Dataset Information

Automatic Measurement of Kidney and Liver Volumes from MR Images of Patients Affected by Autosomal Dominant Polycystic Kidney Disease.

ABSTRACT: BACKGROUND:The formation and growth of cysts in kidneys, and often liver, in autosomal dominant polycystic kidney disease (ADPKD) cause progressive increases in total kidney volume (TKV) and liver volume (TLV). Laborious and time-consuming manual tracing of kidneys and liver is the current gold standard. We developed a fully automated segmentation method for TKV and TLV measurement that uses a deep learning network optimized to perform semantic segmentation of kidneys and liver. METHODS:We used 80% of a set of 440 abdominal magnetic resonance images (T2-weighted HASTE coronal sequences) from patients with ADPKD to train the network and the remaining 20% for validation. Both kidneys and liver were also segmented manually. To evaluate the method's performance, we used an additional test set of images from 100 patients, 45 of whom were also involved in longitudinal analyses. RESULTS:TKV and TLV measured by the automated approach correlated highly with manually traced TKV and TLV (intraclass correlation coefficients, 0.998 and 0.996, respectively), with low bias and high precision (<0.1%±2.7% for TKV and -1.6%±3.1% for TLV); this was comparable with inter-reader variability of manual tracing (<0.1%±3.5% for TKV and -1.5%±4.8% for TLV). For longitudinal analysis, bias and precision were <0.1%±3.2% for TKV and 1.4%±2.9% for TLV growth. CONCLUSIONS:These findings demonstrate a fully automated segmentation method that measures TKV, TLV, and changes in these parameters as accurately as manual tracing. This technique may facilitate future studies in which automated and reproducible TKV and TLV measurements are needed to assess disease severity, disease progression, and treatment response.

SUBMITTER: van Gastel MDA

PROVIDER: S-EPMC6683702 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Publications

Automatic Measurement of Kidney and Liver Volumes from MR Images of Patients Affected by Autosomal Dominant Polycystic Kidney Disease.

van Gastel Maatje D A MDA Edwards Marie E ME Torres Vicente E VE Erickson Bradley J BJ Gansevoort Ron T RT Kline Timothy L TL

Journal of the American Society of Nephrology : JASN 20190703 8

<h4>Background</h4>The formation and growth of cysts in kidneys, and often liver, in autosomal dominant polycystic kidney disease (ADPKD) cause progressive increases in total kidney volume (TKV) and liver volume (TLV). Laborious and time-consuming manual tracing of kidneys and liver is the current gold standard. We developed a fully automated segmentation method for TKV and TLV measurement that uses a deep learning network optimized to perform semantic segmentation of kidneys and liver.<h4>Metho ...[more]

PMID: 31270136

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Project description:Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method.Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2-weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed.Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable.We have developed a fully automated method for segmentation of kidneys from abdominal magnetic resonance images in patients with autosomal dominant polycystic kidney disease with varying kidney volumes. The performance of the automated method was in good agreement with that of manual method.

Project description:BackgroundIn autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) is regarded as an important biomarker of disease progression and different methods are available to assess kidney volume. The purpose of this study was to identify the most efficient kidney volume computation method to be used in clinical studies evaluating the effectiveness of treatments on ADPKD progression.Methods and findingsWe measured single kidney volume (SKV) on two series of MR and CT images from clinical studies on ADPKD (experimental dataset) by two independent operators (expert and beginner), twice, using all of the available methods: polyline manual tracing (reference method), free-hand manual tracing, semi-automatic tracing, Stereology, Mid-slice and Ellipsoid method. Additionally, the expert operator also measured the kidney length. We compared different methods for reproducibility, accuracy, precision, and time required. In addition, we performed a validation study to evaluate the sensitivity of these methods to detect the between-treatment group difference in TKV change over one year, using MR images from a previous clinical study. Reproducibility was higher on CT than MR for all methods, being highest for manual and semiautomatic contouring methods (planimetry). On MR, planimetry showed highest accuracy and precision, while on CT accuracy and precision of both planimetry and Stereology methods were comparable. Mid-slice and Ellipsoid method, as well as kidney length were fast but provided only a rough estimate of kidney volume. The results of the validation study indicated that planimetry and Stereology allow using an importantly lower number of patients to detect changes in kidney volume induced by drug treatment as compared to other methods.ConclusionsPlanimetry should be preferred over fast and simplified methods for accurately monitoring ADPKD progression and assessing drug treatment effects. Expert operators, especially on MR images, are required for performing reliable estimation of kidney volume. The use of efficient TKV quantification methods considerably reduces the number of patients to enrol in clinical investigations, making them more feasible and significant.

Project description:BackgroundPain affects 60% of the autosomal dominant polycystic kidney disease (ADPKD) population. Despite being an early and debilitating symptom, it is poorly characterized and management is suboptimal. This study aimed to develop an ADPKD-specific pain assessment tool (APAT) to facilitate pain research.MethodsFollowing a systematic review of PATs used in ADPKD studies and against international recommendations for pain trials, our multi-disciplinary team of clinical experts and patients constructed an ADPKD-pain conceptual framework of key pain evaluation themes. We compiled a new APAT covering domains prioritized within our framework using components of questionnaires validated in other chronic pain disorders. The APAT was administered longitudinally within a randomized high-water intake trial (NCT02933268) to ascertain feasibility and provide pilot data on ADPKD pain.ResultsThirty-nine ADPKD participants with chronic kidney disease Stages 1-4 provided 129 APAT responses. Each participant completed a median of 3 (range 1-10) assessments. Respondents' mean ± standard deviation age was 47 ± 13 years; 59% (23) were female; and 69% (27) had enlarged kidneys with median time from diagnosis 14.2 (interquartile range 7.0-25.9) years. Pain (52%) and associated analgesic use (29%) were common. Pain severity was associated with increasing age [odds ratio (OR) = 1.07, P = 0.009], female gender (OR = 4.34, P = 0.018), estimated glomerular filtration rate <60 mL/min/1.73 m2 (OR = 5.45, P = 0.021) and hypertension (OR = 12.11, P = 0.007), but not with kidney size (P = 0.23). The APAT achieved good internal consistency (Cronbach's alpha coefficient = 0.91) and test-retest reliability (domain intra-class correlation coefficients ranging from 0.62 to 0.90).ConclusionsThe APAT demonstrated good acceptability and reliability, and following further validation in a larger cohort could represent an invaluable tool for future ADPKD pain studies.

Dataset Information

Automatic Measurement of Kidney and Liver Volumes from MR Images of Patients Affected by Autosomal Dominant Polycystic Kidney Disease.

Publications

Automatic Measurement of Kidney and Liver Volumes from MR Images of Patients Affected by Autosomal Dominant Polycystic Kidney Disease.

Similar Datasets

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