The role of whole-lesion apparent diffusion coefficient analysis for predicting outcomes of prostate cancer patients on active surveillance

Purpose: To explore the role of whole-lesion apparent diffusion coefficient (ADC) analysis for predicting outcomes in prostate cancer patients on active surveillance. Methods: This study included 72 prostate cancer patients who underwent MRI–ultrasound fusion-targeted biopsy at the initiation of active surveillance, had a visible MRI lesion in the region of tumor on biopsy, and underwent 3T baseline and follow-up MRI examinations separated by at least one year. Thirty of the patients also underwent an additional MRI–ultrasound fusion-targeted biopsy after the follow-up MRI. Whole-lesion ADC metrics and lesion volumes were computed from 3D whole-lesion volumes-of-interest placed on lesions on the baseline and follow-up ADC maps. The percent change in lesion volume on the ADC map between the serial examinations was computed. Statistical analysis included unpaired t tests, ROC analysis, and Fisher’s exact test. Results: Baseline mean ADC, ADC_{0–10th-percentile}, ADC_{10–25th-percentile}, and ADC_{25–50th-percentile} were all significantly lower in lesions exhibiting ≥50% growth on the ADC map compared with remaining lesions (all P ≤ 0.007), with strongest difference between lesions with and without ≥50% growth observed for ADC_{0–10th-percentile} (585 ± 308 vs. 911 ± 336; P = 0.001). ADC_{0–10th-percentile} achieved highest performance for predicting ≥50% growth (AUC = 0.754). Mean percent change in tumor volume on the ADC map was 62.3% ± 26.9% in patients with GS ≥ 3 + 4 on follow-up biopsy compared with 3.6% ± 64.6% in remaining patients (P = 0.050). Conclusion: Our preliminary results suggest a role for 3D whole-lesion ADC analysis in prostate cancer active surveillance.