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Published on: May 10, 2021
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Is There an Optimal Definition for a Positive Circumferential Resection Margin in Locally Advanced Esophageal Cancer?
B Brac1, C Dufour2,3, H Behal4
1Department of Digestive and Oncological Surgery, University of Lille, Claude Huriez University Hospital, Lille, France.
Annals of Surgical Oncology
|September 13, 2021
Summary
The College of American Pathologists (CAP) definition of a positive circumferential resection margin (CRM) is more accurate for predicting esophageal cancer survival than the Royal College of Pathologists (RCP) definition. New CRM cutoffs were identified based on histologic type.
Area of Science:
- Oncology
- Surgical Pathology
- Cancer Prognostics
Background:
- Two definitions for positive circumferential resection margin (CRM) in esophageal cancer exist: College of American Pathologists (CAP) at 0 mm and Royal College of Pathologists (RCP) at ≤1 mm.
- The prognostic significance of these definitions and the potential for refined cutoffs require evaluation.
Purpose of the Study:
- To assess the prognostic value of both CAP and RCP definitions of positive CRM in esophageal cancer.
- To identify novel CRM cutoff values for improved survival prediction based on histologic type.
Main Methods:
- Retrospective analysis of 283 patients with locally advanced esophageal cancer undergoing curative resection (2007-2016).
- Reassessment of CRM using an ocular micrometer.
- Overall survival (OS) and disease-free survival (DFS) analyzed using univariate and multivariate models.
Main Results:
- Positive CRM by both CAP (HR 2.26) and RCP (HR 1.42) definitions was linked to poorer OS.
- The CAP definition (0 mm) showed superior predictive accuracy for OS and DFS compared to the RCP definition (≤1 mm).
- Optimal new CRM cutoffs identified: 100 µm for squamous cell carcinoma and 200 µm for adenocarcinoma.
Conclusions:
- The CAP definition of CRM is more accurate for predicting prognosis and recurrence in esophageal cancer.
- Novel, histology-specific CRM cutoff values can enhance survival prediction.

