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Lung Cancer Diagnosis and Treatment / Colorectal Cancer Surgical Treatments / Gastric Cancer Management and Outcomes · Journal article
World Journal of Surgical Oncology · September 5, 2026
A consensus or society position rather than new primary data.
This narrative framework review proposes a multidomain construct for surgical quality assessment in minimally invasive D2 lymphadenectomy that integrates process metrics, pathology metrics, and risk-adjusted clinical outcomes, adjusted for case- and context-dependent technical difficulty. The authors recommend a shift from isolated surrogate endpoints toward an auditable, difficulty-adjusted framework and identify AI as a potential enabler of scalable audit, provided that AI outputs undergo human confirmation, expert review, and external validation. Future validation requires stepwise progression from feasibility and reliability testing through prospective workflow evaluation and multicenter assessment of audit efficiency and benchmarking validity.
Narrative framework review. Literature addressing minimally invasive D2 lymphadenectomy for gastric cancer, technical difficulty assessment, surgical quality measurement, and AI-enabled quality audit.. Intervention: Proposed multidomain framework for quality assessment in minimally invasive D2 lymphadenectomy, incorporating process metrics, pathology metrics, and risk-adjusted clinical outcomes, with potential AI-enabled audit support..
Technical difficulty is conceptualized as shaped by anatomical complexity, vascular variation, therapy-altered tissue planes, visceral adiposity, operative platform, team workflow, and learning stage. Surgical quality is defined as a multidomain construct integrating process metrics, pathology metrics, and risk-adjusted clinical outcomes. Conventional endpoints (lymph node yield, margin status, operative time, blood loss, morbidity, survival) do not consistently capture intraoperative procedural fidelity, safety-critical deviations, or case complexity.
Conventional endpoints (lymph node yield, margin status, operative time, blood loss, morbidity, survival) do not consistently capture intraoperative procedural fidelity, safety-critical deviations, or case complexity.
Clinicians and surgical quality committees should recognize that conventional endpoints alone are insufficient for assessing D2 lymphadenectomy quality; a difficulty-adjusted, multidomain framework integrating process, pathology, and risk-adjusted outcomes is needed. AI tools may support audit efficiency but require human oversight, expert validation, and external testing before deployment in clinical governance.
A narrative framework review synthesizing evidence on technical difficulty, surgical quality assessment, and AI-enabled audit for minimally invasive D2 lymphadenectomy, proposing guidance for quality measurement and process review.
As stated by the source record.
Quoted from the source exactly as published.
Clinicians and surgical quality committees should recognize that conventional endpoints alone are insufficient for assessing D2 lymphadenectomy quality; a difficulty-adjusted, multidomain framework integrating process, pathology, and risk-adjusted outcomes is needed. AI tools may support audit efficiency but require human oversight, expert validation, and external testing before deployment in clinical governance.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.