A pilot study investigating the feasibility of using a fully automatic software to assess the RENAL and PADUA score

Summary Purpose. - Image-based morphometric scoring systems such as the RENAL and PADUA scores are useful to evaluate the complexity of partial nephrectomy for renal cell carcinoma (RCC). The main aim of this study was to develop a new imaging software to enable an automatic detection and a 3D visualization of RCC from CT angiography (CTA) and to address the feasibility to use it to evaluate the features of the RENAL and the PADUA scores. Methods. - A training dataset of 210 patients CTA-scans manually segmented was used to train a deep learning algorithm to develop the automatic detection and 3D-visualization of RCC. A trained operator blindly assessed the RENAL and PADUA scores on a testing dataset of 41 CTA from patients with RCC using a commercialized semi-automatic software (ground truth) and the new automatic software. Concordance between the two methods was evaluated.

Automated summary

The study aimed to develop a new imaging software for automatic detection and 3D visualization of RCC from CTA scans, and to evaluate its feasibility in assessing the features of the RENAL and PADUA scores.