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Lymph node assessment technique matters in radical cystectomy for bladder cancer
Matthew Chau1, Ivan Thia1, Pravin Viswambaram1,2,3
1Department of Urology, Fiona Stanley Hospital, Perth, Western Australia, Australia.
Background:
For patients undergoing radical cystectomy with pelvic lymph node dissection for urothelial cancer, a lymph node count of at least 16 is associated with improved cancer-specific and overall survival. Lymph node yield is presumed to relate directly to extent of dissection and surgical quality, however limited studies have reviewed the impact of the pathological assessment process of lymph nodes on lymph node yield.
Method:
A retrospective assessment of 139 patients who had radical cystectomy for urothelial cancer between March 2015 and July 2021 from Fiona Stanley Hospital (Perth, Australia) by a single surgeon was assessed. A change in pathological assessment process from assessment of only palpable lymph nodes to microscopic assessment of the entire submitted specimens occurred in August 2018. Patients were divided into two groups accordingly and other relevant demographic and pathological data was recorded. The impact of pathological processing technique on lymph node yield was assessed using the Student T test and logistical regression was used to assess the impact of other demographic variables.
Results:
The mean lymph node yield was 16.2 nodes (IQR 12-23) in 54 patients in the pre-process change group compared to 22.4 nodes (IQR 15-28.4) in 85 patients in the post-process change group (P < 0.0001). 53.7% had 16 or more nodes in the pre-process change group compared to 71.3% in the post-process change group (P = 0.04). Age, BMI, and gender were not significant predictors of lymph node yield.
Conclusion:
The current study demonstrates that the microscopic assessment of all lymph node tissue detects significantly more lymph nodes than only examining palpably abnormal tissue. Pathologic assessment protocols should be standardized to this technique to ensure the utility of lymph node yield as a quality metric.
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