Assessing quality in minimally invasive D2 lymphadenectomy: from technical difficulty to AI-enabled audit

Minimally invasive D2 lymphadenectomy for gastric cancer is technically demanding, and its quality varies across patients, surgeons, platforms, and institutions. Conventional endpoints, including lymph node yield, margin status, operative time, blood loss, postoperative morbidity, and survival, remain essential but do not consistently capture intraoperative procedural fidelity, safety-critical deviations, or case complexity. This narrative framework review synthesized evidence from MEDLINE, Embase, and Web of Science from inception to May 10, 2026, focusing on technical difficulty, surgical quality assessment, pathology-centered oncologic adequacy, risk-adjusted outcomes, and artificial intelligence (AI)-enabled audit. Technical difficulty was conceptualized as a case- and context-dependent risk-adjustment layer shaped by anatomical complexity, vascular variation, therapy-altered tissue planes, visceral adiposity, operative platform, team workflow, and learning stage. Surgical quality was defined as a multidomain construct integrating process metrics, pathology metrics, and risk-adjusted clinical outcomes. The proposed framework provides audit-oriented guidance for identifying a simplified minimum dataset, prioritizing high-risk D2 segments for selective process review, and interpreting process, pathology, and outcome indicators together after adjustment for technical difficulty. AI may support scalable audit through video indexing, phase and step recognition, extraction of high-risk operative segments, assisted event logging, and structured feedback. However, AI outputs should be treated as candidate measurement signals requiring human confirmation, expert surgical review, pathology-based assessment, external validation, governance, and post-deployment monitoring. Future validation should proceed stepwise, from feasibility testing and inter-rater reliability assessment to prospective workflow evaluation and multicenter assessment of audit efficiency, benchmarking validity, and process- or patient-level outcomes. Quality assessment in minimally invasive D2 lymphadenectomy should shift from isolated surrogate endpoints toward an auditable, difficulty-adjusted framework that makes “D2 achieved” more measurable, reviewable, and clinically meaningful.

Authors

Institutions

Publication Details

Journal
World Journal of Surgical Oncology
Published
2026-09-05
DOI
https://doi.org/10.1186/s12957-026-04540-y
Primary Topic
Gastric Cancer Management and Outcomes
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Assessing quality in minimally invasive D2 lymphadenectomy: from technical difficulty to AI-enabled audit

Xiaoye Liu, Xiaoyun Dai, Xi Wang, Jie Yin et al.
World Journal of Surgical Oncology
Gastric Cancer Management and Outcomes
article

Assessing quality in minimally invasive D2 lymphadenectomy: from technical difficulty to AI-enabled audit

Xiaoye Liu, Xiaoyun Dai, Xi Wang, Jie Yin, Jun Zhang, Zhi Zheng, Haiqiao Zhang, Yasheng Xue
article en

Abstract

Minimally invasive D2 lymphadenectomy for gastric cancer is technically demanding, and its quality varies across patients, surgeons, platforms, and institutions. Conventional endpoints, including lymph node yield, margin status, operative time, blood loss, postoperative morbidity, and survival, remain essential but do not consistently capture intraoperative procedural fidelity, safety-critical deviations, or case complexity. This narrative framework review synthesized evidence from MEDLINE, Embase, and Web of Science from inception to May 10, 2026, focusing on technical difficulty, surgical quality assessment, pathology-centered oncologic adequacy, risk-adjusted outcomes, and artificial intelligence (AI)-enabled audit. Technical difficulty was conceptualized as a case- and context-dependent risk-adjustment layer shaped by anatomical complexity, vascular variation, therapy-altered tissue planes, visceral adiposity, operative platform, team workflow, and learning stage. Surgical quality was defined as a multidomain construct integrating process metrics, pathology metrics, and risk-adjusted clinical outcomes. The proposed framework provides audit-oriented guidance for identifying a simplified minimum dataset, prioritizing high-risk D2 segments for selective process review, and interpreting process, pathology, and outcome indicators together after adjustment for technical difficulty. AI may support scalable audit through video indexing, phase and step recognition, extraction of high-risk operative segments, assisted event logging, and structured feedback. However, AI outputs should be treated as candidate measurement signals requiring human confirmation, expert surgical review, pathology-based assessment, external validation, governance, and post-deployment monitoring. Future validation should proceed stepwise, from feasibility testing and inter-rater reliability assessment to prospective workflow evaluation and multicenter assessment of audit efficiency, benchmarking validity, and process- or patient-level outcomes. Quality assessment in minimally invasive D2 lymphadenectomy should shift from isolated surrogate endpoints toward an auditable, difficulty-adjusted framework that makes “D2 achieved” more measurable, reviewable, and clinically meaningful.

World Journal of Surgical Oncology
Capital Medical University (CN), National Clinical Research Center for Digestive Diseases (CN), Institut de Recherche en Santé Digestive (FR), Beijing Friendship Hospital (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 11%
Gastric Cancer Management and Outcomes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.