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  • 1
    In: Investigative Radiology, Ovid Technologies (Wolters Kluwer Health), Vol. 55, No. 4 ( 2020-4), p. 217-225
    Abstract: Autosomal dominant polycystic kidney disease (ADPKD) is a chronic progressive disorder with a significant disease burden leading to end-stage renal disease in more than 75% of the affected individuals. Although prediction of disease progression is highly important, all currently available biomarkers—including height-adjusted total kidney volume (htTKV)—have important drawbacks in the everyday clinical setting. Thus, the purpose of this study was to evaluate T2 mapping as a source of easily obtainable and accurate biomarkers, which are needed for improved patient counseling and selection of targeted treatment options. Materials and Methods A total of 139 ADPKD patients from The German ADPKD Tolvaptan Treatment Registry and 10 healthy controls underwent magnetic resonance imaging on a clinical 1.5-T system including acquisition of a Gradient-Echo-Spin-Echo T2 mapping sequence. The ADPKD patients were divided into 3 groups according to kidney cyst fraction (0%–35%, 36%–70%, 〉 70%) as a surrogate marker for disease severity. The htTKV was calculated based on standard T2-weighted imaging. Mean T2 relaxation times of both kidneys (kidney-T2) as well as T2 relaxation times of the residual kidney parenchyma (parenchyma-T2) were measured on the T2 maps. Results Calculation of parenchyma-T2 was 6- to 10-fold faster than determination of htTKV and kidney-T2 (0.78 ± 0.14 vs 4.78 ± 1.17 minutes, P 〈 0.001; 0.78 ± 0.14 vs 7.59 ± 1.57 minutes, P 〈 0.001). Parenchyma-T2 showed a similarly strong correlation to cyst fraction ( r = 0.77, P 〈 0.001) as kidney-T2 ( r = 0.76, P 〈 0.001), the strongest correlation to the serum-derived biomarker copeptin ( r = 0.37, P 〈 0.001), and allowed for the most distinct separation of patient groups divided according to cyst fraction. In contrast, htTKV showed an only moderate correlation to cyst fraction ( r = 0.48, P 〈 0.001). These observations were even more evident when considering only patients with preserved kidney function. Conclusions The rapidly assessable parenchyma-T2 shows a strong association with disease severity early in disease and is superior to htTKV when it comes to correlation with renal cyst fraction.
    Type of Medium: Online Resource
    ISSN: 1536-0210 , 0020-9996
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2020
    detail.hit.zdb_id: 2041543-6
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  • 2
    In: Kidney360, Ovid Technologies (Wolters Kluwer Health), Vol. 3, No. 12 ( 2022-12), p. 2048-2058
    Abstract: We developed a model for automated kidney and liver volumetry in ADPKD to provide assistance with time-consuming volumetry. The model works in both coronal and axial planes and was tested in the real-life setting using large multicentric cohorts. The trained model is published along with the code to allow for further joint development and integration into commercial software packages. Background Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting. Methods The convolutional neural network was trained on a large multicenter dataset consisting of 992 MRI scans of 327 patients. Manual segmentation delivered ground-truth labels. The model’s performance was evaluated in a separate test dataset of 93 patients (350 MRI scans) as well as a heterogeneous external dataset of 831 MRI scans from 323 patients. Results The segmentation model yielded excellent performance, achieving a median per study Dice coefficient of 0.92–0.97 for the kidneys and 0.96 for the liver. Automatically computed TKV correlated highly with manual measurements (intraclass correlation coefficient [ICC]: 0.996–0.999) with low bias and high precision (−0.2%±4% for axial images and 0.5%±4% for coronal images). TLV estimation showed an ICC of 0.999 and bias/precision of −0.5%±3%. For the external dataset, the automated TKV demonstrated bias and precision of −1%±7%. Conclusions Our deep learning model enabled accurate segmentation of kidneys and liver and objective assessment of TKV and TLV. Importantly, this approach was validated with axial and coronal MRI scans from 40 different scanners, making implementation in clinical routine care feasible. Clinical Trial registry name and registration number: The German ADPKD Tolvaptan Treatment Registry (AD[H]PKD), NCT02497521
    Type of Medium: Online Resource
    ISSN: 2641-7650
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2022
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