ArticleQuantitative imaging in medicine and surgery2026
Impact of CT slice thickness reduction algorithm on AI-based lung nodule detection in chest CTs of colorectal cancer patients.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This retrospective multicenter study evaluated whether deep learning-based super-resolution (SR) reconstruction can enhance structural conspicuity in thick-section chest computed tomography (CT) and improve the detection performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system for lung nodules in 96 patients with colorectal cancer (CRC) undergoing chest CT for metastatic surveillance. Pulmonary nodules <10 mm and ≤3 per patient were analyzed. Three image sets were evaluated using a commercial AI-CAD system: original thin-section images (reference), original 5-mm thick-section images, and SR-converted thin-section images. Nodule- and patient-level detection performances were compared using the Cochran-Mantel-Haenszel test and aligned rank transform analysis of variance (ART-ANOVA). Quantitative nodule metrics, image noise, and morphologic consistency were assessed between original and SR-converted thin-section images. Among 105 reference nodules, AI sensitivity increased from 31.4% with thick-section images to 61.0% with SR-converted images, and positive predictive value (PPV) increased from 42.9% to 80.0%. Patient-level sensitivity improved from 41.5% to 67.1%. SR reconstruction reduced image noise (P<0.001) and preserved nodule morphology, with 95.3% anatomic concordance and 80% solid-feature consistency. Nodule size remained comparable; however, density was lower in converted images. SR reconstruction generated 20% hallucinated nodules (16/80), predominantly benign or artifactual structures. In conclusion, SR reconstruction enhances AI-based pulmonary nodule detection by compensating for structural detail lost in thick-section CT. Despite hallucinated nodules, SR reconstruction may provide a feasible harmonization strategy for retrospective multicenter AI research using heterogeneous CT datasets, although further validation in larger populations is required.
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