What type of study is this?

This is a Quantitative Study study.

October 15, 2025Open Access

Clinical Relevance of Computationally Derived Attributes of Arteries and Arterioles in focal segmental glomerulosclerosis and minimal change disease

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Authors

JZJin Zhou DDDawit Demeke XLXiang Li

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Overview

This research demonstrates significant correlations between pathomic features and disease progression in focal segmental glomerulosclerosis and minimal change disease.

Key Points

Using computational methods, significant correlations were found between visual scores of pathology and arterial characteristics in patients with focal segmental glomerulosclerosis and minimal change disease.
Pathomic features showed improved predictions of disease progression, enhancing clinical assessment metrics from 0.70 to 0.75 for arterioles and from 0.69 to 0.74 for arcuate arteries.
A total of 2,316 arterial structures, including arterioles and arteries, were analyzed using advanced deep learning models to accurately segment and quantify vascular features.
This innovative computational pipeline improves the reliability and standardization of arterial assessments, potentially transforming clinical practices in nephrology.

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/68f01110f081da0584b5679c https://doi.org/10.1101/2025.10.08.25336276

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October 15, 2025Open Access

Clinical Relevance of Computationally Derived Attributes of Arteries and Arterioles in focal segmental glomerulosclerosis and minimal change disease

View Full Paper

Ask AI

Bookmark

Authors

JZJin Zhou DDDawit Demeke XLXiang Li

Discussion

Member takes

Overview

This research demonstrates significant correlations between pathomic features and disease progression in focal segmental glomerulosclerosis and minimal change disease.

Key Points

Using computational methods, significant correlations were found between visual scores of pathology and arterial characteristics in patients with focal segmental glomerulosclerosis and minimal change disease.
Pathomic features showed improved predictions of disease progression, enhancing clinical assessment metrics from 0.70 to 0.75 for arterioles and from 0.69 to 0.74 for arcuate arteries.
A total of 2,316 arterial structures, including arterioles and arteries, were analyzed using advanced deep learning models to accurately segment and quantify vascular features.
This innovative computational pipeline improves the reliability and standardization of arterial assessments, potentially transforming clinical practices in nephrology.

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/68f01110f081da0584b5679c https://doi.org/10.1101/2025.10.08.25336276

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