How to Estimate Kidney Growth in Patients with Autosomal Dominant Polycystic Kidney Disease

Utiel, Francisco José Borrego; Hernández, Y Mario Espinosa

doi:10.1681/asn.0000000000000130

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…In our practice, we report the TKV over time and the estimated annual TKV growth rate. This growth rate is calculated from serial imaging data [35], presuming exponential kidney enlargement, with intervals of no less than one year between images. Incorporating the growth rate with Mayo Imaging Classification [36] offers a more comprehensive depiction of disease progression, accommodating the inter-individual variability observed within each Mayo class [37].…”

Section: Tkv and Its Annual Growth Ratementioning

confidence: 99%

A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression

Zhu,

He,

Blumenfeld

et al. 2024

Biomedicines

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Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment’s efficacy. Deep learning for segmenting the kidneys has improved these measurements’ speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.

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Section: Tkv and Its Annual Growth Ratementioning

confidence: 99%

A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression

Zhu,

He,

Blumenfeld

et al. 2024

Biomedicines

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“…In 2015, Irazabal et al proposed a model to predict GFR decline, using a single heightadjusted TKV (HtTKV) measurement, alongside age [23,24]. According to this model (the Mayo Imaging Classification), based on the assumptions of exponential kidney growth, and a fairly stable kidney growth rate over years [25], typical ADPKD patients can be divided into five classes (1A-E), characterized by their different renal function decline rates. This classification method, easily applied using the Mayo Clinic website (https://www.mayo.…”

Section: Total Kidney Volumementioning

confidence: 99%

Abdominal Imaging in ADPKD: Beyond Total Kidney Volume

Caroli

Kline

2023

JCM

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In the context of autosomal dominant polycystic kidney disease (ADPKD), measurement of the total kidney volume (TKV) is crucial. It acts as a marker for tracking disease progression, and evaluating the effectiveness of treatment strategies. The TKV has also been recognized as an enrichment biomarker and a possible surrogate endpoint in clinical trials. Several imaging modalities and methods are available to calculate the TKV, and the choice depends on the purpose of use. Technological advancements have made it possible to accurately assess the cyst burden, which can be crucial to assessing the disease state and helping to identify rapid progressors. Moreover, the development of automated algorithms has increased the efficiency of total kidney and cyst volume measurements. Beyond these measurements, the quantification and characterization of non-cystic kidney tissue shows potential for stratifying ADPKD patients early on, monitoring disease progression, and possibly predicting renal function loss. A broad spectrum of radiological imaging techniques are available to characterize the kidney tissue, showing promise when it comes to non-invasively picking up the early signs of ADPKD progression. Radiomics have been used to extract textural features from ADPKD images, providing valuable information about the heterogeneity of the cystic and non-cystic components. This review provides an overview of ADPKD imaging biomarkers, focusing on the quantification methods, potential, and necessary steps toward a successful translation to clinical practice.

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“…Irazabal et al found that MIC changed in 10% of ADPKD subjects after a median follow-up of 4 years 6 . Other studies also reported scan to scan variability in MIC class 19,20 .…”

mentioning

confidence: 93%

“…Irazabal et al found that MIC changed in 10% of ADPKD subjects after a median follow-up of 4 years 6 . Other studies also reported scan to scan variability in MIC class 19,20 .In this study, we identified ADPKD subjects with over 8 years of cross-sectional imaging follow-up to establish a ground truth annual htTKV growth rate. This ground truth was then compared to MIC using the earliest available scan, MIC using the latest scan, averaging MIC and least squares fitting.…”

mentioning

confidence: 99%

Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting

Hu,

Sharbatdaran,

et al. 2024

Sci Rep

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Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ($$n = 36$$ n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was $$2.1\% \pm 2\%$$ 2.1 % ± 2 % compared to $$1.1\% \pm 1\%$$ 1.1 % ± 1 % ($$p = 0.002$$ p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or $$1.4\% \pm 1\%$$ 1.4 % ± 1 % ($$p = 0.01$$ p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ($$p = 0.05$$ p = 0.05 ) and PKD2 mutation ($$p = 0.04$$ p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.

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How to Estimate Kidney Growth in Patients with Autosomal Dominant Polycystic Kidney Disease

Cited by 5 publications

References 31 publications

A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression

A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression

Abdominal Imaging in ADPKD: Beyond Total Kidney Volume

Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting

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