1. A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists
- Author
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Labus, S., Altmann, M.M., Huisman, H.J., Tong, A., Penzkofer, T., Choi, M.H., Shabunin, I., Winkel, D.J., Xing, P., Szolar, D.H., Shea, S.M., Grimm, R., Busch, H., Kamen, A., Herold, T., Baumann, C., Labus, S., Altmann, M.M., Huisman, H.J., Tong, A., Penzkofer, T., Choi, M.H., Shabunin, I., Winkel, D.J., Xing, P., Szolar, D.H., Shea, S.M., Grimm, R., Busch, H., Kamen, A., Herold, T., and Baumann, C.
- Abstract
Item does not contain fulltext, OBJECTIVES: To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. METHODS: In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. RESULTS: In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG >/= 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG >/= 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG >/= 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG >/= 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). CONCLUSIONS: DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. KEY POINTS: * DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesio
- Published
- 2023