#3390 adpkd predictor: a cloud-based prognostic tool for autosomal dominant polycystic kidney disease

Abstract Background and Aims ADPKD is a progressively debilitating genetic disease characterized by the growth of numerous cysts in the kidneys, leading eventually to end-stage renal disease. Total Kidney Volume (TKV) is accepted by FDA and EMA as a prognostic biomarker and quantitative predictor of kidney function decline, and is currently used to select patients to receive Tolvaptan treatment. However, TKV calculation from manual tracing of medical images is labor-intensive; for better accuracy, current available methods involve the injection of an iodinated contrast medium for CT scan, with important limitations in patients with impaired renal function. We developed ADPKD Predictor, a user-friendly cloud-based tool for fast and accurate estimation of disease classification and progression, based on automated kidney and cysts segmentation from MRI data. Method An online tool was designed on Microsoft Azure Cloud to automatize the set-up and running of a previously developed algorithm implemented in MATLAB to automatically detect kidneys and cysts contours from MRI data (T2-weighted turbo-spin-echo SPIR), based on advanced image processing techniques [1]. Through the web interface, the user is requested to upload the MRI data and select one point inside kidney's parenchyma in the central slice (Figure 1). Then, TKV is automatically calculated and eGFR based on CKD-EPI equation, ADPKD Imaging Classification and future eGFR [2], estimated Tolvaptan treatment effect [3], and GFR Category based on KDIGO CKD staging system are obtained (Figure 2). The MRI dataset is anonymized before upload to the cloud; data and results are stored in a secure and reliable environment controlled by the user. Results The proposed solution is very fast and precise compared to manual segmentation of medical images (absolute mean error 2.4% ± 2.7%) [1]. Moreover, it is faster and more accurate than the commonly used ellipsoid-based method, resulting in a manifold reduction of misclassification error (2.5%) [1] and therefore potential therapeutic consequences. Another advantage is its usability, with no specific computational expertise, numerical software or dedicated hardware required, since all computations are run remotely in the cloud. Conclusion ADPKD Predictor provides a fast and reproducible assessment of risk classification and disease progression, based on precise morphologic classification of the renal and cysts volume of patient. The proposed solution represents an extremely useful tool for researchers and clinicians to easily obtain an accurate estimation of risk classification potentially helping in a correct and effective stratification of patients, and monitor patient's disease progression, hence supporting a correct and effective therapy administration. Also, it would represent a great benefit for the patient, since the tool analyzes medical images obtained without the use of contrast medium.