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Background: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. Methods: Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test–retest reproducibility ADPKD patients. Results: Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test–retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. Conclusions: Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted. [ABSTRACT FROM AUTHOR]

Background: Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates. Purpose: To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1‐weighted, T2‐weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements. Study Type: Retrospective training, prospective testing. Subjects: Three hundred ninety‐seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility. Field Strength/Sequence: T2‐weighted single‐shot fast spin echo (T2), SSFP, and T1‐weighted 3D spoiled gradient echo (T1) at 1.5 and 3T. Assessment: 2D U‐net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model‐assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1–3 weeks to assess reproducibility. Manual and model‐assisted segmentation times were recorded. Statistical Tests: Bland–Altman, Schapiro–Wilk (normality assessment), Pearson's chi‐squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P‐value < 0.05 was considered statistically significant. Results: In 17 ADPKD subjects, model‐assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model‐assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model‐assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier. Data Conclusion: Measuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model‐assisted segmentations and can improve TKV measurement reproducibility more than 2‐fold in ADPKD. Evidence Level: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired. [ABSTRACT FROM AUTHOR]

Background: Screening for rapidly progressing autosomal dominant polycystic kidney disease (ADPKD) is necessary for assigning and monitoring therapies. Height-adjusted total kidney volume (ht-TKV) is an accepted biomarker for clinical prognostication, but represents only a small fraction of information on abdominal MRI.Purpose: To investigate the utility of other MR features of ADPKD to predict progression.Study Type: Single-center retrospective.Population: Longitudinal data from 186 ADPKD subjects with baseline serum creatinine, PKD gene testing, abdominal MRI measurements, and ≥2 follow-up serum creatinine were reviewed.Field Strength/sequence: 1.5T, T2 -weighted single-shot fast spin echo, T1 -weighted 3D spoiled gradient echo (liver accelerated volume acquisition) and 2D cine velocity encoded gradient echo (phase contrast MRA).Assessment: Ht-TKV, renal blood flow (RBF), number and fraction of renal and hepatic cysts, bright T1 hemorrhagic renal cysts, and liver and spleen volumes were independently assessed by three observers blinded to estimated glomerular filtration rate (eGFR) data.Statistical Tests: Linear mixed-effect models were applied to predict eGFR over time using MRI features at baseline adjusted for confounders. Validation was performed in 158 patients who had follow-up MRI using receiver operator characteristic, sensitivity, and specificity.Results: Hemorrhagic cysts, fraction of renal and hepatic cysts, height-adjusted liver and spleen volumes were significant independent predictors of future eGFR (final prediction model R2 = 0.88 P < 0.05). The number of hemorrhagic cysts significantly improved the prediction compared to ht-TKV in predicting future eGFR (area under the curve [AUC] = 0.94, 95% confidence interval [CI]: 0.9-0.94 vs. R2 = 0.9, 95% CI: 0.85-0.9, P = 0.045). For baseline eGFR ≥60 ml/min/1.73m2 , sensitivity for predicting eGFR<45 ml/min/1.73m2 by ht-TKV alone was 29%. Sensitivity increased to 72% with all MRI variables in the model (P < 0.05 = 0.019), whereas specificity was unchanged, 100% vs. 99%.Data Conclusion: Combining multiple MR features including hemorrhagic renal cysts, renal cyst fraction, liver and spleen volume, hepatic cyst fraction, and renal blood flow enhanced sensitivity for predicting eGFR decline in ADPKD compared to the standard model including only ht-TKV. Level of Evidence 2 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:564-576. [ABSTRACT FROM AUTHOR]

Background: Autosomal dominant polycystic kidney disease (ADPKD) can involve prostate and seminal vesicles but the potential interrelationship of these findings and associations with PKD gene mutation locus and type is unknown.Purpose: To determine the interrelationship of seminal megavesicles (seminal vesicles with lumen diameter > 10mm) and prostatic cysts in ADPKD and to determine whether there are associations with PKD gene mutations.Study Type: Retrospective, case control.Population: Male ADPKD subjects (n = 92) with mutations in PKD1 (n = 71; 77%) or PKD2 (n = 21; 23%), and age/gender-matched controls without ADPKD (n = 92).Field Strength/sequence: 1.5T, axial/coronal T2 -weighted MR images.Assessment: Reviewers blinded to genotype independently measured seminal vesicle lumen diameter and prevalence of cysts in prostate, kidney, and liver.Statistical Tests: Nonparametric tests for group comparisons and univariate and multivariable logistic regression analyses to identify associations of megavesicles and prostate median cysts with mutations and renal/hepatic cyst burden.Results: Seminal megavesicles were found in 23 of 92 ADPKD (25%) subjects with PKD1 (22/71, 31%) or PKD2 (n = 1/21, 5%) mutations, but in only two control subjects (P < 0.0001). Prostate median cysts were found in 17/92 (18%) ADPKD subjects, compared with only 6/92 (7%) controls (P = 0.01), and were correlated with seminal vesicle diameters (ρ = 0.24, P = 0.02). Nonmedian prostate cyst prevalence was identical between ADPKD and controls (7/92, 8%). After adjusting for age, estimated glomerular filtration rate, and height-adjusted total kidney volume, ADPKD subjects with megavesicles were 10 times more likely to have a PKD1 than a PKD2 mutation. Among PKD1 subjects, seminal megavesicles occurred more frequently with nontruncating mutations with less severe kidney involvement.Data Conclusion: ADPKD is associated with prostate median cysts near ejaculatory ducts. These cysts correlate with seminal megavesicles (dilated to >10 mm) which predict a 10-fold greater likelihood of PKD1 vs. PKD2 mutation. Cysts elsewhere in the prostate are not related to ADPKD.Level Of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:894-903. [ABSTRACT FROM AUTHOR]