Impact of AI-Generated ADC Maps on Computer-Aided Diagnosis of Prostate Cancer: A Feasibility Study.
Authors
Affiliations (7)
Affiliations (7)
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.).
- Transitional Program, Sinai-Grace Hospital, Detroit Medical Center, Detroit, Michigan (E.C.Y.).
- Radiology and Biomedical Engineering Department, Northwestern University Feinberg School of Medicine, Chicago, IL 60611 (U.B.).
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland (M.J.M.).
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, Maryland (S.G., P.A.P.).
- Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health, Bethesda, Maryland (B.J.W.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland (B.J.W.).
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.); Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland (K.B.O., S.A.H., D.G.G., B.D.S., R.L., P.L.C., B.T.). Electronic address: [email protected].
Abstract
To evaluate the impact of AI-generated apparent diffusion coefficient (ADC) maps on diagnostic performance of a 3D U-Net AI model for prostate cancer (PCa) detection and segmentation at biparametric MRI (bpMRI). The study population was retrospectively collected and consisted of 178 patients, including 119 cases and 59 controls. Cases had a mean age of 62.1 years (SD=7.4) and a median prostate-specific antigen (PSA) level of 7.27ng/mL (IQR=5.43-10.55), while controls had a mean age of 63.4 years (SD=7.5) and a median PSA of 6.66ng/mL (IQR=4.29-11.30). All participants underwent 3.0 T T2-weighted turbo spin-echo MRI and high b-value echo-planar diffusion-weighted imaging (bpMRI), followed by either prostate biopsy or radical prostatectomy between January 2013 and December 2022. We compared the lesion detection and segmentation performance of a pretrained 3D U-Net AI model using conventional ADC maps versus AI-generated ADC maps. The Wilcoxon signed-rank test was used for statistical comparison, with 95% confidence intervals (CI) estimated via bootstrapping. A p-value <0.05 was considered significant. AI-ADC maps increased the accuracy of the lesion detection AI model, from 0.70 to 0.78 (p<0.01). Specificity increased from 0.22 to 0.47 (p<0.001), while maintaining high sensitivity, which was 0.94 with conventional ADC maps and 0.93 with AI-ADC maps (p>0.05). Mean dice similarity coefficients (DSC) for conventional ADC maps was 0.276, while AI-ADC maps showed a mean DSC of 0.225 (p<0.05). In the subset of patients with ISUP≥2, standard ADC maps demonstrated a mean DSC of 0.282 compared to 0.230 for AI-ADC maps (p<0.05). AI-generated ADC maps can improve performance of computer-aided diagnosis of prostate cancer.