Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI.

This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development. Model performance was assessed using Dice similarity coefficient (DSC) and Bland-Altman analysis. External validation in 20 patients from outside institutions demonstrated a DSC of 0.98 (IQR, 0.97-0.99) and a Bland-Altman difference of 2.6% (95% CI: 1.0%, 4.1%). Prospective validation in 53 patients demonstrated a DSC of 0.97 (IQR, 0.94-0.98) and a Bland-Altman difference of 3.6% (95% CI: 2.0%, 5.2%). Last, the efficiency of model-assisted annotation was evaluated on the first 50% of prospective cases (n = 28), with a 51% mean reduction in contouring time (P < .001), from 1724 seconds (95% CI: 1373, 2075) to 723 seconds (95% CI: 555, 892). In conclusion, our deployed artificial intelligence pipeline accurately performs automated segmentation for TKV estimation of polycystic kidneys and reduces expert contouring time. Keywords: Convolutional Neural Network (CNN), Segmentation, Kidney ClinicalTrials.gov identification no.: NCT00792155 Supplemental material is available for this article. © RSNA, 2022.

Detection of PKD1 and PKD2 Somatic Variants in Autosomal Dominant Polycystic Kidney Cyst Epithelial Cells by Whole-Genome Sequencing.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is a genetic disorder characterized by the development of multiple cysts in the kidneys. It is often caused by pathogenic mutations in PKD1 and PKD2 genes that encode polycystin proteins. Although the molecular mechanisms for cystogenesis are not established, concurrent inactivating germline and somatic mutations in PKD1 and PKD2 have been previously observed in renal tubular epithelium (RTE). METHODS: To further investigate the cellular recessive mechanism of cystogenesis in RTE, we conducted whole-genome DNA sequencing analysis to identify germline variants and somatic alterations in RTE of 90 unique kidney cysts obtained during nephrectomy from 24 unrelated participants. RESULTS: Kidney cysts were overall genomically stable, with low burdens of somatic short mutations or large-scale structural alterations. Pathogenic somatic "second hit" alterations disrupting PKD1 or PKD2 were identified in 93% of the cysts. Of these, 77% of cysts acquired short mutations in PKD1 or PKD2 ; specifically, 60% resulted in protein truncations (nonsense, frameshift, or splice site) and 17% caused non-truncating mutations (missense, in-frame insertions, or deletions). Another 18% of cysts acquired somatic chromosomal loss of heterozygosity (LOH) events encompassing PKD1 or PKD2 ranging from 2.6 to 81.3 Mb. 14% of these cysts harbored copy number neutral LOH events, while the other 3% had hemizygous chromosomal deletions. LOH events frequently occurred at chromosomal fragile sites, or in regions comprising chromosome microdeletion diseases/syndromes. Almost all somatic "second hit" alterations occurred at the same germline mutated PKD1/2 gene. CONCLUSIONS: These findings further support a cellular recessive mechanism for cystogenesis in ADPKD primarily caused by inactivating germline and somatic variants of PKD1 or PKD2 genes in kidney cyst epithelium.

Hemorrhagic Cysts and Other MR Biomarkers for Predicting Renal Dysfunction Progression in Autosomal Dominant Polycystic Kidney Disease.

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.