A retrospective evaluation of lung cancer screening cases found that deep learning automated segmentation and 3D volumetric nodule analysis improved inter-observer consistency compared with manual readings. Using nnU-Net v2-based segmentation pipelines, investigators reported intraclass correlation coefficients above 0.90 for dimensional metrics across reader pairs. While baseline numerical reliability was already strong, semiautomated measurements showed narrower 95% limits of agreement and reduced systematic bias versus manual approaches. The work also targeted Lung-RADS category concordance, addressing clinically important discrepancies that can alter downstream surveillance and workup. For diagnostic AI developers and radiology departments, the study supports practical deployment: improving reproducibility for screening workflow decisions without increasing management ambiguity.