Takahashi, Naoki

ABSTRACT Background Family history of pancreatic cancer (PC), pathogenic germline variants (PGVs), and increased intrapancreatic fat (IPF) are individually associated with increased PC risk. Whether IPF is elevated in high‐risk individuals (HRIs) with PC family history or PGVs is unknown. Although MRI is the preferred modality for IPF measurement, region‐of‐interest–based approaches are prone to sampling bias because pancreatic fat is heterogeneously distributed. We aimed to compare IPF among HRIs and matched non‐HRI controls using an Artificial intelligence (AI)‐based tool for whole pancreatic IPF measurement applied to magnetic resonance imaging (MRI). Methods We developed and validated a deep‐learning pipeline for IPF measurement that incorporates automated pancreas segmentation on T1‐weighted MRI images registered to the corresponding iterative decomposition of water and fat with echo asymmetry and least squares estimation images to compute IPF using proton density fat fraction (PDFF). This pipeline was used to quantify IPF and assess IPF distribution patterns in 85 HRIs and 170 non‐HRIs matched 1:2 for age, sex, BMI, and diabetes status. A surface erosion approach was developed to minimize the impact of peripancreatic fat on IPF measurement. Linear models adjusting for matching variables were used to assess associations. Results Model‐expert agreement for whole‐pancreas PDFF was excellent (intraclass correlation coefficient 0.99; 95% CI, 0.98–0.99 mean absolute error 1.4%). Mean PDFF did not differ between HRIs and controls (25.8% [11.5] vs. 26.9% [11.4]; p = 0.32). PDFF increased with age ( p = 0.015), male sex ( p < 0.001), presence of diabetes ( p = 0.009), BMI ( p < 0.001). IPF distribution patterns did not differ by risk group. Conclusion An AI‐based approach provides accurate, scalable, and objective assessment of IPF quantity and distribution. IPF quantity and spatial distribution do not appear to differ based on germline or familial predisposition to PC.

Abstract Purpose Validate the semiquantitative (SQ)-MRI score for chronic pancreatitis (CP) diagnosis in an independent, multicenter cohort. Methods We analyzed MRIs performed at baseline in 337 participants with acute (AP) or recurrent AP (RAP) and 67 with non-calcific chronic pancreatitis (NCCP) enrolled in the PROCEED study from 6/2017 to 3/2023. MRIs were performed at multiple clinical centers using site-specific clinical protocols and a variety of MRI vendors to evaluate the SQ-MRI score in a real-world setting. The SQ-MRI score was calculated using the T1 signal intensity ratio of the pancreas to the spleen (T1 score), arteriovenous enhancement ratio (AVR), and pancreatic body diameter (PBD). We adjusted for confounding by participant characteristics via propensity score weighting. Results Median AVR was higher in AP/RAP (1.18, IQR: 0.97–1.44) than in the NCCP group (1.03, IQR: 0.78–1.22, p = 0.007). Pancreatic body diameters were larger in AP/RAP than in the NCCP group − 7–14 mm (10% vs 25%), 14–20 mm (40% vs 54%), and > 20 mm (50% vs 21%), respectively ( p < 0.01). Median T1 score was higher in AP/RAP (1.18, IQR: 0.96–1.40) than in the NCCP group (1.04, IQR: 0.86–1.32, p = 0.10). Median SQ-MRI score in the AP/RAP group was significantly lower, 1.91 (IQR: 0.97, 2.95), than in the NCCP group, 2.74 (IQR: 2.04–4.03) ( p < 0.001). The propensity score-adjusted AUC for SQ-MRI to distinguish AP/RAP from NCCP was 0.64, which was higher than for individual parameters. Conclusion We have validated the SQ-MRI score in a multicenter cohort using an independent, real-world MRI dataset.

Abstract Purpose To assess if PSA density (PSAD) velocity is a predictor of clinically significant prostate cancer (csPCa). Methods Patients undergoing prostate MR for suspected csPCa between 2017 and 2023 were included. Of these, patients who underwent two or more MR exams within 0.3 to 2 years to each other and pathological confirmation within 1 year after the last MR were identified. PI-RADS score, patient age, screening or active surveillance (AS), PSA, PSAD, PSA-velocity and PSAD-velocity were assessed if they were predictor of csPCa. For univariate analysis, all variables were assessed using student t or chi-square tests. When variables were considered significant, variables were entered into multivariate logistic regression model. Results 23,244 MRs were performed (17,703 screening, 5,541AS). 1,174 patients (mean age: 65.9) met the inclusion criteria, and 550 patients (47%) were diagnosed with csPCa. On univariate analysis, PI-RADS score (p<.0001), age (67.0 vs 64.9, p<.0001), AS exam (67% vs 59%, p = 0.004), PSA (7.5 vs 6.6, p<.001 ), PSAD (0.16 vs 0.12, p<.0001) and PSAD-velocity (0.023 vs 0.012, p = 0.005 ) were predictors of csPCa but PSA-velocity (1.4 vs 1.2, p=.4) was not. On multivariate analysis, PI-RADS score (p<.0001), age (p<.0001), AS exam (p<.0001), PSA (p<.0001) and PSAD (p<.0001) remained significant predictors, but PSAD-velocity was not (p=.1). Conclusions Higher PSAD-velocity was associated with presence of csPCa on univariate analysis but not on multivariate analysis when combined with other predictors such PI-RADS score, age, and PSAD.

Imaging is integral to detection and characterization of complications after prostate interventions, including vascular, infectious, urinary, and radiation-related complications, and facilitates timely diagnosis and management.

ABSTRACT Background Clinical T 2 ‐weighted (T2W) MRI of prostate cancer (PCa) usually implements a 2D fast‐spin‐echo (FSE) sequence, but understanding the echo‐time‐dependent contrast behavior is not straightforward due to the complicated FSE signal evolution. Consequently, consensus‐based recommendations on the optimal effective echo time (TE eff ) to maximize contrast are lacking. Purpose To investigate the effect of TE eff on PCa lesion contrast in prostate T2W MRI. Study Type Phantom and in vivo prospective study. Subjects A standard MRI phantom and 53 prostate cancer patients (29 for image evaluation and 24 for quantitative lesion analysis). Field Strength and Sequence Multi‐slice T2W FSE sequence at 3 T acquired at multiple TE eff values. Assessment Three readers (13, 25, and 27 years' experience) evaluated TE eff = 100 ms versus 150 ms on diagnostic quality and reader preference. The effective T 2 relaxation rates ( T 2,eff ) were measured using FSE sequences in the phantom and used to predict the optimal TE eff maximizing T2W contrasts. From multi‐TE eff scans of subjects with suspected PCa, T 2,eff values of PCa lesions and healthy tissue were measured and used to calculate the optimal TE eff maximizing the lesion contrast. In vivo contrast metrics were measured as a function of TE eff . Statistical Tests Wilcoxon test of reader scores, with p < 0.05 defining statistical significance. Results No statistically significant difference was found in diagnostic quality between TE eff = 150 ms and 100 ms images, but all three readers significantly favored TE eff = 150 ms. Phantom experiments demonstrated that TE eff ‐dependent FSE signal progression can be characterized by T 2,eff values typically 40% longer than actual T 2 values, allowing accurate prediction of optimal TE eff within 2%–5%. Applied to PCa imaging, in vivo measurements indicated that effective T 2 relaxation times of the prostate are on average 1.5× longer than published T 2 values. Data Conclusion The benefits of TE eff = 150 ms versus 100 ms in prostate T2W FSE were supported by reader preference, phantom experiments, in vivo measurements, and analytic optimizations. Evidence Level 2. Technical Efficacy 2.

Importance Multiparametric magnetic resonance imaging (MRI), with or without prostate biopsy, has become the standard of care for diagnosing clinically significant prostate cancer. Resource capacity limits widespread adoption. Biparametric MRI, which omits the gadolinium contrast sequence, is a shorter and cheaper alternative offering time-saving capacity gains for health systems globally. Objective To assess whether biparametric MRI is noninferior to multiparametric MRI for diagnosis of clinically significant prostate cancer. Design, Setting, and Participants A prospective, multicenter, within-patient, noninferiority trial of biopsy-naive men from 22 centers (12 countries) with clinical suspicion of prostate cancer (elevated prostate-specific antigen [PSA] level and/or abnormal digital rectal examination findings) from April 2022 to September 2023, with the last follow-up conducted on December 3, 2024. Interventions Participants underwent multiparametric MRI, comprising T2-weighted, diffusion-weighted, and dynamic contrast–enhanced (DCE) sequences. Radiologists reported abbreviated biparametric MRI first (T2-weighted and diffusion-weighted), blinded to the DCE sequence. After unblinding, radiologists reported the full multiparametric MRI. Patients underwent a targeted biopsy with or without systematic biopsy if either biparametric MRI or multiparametric MRI was suggestive of clinically significant prostate cancer. Main outcomes and measures The primary outcome was the proportion of men with clinically significant prostate cancer. Secondary outcomes included the proportion of men with clinically insignificant cancer. The noninferiority margin was 5%. Results Of 555 men recruited, 490 were included for primary outcome analysis. Median age was 65 (IQR, 59-70) years and median PSA level was 5.6 (IQR, 4.4-8.0) ng/mL. The proportion of patients with abnormal digital rectal examination findings was 12.7%. Biparametric MRI was noninferior to multiparametric MRI, detecting clinically significant prostate cancer in 143 of 490 men (29.2%), compared with 145 of 490 men (29.6%) (difference, −0.4 [95% CI, −1.2 to 0.4] percentage points; P = .50). Biparametric MRI detected clinically insignificant cancer in 45 of 490 men (9.2%), compared with 47 of 490 men (9.6%) with the use of multiparametric MRI (difference, −0.4 [95% CI, −1.2 to 0.4] percentage points). Central quality control demonstrated that 99% of scans were of adequate diagnostic quality. Conclusion and relevance In men with suspected prostate cancer, provided image quality is adequate, an abbreviated biparametric MRI scan, with or without targeted biopsy, could become the new standard of care for prostate cancer diagnosis. With approximately 4 million prostate MRIs performed globally annually, adopting biparametric MRI could substantially increase scanner throughput and reduce costs worldwide. Trial registration ClinicalTrials.gov Identifier: NCT04571840

Abstract Purpose To evaluate the effectiveness of subcutaneous glucagon in reducing motion artifact during prostate MRI through intraindividual comparison. Methods At our institution patients undergoing a clinical prostate MRI exam receive 1 mg of subcutaneous glucagon before scanning. From February 15, 2024 to February 11, 2025 33 such patients were recruited to undergo an additional, research exam without glucagon. All exams were acquired at 3T. An axial T2-weighted spin-echo series (T2-WI) was acquired within both exams. Evaluation of the T2-WI series was done by three experienced radiologists using the criteria of diagnostic quality (0–3 scale), PI-QUALv2 (0–3 sum), motion artifact (significant, visible, none), and reviewer preference (five-point relative scale). Due to differences in prescribed coverage, the scan times for the two T2-WI sequences were in general different for each subject. Results were stratified using the acquisition time ratio (Trel) between the glucagon vs. non-glucagon scans. Wilcoxon tests assessed score differences. Results Across all 33 subjects, no significant differences were found between glucagon and non-glucagon scans. However, the observed negative correlation between glucagon preference and Trel (p = 0.026) led to stratification into low-Trel (n = 16) and high-Trel (n = 17) groups. In the low-Trel group the glucagon scans provided significantly improved diagnostic quality (p = 0.048), PI-QUALv2 sum (p = 0.049), motion scores (p = 0.047), and reader preference (p = 0.042). Conclusion Subcutaneous glucagon provides improved image quality in prostate T2-WI MRI when scan duration remains within 1.25× of that of a non-glucagon T2-WI series. The benefit appears to decrease with longer scan times.

Objectives: The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)–related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance. Methods: This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa. Seven clinical, 3 patient-level MRI-related, and 4 lesion-level MRI-related findings were extracted. After feature selection, 2 logistic regression models with and without lesions-level findings were created using data from facility I and II (development cohort). The area under the receiver operating characteristic curve (AUC) between the 2 models was compared in the PI-RADS ≥3 population in the development cohort and Facility III (validation cohort) using the Delong test. Interfacility differences of the selected predictive variables were evaluated using the Kruskal-Wallis test or chi-squared test. Results: Selected lesion-level features included the peripheral zone involvement and apparent diffusion coefficient (ADC) values. The model with lesions-level findings had significantly higher AUC than the model without in 655 examinations in the development cohort (0.81 vs 0.79, respectively, P = 0.005), but not in 553 examinations in the validation cohort (0.77 vs 0.76, respectively). Large interfacility differences were seen in the ADC distribution (P < 0.001) and csPCa proportion in PI-RADS 3–5 (P < 0.001). Conclusions: Adding lesions-level findings improved the csPCa discrimination in the development but not the validation cohort. Interfacility differences impeded model generalization, including the distribution of reported ADC values and PI-RADS score-level csPCa proportion.