Dataset Information

Prognostic Performance of Kidney Volume Measurement for Polycystic Kidney Disease: A Comparative Study of Ellipsoid vs. Manual Segmentation.

ABSTRACT: Total kidney volume (TKV) is a validated prognostic biomarker for risk assessment in autosomal dominant polycystic kidney disease (ADPKD). TKV by manual segmentation (MS) is the "gold standard" but is time-consuming and requires expertise. The purpose of this study was to compare TKV-based prognostic performance by ellipsoid (EL) vs. MS in a large cohort of patients. Cross-sectional study of 308 patients seen at a tertiary referral center; all had a standardized MRI with typical imaging of ADPKD. An experienced radiologist blinded to patient clinical results performed all TKV measurements by EL and MS. We assessed the agreement of TKV measurements by intraclass correlation(ICC) and Bland-Altman plot and also how the disagreement of the two methods impact the prognostic performance of the Mayo Clinic Imaging Classification (MCIC). We found a high ICC of TKV measurements (0.991, p?20%. We also found a high degree of agreement of the individual MCIC risk classes (i.e. 1A to 1E) with a Cohen's weighted-kappa of 0.89; but 42 cases (13.6%) were misclassified by EL with no misclassification spanning more than one risk class. The sensitivity and specificity of EL in distinguishing low-risk (1A-B) from high-risk (1C-E) MCIC prognostic grouping were 96.6% and 96.1%, respectively. Overall, we found an excellent agreement of TKV-based risk assessment between EL and MS. However, caution is warranted for patients with MCIC 1B and 1C, as misclassification can have therapeutic consequence.

SUBMITTER: Shi B

PROVIDER: S-EPMC6662759 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Prognostic Performance of Kidney Volume Measurement for Polycystic Kidney Disease: A Comparative Study of Ellipsoid vs. Manual Segmentation.

Shi Beili B Akbari Pedram P Pourafkari Marina M Iliuta Ioan-Andrei IA Guiard Elsa E Quist Crystal F CF Song Xuewen X Hillier David D Khalili Korosh K Pei York Y

Scientific reports 20190729 1

Total kidney volume (TKV) is a validated prognostic biomarker for risk assessment in autosomal dominant polycystic kidney disease (ADPKD). TKV by manual segmentation (MS) is the "gold standard" but is time-consuming and requires expertise. The purpose of this study was to compare TKV-based prognostic performance by ellipsoid (EL) vs. MS in a large cohort of patients. Cross-sectional study of 308 patients seen at a tertiary referral center; all had a standardized MRI with typical imaging of ADPKD ...[more]

PMID: 31358787

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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:Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability.

Project description:OBJECTIVE:Volume measurements by three-dimensional (3D) ultrasonography are considered more accurate than those performed by two-dimensional (2D) ultrasonography. The purpose of this study was to compare the agreement of three techniques, as well as the inter- and intraobserver agreements for volume measurements of fetal fluid-filled structures. METHODS:Fifty 3D volume datasets of fetal stomachs and bladders were explored. Volume measurements were performed independently by two observers using: (1) Virtual Organ Computer-aided AnaLysis (VOCAL); (2) inversion mode; and (3) 'manual segmentation'. Reliability was evaluated using intraclass correlation coefficient (ICC), and Bland-Altman plots were generated to examine bias and agreement. The time required to complete the measurements was compared using Student's t-test or the Wilcoxon Signed Rank Test, and P-values < 0.025 or < 0.05 were considered statistically significant. RESULTS:All volume datasets could be measured using the three techniques. A high degree of reliability was observed between: (1) VOCAL and inversion mode (ICC, 0.995; 95% CI, 0.992-0.997); (2) VOCAL and manual segmentation (ICC, 0.997; 95% CI, 0.995-0.998); and (3) inversion mode and manual segmentation (ICC, 0.995; 95% CI, 0.992-0.997). There was good agreement between VOCAL and inversion mode (mean, - 2.4%; 95% limits of agreement, - 20.1 to 15.3%), VOCAL and manual segmentation (mean, - 8.3%; 95% limits of agreement, - 28.8 to 12.2%) as well as between inversion mode and manual segmentation (mean, 5.9%, 95% limits of agreement: - 14.3 to 26%). Manual segmentation and inversion mode measurements were obtained significantly faster than those by VOCAL. CONCLUSIONS:Volume measurements of fetal fluid-filled structures of relatively regular shape with inversion mode and manual segmentation are feasible. Both techniques have good agreement with VOCAL and are significantly faster than VOCAL. Inversion mode is a reliable method for volume calculations of fluid-filled organs, whereas manual segmentation can be used when volume measurements by VOCAL or inversion mode are technically difficult to obtain, such as solid structures with poorly defined borders as the volume dataset is rotated, like the uterine cervix.

Dataset Information

Prognostic Performance of Kidney Volume Measurement for Polycystic Kidney Disease: A Comparative Study of Ellipsoid vs. Manual Segmentation.

Publications

Prognostic Performance of Kidney Volume Measurement for Polycystic Kidney Disease: A Comparative Study of Ellipsoid vs. Manual Segmentation.

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