal staging will have greater impact if primary tumor staging can be improved.