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. 2024 Sep 27;24(1):256.
doi: 10.1186/s12880-024-01434-x.

Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy

Yoon Ho Choi  1 , Ji-Eun Kim  2 , Ro Woon Lee  3 , Byoungje Kim  4 , Hyeong Chan Shin  5 , Misun Choe  5 , Yaerim Kim  6 , Woo Yeong Park  6 , Kyubok Jin  6 , Seungyeup Han  6 , Jin Hyuk Paek #  7 , Kipyo Kim #  8
Affiliations

Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy

Yoon Ho Choi et al. BMC Med Imaging. .

Abstract

Background: Kidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade.

Methods: We included patients aged ≥ 18 years who underwent kidney biopsy and abdominal CT scan within a week before kidney biopsy. Left kidneys were three-dimensionally segmented using a deep learning model based on the 3D Swin UNEt Transformers architecture. We additionally defined isovolumic cortical regions of interest near the lower pole of the left kidneys. Shape, first-order, and high-order texture features were extracted after resampling and kernel normalization. Correlations and diagnostic metrics between extracted features and chronic histologic lesions were examined. A machine learning-based radiomic prediction model for moderate chronicity was developed and compared according to the segmented regions of interest (ROI).

Results: Overall, moderate correlations with statistical significance (P < 0.05) were found between chronic histopathologic grade and top-ranked radiomic features. Total parenchymal features were more strongly correlated than cortical ROI features, and texture features were more highly ranked. However, conventional imaging markers, including kidney length, were poorly correlated. Top-ranked individual radiomic features had areas under receiver operating characteristic curves (AUCs) of 0.65 to 0.74. Developed radiomics models for moderate-to-severe chronicity achieved AUCs of 0.89 (95% confidence interval [CI] 0.75-0.99) and 0.74 (95% CI 0.52-0.93) for total parenchymal and cortical ROI features, respectively.

Conclusion: Significant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.

Keywords: Chronic kidney disease; Histopathology; Kidney fibrosis; Radiomics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Three-dimensional masking processes. (A) A representative axial slice of a CT image and its corresponding mask. (B) 3D reconstruction image of a resampled mask. (C) An example of an isovolumic mask of a cortical ROI. (D) 3D reconstruction image of a cortical ROI mask
Fig. 2
Fig. 2
Receiver operating characteristic curves and areas under the curves of the radiomic features for chronic histopathologic lesions. Total parenchymal features predicting (A) interstitial fibrosis, (B) tubular atrophy, (C) glomerulosclerosis, and (D) chronicity grade. Cortical ROI features predicting (E) interstitial fibrosis, (F) tubular atrophy, (G) glomerulosclerosis, and (H) chronicity grade. The top five features, ranked by AUC, were plotted alongside the conventional marker, major axis length
Fig. 3
Fig. 3
Evaluation metrics and feature importance of the developed radiomics model. (A) Receiver operating characteristic curves and (B) precision-recall curves of the model based on total parenchymal and cortical ROI features. (C) SHAP summary plot for the model developed using total parenchymal features
See this image and copyright information in PMC

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