TNM-Accountable Whole-Body 3-Dimensional Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Report Drafting in Lung Cancer Cohorts via Structured Impressions and Organ-wise Exemplar Synthesis

F-FDG PET/CT) is central to lung cancer staging, yet report drafting remains time intensive and error sensitive, as a single mislocalized finding can alter TNM stage and treatment decisions. We cast PET/CT report drafting as TNM-accountable decision support and propose RIDE, a 2-stage dual-modality 3-dimensional framework that separates TNM-critical anchoring from narrative completion. Stage I generates TNM-oriented structured impressions from paired PET and CT volumes; stage II performs hierarchical organ-wise exemplar retrieval and prompts a large language model to synthesize complete Findings and Impression conditioned on these inspectable artifacts. We curate a multicenter whole-body FDG PET/CT cohort of 1,583 patients from 3 hospitals, all referred for suspected or confirmed lung cancer, and evaluate it on a held-out test set of 520 cases, including 276 external cases from 2 independent institutions. RIDE achieves the strongest overall drafting performance and cross-site generalization, improving a clinically grounded competency metric by +20.4 on the external set and maintaining high clinician ratings under shift (mean Likert 4.00 internal; 4.17 external) while also delivering the best TNM staging performance among evaluated baselines. These findings support workflow-decomposed, TNM-accountable PET/CT drafting as a promising decision-support approach for human verification, and prospective studies will be valuable for further characterizing its utility in real-world clinical workflows.