Assessment of the extent of lymphadenectomy in esophageal cancer surgery in the observational TIGER study: [TIGER-SQA] study protocol

Abstract

The distribution pattern of esophageal cancer lymph node metastases and consequently the lymph node stations that should be resected are currently being investigated in the international distribution of lymph node metastases in esophageal carcinoma (TIGER) study. This observational study has no specific entry criteria, resulting in significant variation among participating centers regarding the extent of lymphatic tissue clearance during surgery. To ensure reliable interpretation of the TIGER study results, this study aims to develop an intraoperative metric (surgical quality assessment tool) to examine the extent of lymphadenectomy. This multicenter prospective study assesses the extent of lymphadenectomy during esophagectomies performed within the TIGER study. After consensus on the applicability of surgical quality assessment in observational studies (phase 1), a short photo/video of the thoracic, abdominal, and [if applicable] cervical area will be captured after lymphadenectomy (phase 2). Those images will be rated by expert surgeons using a structured assessment tool (phase 3), and an automatic artificial intelligence (AI)-based quality assessment will be developed (phase 4). The variability in lymphadenectomy will be adjusted when analyzing TIGER study outcomes. Technique standardization by surgical quality assurance (SQA) reduces variation in trial outcomes. This cannot be applied to the TIGER study because of its observational nature, although surgical heterogeneity might influence TIGER study outcomes. To monitor surgical performance within observational studies, surgical quality assessment can be applied, although this has not been done yet. This study will ensure a reliable interpretation of TIGER study results and could be used as a benchmark for quality assessment in future surgical studies.

Keywords

Esophagectomy / lymph node metastases / lymphadenectomy / surgical variability / surgical quality assessment / artificial intelligence

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