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Abstract: SA-PO007

ADPKD Segmentator: A Cloud-Based Prognostic Tool for Autosomal Dominant Polycystic Kidney Disease

Session Information

  • Bioengineering
    November 05, 2022 | Location: Exhibit Hall, Orange County Convention Center‚ West Building
    Abstract Time: 10:00 AM - 12:00 PM

Category: Bioengineering

  • 300 Bioengineering

Authors

  • Carbone, Vincenzo, InSilicoTrials Technologies, Trieste, Italy
  • Baretta, Alessia, InSilicoTrials Technologies, Trieste, Italy
  • Corsi, Cristiana, Universita degli Studi di Bologna, Bologna, Emilia-Romagna, Italy
  • Bursi, Roberta, InSilicoTrials Technologies, Trieste, Italy
Background

ADPKD is characterized by the growth of numerous cysts in the kidneys, leading to an increment of Total Kidney Volume (TKV) and progressive decline in renal function. FDA and EMA accept TKV as a prognostic biomarker for disease progression, however TKV calculation from medical images is labor-intensive. ADPKD Segmentator is a user-friendly cloud-based tool for fast and accurate ADPKD classification, based on automated kidney and cysts segmentation from MRI.

Methods

An online tool was designed on Microsoft Azure Cloud to automatize the set-up and running of a previously developed software implemented in MATLAB to automatically detect kidney' and cysts’ contours from MRI. Through the web interface, the user is only requested to upload the MRI dataset and select one point inside kidney’s parenchyma in the central slice. Then, TKV is automatically calculated and ADPKD Classification is obtained (Figure 1). 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 (Figure 1). Moreover, it is faster and more accurate than the commonly used ellipsoid-based method, resulting in a manifold reduction of misclassification error (Table 1). 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 Segmentator provides a reproducible and precise morphologic classification of the renal and cysts volume of ADPKD patients. It represents an extremely useful tool for clinicians, potentially helping in monitoring disease progression, supporting correct therapy administration, and effective stratification of patients. Also, it would represent a great benefit for the patient, since the tool analyzes medical images obtained without the use of contrast medium.

 ADPKD SEGMENTATORELLIPSOID-BASED
METHOD
Manual Intervention< 1 minup to 5 min
Absolute mean error2.4% ± 2.7%7.4% ± 5.1%
Misclassification error2.5%13.7%