AUTHOR=Kang Weijie , Ji Min , Zhang Huili , Shi Hua , Xiang Tianchao , Li Yaqi , Fang Ye , Qi Qi , Wang Junbo , Shen Jian , Tang Liangfeng , Liu Xiaoxiong , Ye Yingzi , Ge Xiaoling , Wang Xiang , Xu Hong , Qiao Zhongwei , Shi Jun , Rao Jia
TITLE=A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI
JOURNAL=Frontiers in Physics
VOLUME=10
YEAR=2022
URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.920506
DOI=10.3389/fphy.2022.920506
ISSN=2296-424X
ABSTRACT=Background: Assessment of renal lesions and deficiency accurately remains critical in the diagnosis of congenital anomalies of the kidneys and urinary tracts (CAKUT) in children. Advanced imaging such as Magnetic resonance Imaging (MRI) and Diffusion weighted Imaging (DWI) allows structural and functional insufficiency to be detected. Currently, radiomics machine learning models are being explored as full-automated diagnostic tools. We aimed to develop a machine learning integrated radiomics model to predict renal anomalies and deficiency in children.
Methods: A retrospective study of 280 children with MRI/DWI were enrolled between 2018 and 202 at a children’s hospital. A total of 1,037 radiomics features were extracted from the DWI images of each participant, which were divided into training set and test set (8:2 split). Using 5-fold cross-validated method, multiple machine learning algorithms were employed to predict renal lesions and deficiency when compared with the radiologist’s diagnosis based on DWI, 99mTc-labeled dimercaptosuccinic acid (DMSA) SPECT cortical renal scintigraphy or 99mTc-labeled diethylenetriamine pentaacetate (DTPA) renal scan.
Results: For detecting the kidney lesions, the LASSO + Random Forest algorithm outperformed other classifiers with an accuracy of 0.750 (95% confidence interval, 0.734–0.766) and area under the curve (AUC) of 0.765 (95% confidence interval, 0.700–0.831). The performance of classifiers did not show a significant difference (p > 0.05) in detecting bilateral or unilateral kidney lesions by DWI scanning. The classifiers performed significantly better in bilateral kidney deficit than in unilateral kidney deficit (p < 0.05). We next built prediction models for renal deficiency using the radiomics signature and clinical features compared to renal scintigraphy. The ensemble model had a high-test accuracy of 80.9% ± 4.2% and a sensitivity of 91.7% ± 7.1% with a moderate calibration.
Conclusion: An ensemble model integrated with DWI-radiomic and clinical features can be utilized to predict renal lesions and deficiency in children with CAKUT.