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Abstract: TH-PO398

A Novel CT Imaging-Radiomics Approach for Kidney Function Evaluation in Autosomal Dominant Polycystic Kidney Disease

Session Information

Category: Genetic Diseases of the Kidneys

  • 1101 Genetic Diseases of the Kidneys: Cystic

Authors

  • Calvaruso, Luca, Dipartimento di Scienze Mediche e Chirurgiche, U.O.C. Nefrologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy., Rome, Italy
  • Ferraro, Pietro Manuel, Dipartimento di Scienze Mediche e Chirurgiche, U.O.C. Nefrologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy., Rome, Italy
  • Fulignati, Pierluigi, Dipartimento di Scienze Mediche e Chirurgiche, U.O.C. Nefrologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy., Rome, Italy
  • Larosa, Luigi, Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology – Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
  • Boldrini, Luca, Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology – Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
  • Tran, Huong Elena, Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology – Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
  • Votta, Claudio, Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology – Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
  • Grandaliano, Giuseppe, Dipartimento di Scienze Mediche e Chirurgiche, U.O.C. Nefrologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy., Rome, Italy
Background

The aim of this study is to develop and validate a model based on radiomics to predict kidney function among patients with autosomal dominant polycystic kidney disease (ADPKD) studied by CT for determination of total kidney volume (TKV).

Methods

We retrospectively selected a cohort of 58 patients with ADPKD who underwent CT scan in 2021, including 31 patients with eGFR ≥ 60 mL/min/1.73 m2 (class 0) and 27 with eGFR < 60 mL/min/1.73 m2 (class 1). An expert radiologist generated a region of interest (ROI) segmentation for cystic kidney compounds, obtaining 58 ROIs from which we extracted 217 radiomic features using a dedicated software. We built three different logistic regression models: a height-adjusted TKV (Ht-TKV) model, a radiomic model based on the most statistically significative radiomic feature from univariate analysis (F_cm.sum.var), and a model from the combination of the above. Area under the curve (AUC) of the receiver operating characteristic (ROC) and accuracy were employed to evaluate models performance in discriminating between the two eGFR classes. Internal 3-fold cross-validation (CV) was performed.

Results

The Ht-TKV, radiomic and combined models presented respectively an AUC (95% confidence interval) of 0.79 (0.67-0.91), 0.84 (0.73-0.94), 0.85 (0.75-0.95), confirmed by the CV. Mean (standard deviation) values of the accuracy over CV iterations were 0.67(0.09), 0.78(0.08), 0.79(0.08) for the three models.

Conclusion

The Ht-TKV-radiomic combined model based on CT images from polycystic kidneys resulted the most effective in the prediction of baseline kidney function in our cohort. Further studies should implement a model extension to predict kidney function slope in order to confirm the role of radiomics in ADPKD management.