Artificial intelligence–generated apparent diffusion coefficient (AI-ADC) maps for prostate gland assessment: a multi-reader study

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Abstract

Objective: To compare the quality of AI-ADC maps and standard ADC maps in a multi-reader study. Materials and methods: 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. Results: 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. Conclusion: 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. Key Points: 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.

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Ozyoruk, K. B., Harmon, S. A., Yilmaz, E. C., Huang, E. P., Gelikman, D. G., Gaur, S., … Turkbey, B. (2026). Artificial intelligence–generated apparent diffusion coefficient (AI-ADC) maps for prostate gland assessment: a multi-reader study. European Radiology, 36(1), 288–295. https://doi.org/10.1007/s00330-025-11871-z

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