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Surgical excision margins: a pathologist's perspective.
1Division of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, USA.
Advances in Anatomic Pathology
|May 26, 1999
Summary
Surgical margin assessment in squamous cell carcinomas of the upper aerodigestive tract is crucial but often unsatisfactory. This review critiques current methods and explores future directions for improved recurrence prediction and patient outcomes.
Area of Science:
- Oncologic Pathology
- Surgical Oncology
- Head and Neck Cancer Research
Background:
- Histopathologic assessment of surgical margins is a key prognostic factor for malignant neoplasms.
- The predictive accuracy of current margin assessments for recurrence and prognosis remains suboptimal.
- Squamous cell carcinomas of the upper aerodigestive tracts have been extensively studied regarding margin significance.
Purpose of the Study:
- To critique current applications and clinical implications of surgical margins in upper aerodigestive tract squamous cell carcinomas.
- To evaluate the impact of margin status on recurrence and patient outcomes.
- To explore novel approaches, including molecular markers, for margin assessment.
Main Methods:
- Review of existing literature on surgical margin assessment in head and neck squamous cell carcinomas.
- Analysis of factors influencing margin measurements, including postremoval artifacts.
- Discussion of specific clinical scenarios: conservation surgery, bone invasion, and molecular markers.
Main Results:
- Current margin assessment methods have limitations in predicting recurrence and prognosis.
- Postremoval artifacts can affect margin measurements.
- Molecular markers like p53 and eIF4E show potential for enhanced margin evaluation.
Conclusions:
- Rethinking current surgical margin assessment strategies is necessary for improved patient care in upper aerodigestive tract cancers.
- Further research into molecular markers and standardized measurement techniques is warranted.
- Optimized margin analysis can lead to better prediction of recurrence and patient outcomes.