Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning

Abstract

Following autosomal dominant polycystic kidney disease (ADPKD) progression by measuring organ volumes requires low measurement variability. The objective of this study is to reduce organ volume measurement variability on MRI of ADPKD patients by utilizing all pulse sequences to obtain multiple measurements which allows outlier analysis to find errors and averaging to reduce variability. In order to make measurements on multiple pulse sequences practical, a 3D multi-modality multi-class segmentation model based on nnU-net was trained/validated using T1, T2, SSFP, DWIand CT from 413 subjects. Reproducibility was assessed with test-re-test methodology on ADPKD subjects (n=19) scanned twice within a 3-week interval correcting outliers and averaging the measurements across all sequences. Absolute percent differences in organ volumes were compared topaired students t-test. Dice similarlity coefficient >97%, Jaccard Index >0.94, mean surface distance <1mmand mean Hausdorff Distance <2cm for all three organs and all five sequences werefound on internal (n=25), external (n=37) and test-re-test reproducibility assessment (38 scans in 19 subjects). When averaging volumes measured from fiveMRI sequences, the model automatically segmented kidneys with test-re-test reproducibility (percent absolute difference between exam 1 and exam 2) of 1.3% which was better than all fiveexpert observers. It reliably stratified ADPKD into Mayo Imaging Classification (area under the curve=100%) compared to radiologist. 3D deep learning measures organ volumes on fiveMRI sequences leveraging the power of outlier analysis and averaging to achieve 1.3% total kidney test-re-test reproducibility.