A Deep Learning Algorithm for Liver Metastasis Detection at Contrast-enhanced Abdominal CT in Patients with Colorectal Cancer: A Comparative Study with Radiologists.

Purpose To evaluate the performance of a deep learning algorithm (DLA) for detecting liver metastases (LM) in patients with colorectal cancer (CRC) across diverse clinical contexts and compare its accuracy with that of radiologists. Materials and Methods This retrospective, bicentric study included patients with CRC who underwent contrast-enhanced abdominal CT between January 2019 and December 2021. The DLA accuracy was assessed at the per-nodule and per-patient levels and compared with that of a senior (R1) and an in-training (R2) radiologist blinded to each other's results. The LM detection and false detection rates and interreader agreement were determined. Results Among 181 patients with CRC (mean age, 64 years ± 13 [SD]; 102 male), 95 had LM and 86 had no LM. In the per-nodule analysis, the DLA LM detection rate was 81% (227 of 280; 95% CI: 76.1, 85.2), with no difference compared with R1 (79%; 222 of 280; 95% CI: 74.2, 83.6; = .49) or R2 (76%; 214 of 280; 95% CI: 71.1, 81.0; = .19). Detection rates of DLA increased with lesion size: less than 10 mm, 55% (51 of 93; 95% CI: 44.7, 64.6); 10-19 mm, 91% (96 of 106; 95% CI: 83.5, 94.8); and 20 mm or more, 99% (80 of 81; 95% CI: 93.3, 99.8). Detection of subcapsular LM was comparable across readers (DLA, 90% [113 of 125; 95% CI: 84.0, 94.4]; R1, 91% [114 of 125; 95% CI: 84.9, 95.0]; R2, 89% [111 of 125; 95% CI: 82.1, 93.2]). False detection rates were low (DLA, 22% [39 of 181; 95% CI: 16.2, 28.1]; R1, 20% [37 of 181; 95% CI: 15.2, 26.9]; R2, 26% [47 of 181; 95% CI: 20.1, 32.8]; DLA vs R1, = .004; DLA vs R2, = .01). DLA false positives were mainly biliary dilatations ( = 14) and diaphragmatic indentations ( = 12). Interreader agreement was moderate (κ = 0.63-0.75). Conclusion DLA performance did not differ from radiologists in detecting LM, with consistent results across lesion sizes and locations. Imaging Modality, Abdomen, Gastrointestinal, Liver, Oncology, Comparative Studies, Segmentation, Diagnosis, Deep Learning © RSNA, 2026.