Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.

objectivesTo evaluate the performance of a body mass index (BMI)-based sub-milliSievert low-dose CT (LDCT) protocol with multiple reconstruction algorithms for image quality and lung nodule assessment. MATERIALS AND

methodsThis prospective study included 214 participants who underwent standard-dose CT (SDCT, 3.68 ± 1.53 mSv) reconstructed with 50% adaptive statistical iterative reconstruction (ASIR-V-50%) and LDCT. LDCT was randomly divided into a higher-dose group (LD-A, 0.57-1.15 mSv, n = 108) and a lower-dose group (LD-B, 0.33-0.63 mSv, n = 106). Each group was stratified into four BMI-based subgroups with individualized protocols reconstructed with deep learning image reconstruction (DLIR-H and DLIR-M), ASIR-V-50%, and filtered back projection (FBP). Image quality, nodule detection across BMI subgroups, and the performance of four algorithms in detection, size measurement accuracy, and Lung-RADS v2022 consistency were analyzed.

resultsIn LDCT, DLIR-H provided superior image quality (p < 0.001) and the highest overall nodule detection rate (99.04%), surpassing ASIR-V-50% (98.55%) and FBP (97.87%) (both p < 0.05). The advantage was most evident for nodules < 6 mm, while all nodules ≥ 6 mm were consistently detected across algorithms. Detection rates showed no significant variation among BMI subgroups (all p > 0.05). For measurement accuracy, FBP and ASIR-V-50% performed better in LD-A (all p < 0.05), whereas DLIR-M was superior in LD-B (p < 0.001). All algorithms demonstrated excellent Lung-RADS agreement (κ > 0.9, p < 0.001).

conclusionA BMI-based sub-milliSievert LDCT protocol significantly reduced radiation exposure while maintaining nodule detection across BMI subgroups, with DLIR offering superior image quality and diagnostic performance. KEY POINTS: Question Evidence remains scarce on BMI-based sub-milliSievert low-dose CT using different reconstruction algorithms, regarding image quality and nodules detection (particularly < 6 mm). Findings BMI-based sub-milliSievert low-dose CT ensured balanced detectability across populations, while deep learning reconstruction improved image quality and achieved excellent sensitivity for lung nodule detection. Clinical relevance Deep learning reconstruction enhanced BMI-based sub-milliSievert low-dose CT, supporting its application in personalized sub-milliSievert low-dose lung cancer screening.