261 Subcarinal Lymph Node Dissection in Two Stage Oesophagectomy - Can We Adopt a Tailored Approach?
Year: 2025
Type: article
Study design: Observational Study
Abstract: Aim The role of dissecting specific lymph node groups during oesophagectomy remains controversial. Subcarinal lymph node metastasis is reported in 8-10% of patients. Dissection has potential for significant morbidity due surrounding anatomy and risk of bleeding. This study aims to evaluate the rate of subcarinal lymph node metastasis in patients undergoing two stage oesophagectomy, and the ability of preoperative FDG PET scans to predict this outcome. Method Clinical, radiological and pathological data was collected for all patients who underwent two stage oesophagectomy between 2018-2024. Patients were identified from a prospectively maintained database. Exclusion criteria included patients that underwent oesophagectomy for benign or premalignant disease, and those with missing PET scan or histology data. Fishers exact test was used to analyse categorical variables. Results 319 patients were identified from our database. Preoperative PET scans identified FDG avid subcarinal lymph nodes in 3.8% of patients. Subcarinal lymph node metastasis was found in 6.8% of patients that underwent dissection. Sensitivity and specificity of PET scan in predicting subcarinal node metastasis was 14.3% and 95.3% respectively. Positive predictive value was 18.2%. Negative predictive value was 93.8%. Negative PET scan was associated with lower chance of subcarinal lymph node metastasis (OR 0.30, 95% CI 0.06 – 1.53, p = 0.146) but was not statistically significant. Conclusions Rates of subcarinal lymph node metastasis in patients undergoing oesophagectomy are low. Routine dissection cannot be excluded based on PET scan alone. Further research regarding long term outcomes following subcarinal lymph node dissection is warranted.
Source: British journal of surgery
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7117483642
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