Artificial intelligence-generated apparent diffusion coefficient (AI-ADC) maps for prostate gland assessment: a multi-reader study.
Authors
Affiliations (14)
Affiliations (14)
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA.
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA.
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
- Department of Radiology, Brigham & Women's Hospital, Boston, MA, USA.
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK.
- Division of Surgery & Interventional Science, University College London, London, UK.
- Department of Radiology, Singapore General Hospital, Singapore, Singapore.
- Weill Cornell Imaging, Cornell University, New York, NY, USA.
- Office of Information Technology, Center for Cancer Research, NCI, Bethesda, MD, USA.
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA.
- Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health, Bethesda, MD, USA.
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA. [email protected].
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA. [email protected].
Abstract
To compare the quality of AI-ADC maps and standard ADC maps in a multi-reader study. Multi-reader study included 74 consecutive patients (median age = 66 years, [IQR = 57.25-71.75 years]; median PSA = 4.30 ng/mL [IQR = 1.33-7.75 ng/mL]) with suspected or confirmed PCa, who underwent mpMRI between October 2023 and January 2024. The study was conducted in two rounds, separated by a 4-week wash-out period. In each round, four readers evaluated T2W-MRI and standard or AI-generated ADC (AI-ADC) maps. Fleiss' kappa, quadratic-weighted Cohen's kappa statistics were used to assess inter-reader agreement. Linear mixed effect models were employed to compare the quality evaluation of standard versus AI-ADC maps. AI-ADC maps exhibited significantly enhanced imaging quality compared to standard ADC maps with higher ratings in windowing ease (β = 0.67 [95% CI 0.30-1.04], p < 0.05), prostate boundary delineation (β = 1.38 [95% CI 1.03-1.73], p < 0.001), reductions in distortion (β = 1.68 [95% CI 1.30-2.05], p < 0.001), noise (β = 0.56 [95% CI 0.24-0.88], p < 0.001). AI-ADC maps reduced reacquisition requirements for all readers (β = 2.23 [95% CI 1.69-2.76], p < 0.001), supporting potential workflow efficiency gains. No differences were observed between AI-ADC and standard ADC maps' inter-reader agreement. Our multi-reader study demonstrated that AI-ADC maps improved prostate boundary delineation, had lower image noise, fewer distortions, and higher overall image quality compared to ADC maps. Question Can we synthesize apparent diffusion coefficient (ADC) maps with AI to achieve higher quality maps? Findings On average, readers rated quality factors of AI-ADC maps higher than ADC maps in 34.80% of cases, compared to 5.07% for ADC (p < 0.01). Clinical relevance AI-ADC maps may serve as a reliable diagnostic support tool thanks to their high quality, particularly when the acquired ADC maps include artifacts.