From feasibility to reliability in robotic pulmonary segmentectomy: a proposed quality-assurance framework

Anatomical segmentectomy has become an evidence-based option for selected patients with early-stage non-small cell lung cancer. Robotic platforms may facilitate dissection in anatomically complex cases, but technical feasibility does not ensure reproducible anatomical, oncological or perioperative quality. Current studies report heterogeneous definitions of segmentectomy, margins, nodal evaluation, conversion and learning, which limits comparison and programme-level improvement. This structured narrative review examines robotic pulmonary segmentectomy through a quality-assurance lens. We propose a five-stage framework: Define, Plan, Perform and Rescue, Verify, and Sustain. Define establishes indication, multidomain complexity and explicit quality targets. Plan integrates patient optimisation, three-dimensional anatomical reconstruction, tumour localisation, margin strategy and prespecified contingencies. Perform and Rescue links anatomical execution to early recognition of bleeding, misidentification, localisation failure and inadequate margins. Verify reconciles the intended resection with margin status, nodal evaluation, pathology and perioperative recovery data. Sustain extends reliability beyond the individual surgeon through team training, emergency simulation, structured outcome capture and risk-adjusted audit. Evidence is interpreted according to study design and directness, and neutral or negative comparative findings are retained. A minimum dataset is proposed for transparent reporting and multicentre testing. Robotic segmentectomy should be evaluated by the consistency with which teams deliver an anatomically complete, oncologically adequate and safely recoverable operation, rather than by completion through a robotic approach alone. The proposed framework and dataset are intended as testable quality-improvement tools. They require prospective validation and should not be interpreted as established credentialing standards.