What type of study is this?

This is a Quantitative Study study (also classified as: Experimental Study).

October 9, 2025Open Access

Glomerular Segmentation, Classification, and Pathomic Feature-based Prediction of Clinical Outcomes in Minimal Change Disease and Focal Segmental Glomerulosclerosis

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Authors

AAAkhil Ambekar MRMaryam Roohian QLQian Liu

Discussion

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Overview

This analysis uses deep learning for glomerular segmentation to predict outcomes in focal segmental glomerulosclerosis and minimal change disease, suggesting new diagnostic pathways.

Key Points

Deep learning models accurately segment and classify glomeruli, leading to comparable outcomes to manual scoring.
The prognostic performance of pathomic features associated with disease progression and proteinuria remission were evaluated using Cox regression.
Agreement between computer-aided scoring and visual assessment was good for glomerulosclerosis, indicating reliability in automated methods.
Two pathomic features remained significant in predicting proteinuria remission after accounting for demographic and clinical factors.

Cite This Study

Ambekar et al. (2025) studied this question.

synapsesocial.com/papers/68e70da790569dd607ee5c5e https://doi.org/10.1101/2025.10.01.25336172

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October 9, 2025Open Access

Glomerular Segmentation, Classification, and Pathomic Feature-based Prediction of Clinical Outcomes in Minimal Change Disease and Focal Segmental Glomerulosclerosis

View Full Paper

Ask AI

Bookmark

Authors

AAAkhil Ambekar MRMaryam Roohian QLQian Liu

Discussion

Member takes

Overview

This analysis uses deep learning for glomerular segmentation to predict outcomes in focal segmental glomerulosclerosis and minimal change disease, suggesting new diagnostic pathways.

Key Points

Deep learning models accurately segment and classify glomeruli, leading to comparable outcomes to manual scoring.
The prognostic performance of pathomic features associated with disease progression and proteinuria remission were evaluated using Cox regression.
Agreement between computer-aided scoring and visual assessment was good for glomerulosclerosis, indicating reliability in automated methods.
Two pathomic features remained significant in predicting proteinuria remission after accounting for demographic and clinical factors.

Cite This Study

Ambekar et al. (2025) studied this question.

synapsesocial.com/papers/68e70da790569dd607ee5c5e https://doi.org/10.1101/2025.10.01.25336172

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