Clinical indication–based CT scan range reduction: A scoping review of radiation dose savings and diagnostic accuracy

INTRODUCTION: Computed Tomography (CT) is central to modern imaging practice and accounts for over 60% of the collective radiation dose from medical imaging, underscoring the need for optimisation. Adjusting the scan range based on clinical indication is an evidence-based method for dose optimisation. However, the evidence remains dispersed across indications without a concise cross‑domain synthesis; this review consolidates evidence on indication-based scan range optimisation, evaluating its impact on radiation dose, diagnostic accuracy, and implications for missed findings across multiple CT applications. METHODS: This scoping review followed Joanna Briggs Institute methodology and PRISMA-ScR guidelines. PubMed, Embase, and Scopus were searched for studies (2010-2025) on indication-driven CT scan range optimisation. Eligible studies evaluated reduced z‑axis coverage based on clinical indications and reported at least one outcome: scan length (mm), radiation dose metrics (mSv), or diagnostic accuracy (%). Data were synthesised narratively with descriptive reporting of key outcomes. RESULTS: Twenty-one studies across six clinical domains were included. Indication-driven protocols reduced radiation exposure proportionally to scan length, achieving reductions of 33-50% in scan length and 10-50% in dose. Organ-specific reductions reached 97% for breast and 81% for testes, with foetal dose reductions exceeding 80% in pregnancy-adapted protocols. Diagnostic sensitivity for the primary indication remained 98-100%. Aggressive truncation increased missed alternative diagnoses in up to 17% of patients, particularly in older or multimorbid cohorts. CONCLUSION: Clinical indication-driven optimisation of CT scan range achieves substantial radiation-dose reduction without compromising diagnostic accuracy. These findings support evidence-based adoption of landmark-based protocols for focused clinical questions, with selective application in patients with broader differential diagnoses. Future directions include AI-assisted landmark detection and automated planning tools to enhance reproducibility and workflow efficiency.