Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients

BACKGROUND: The analytical renal pathology system (ARPS) based on convolutional neural networks has been used successfully in native IgA nephropathy (IgAN) patients. Considering the similarity of pathologic features, we aim to evaluate the performance of the ARPS in allograft IgAN patients and broaden its implementation.

METHODS: Biopsy-proven allograft IgAN patients from two different centers were enrolled for internal and external validation. We implemented the ARPS to identify glomerular lesions and intrinsic glomerular cells, and then evaluated its performance. Consistency between the ARPS and pathologists was assessed using intraclass correlation coefficients. The association of digital pathological features with clinical and pathological data was measured. Kaplan-Meier survival curve and cox proportional hazards model were applied to investigate prognosis prediction.

RESULTS: A total of 56 biopsy-proven allograft IgAN patients from the internal center and 17 biopsy-proven allograft IgAN patients from the external center were enrolled in this study. The ARPS was successfully applied to identify the glomerular lesions (F1-score, 0.696-0.959) and quantify intrinsic glomerular cells (F1-score, 0.888-0.968) in allograft IgAN patients rapidly and precisely. Furthermore, the mesangial hypercellularity score was positively correlated with all mesangial metrics provided by ARPS [Spearman's correlation coefficient (r), 0.439-0.472, and all p values < 0.001]. Besides, a higher allograft survival was noticed among patients in the high-level groups of the maximum and ratio of endothelial cells, as well as the maximum and density of podocytes.

CONCLUSION: We propose that the ARPS could be implemented in future clinical practice with outstanding capability.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:46

Enthalten in:

Renal failure - 46(2024), 1 vom: 01. März, Seite 2322043

Sprache:

Englisch

Beteiligte Personen:

Liu, Xumeng [VerfasserIn]
Fang, Huiwen [VerfasserIn]
Liang, Dongmei [VerfasserIn]
Lei, Qunjuan [VerfasserIn]
Wang, Jiaping [VerfasserIn]
Xu, Feng [VerfasserIn]
Liang, Shaoshan [VerfasserIn]
Liang, Dandan [VerfasserIn]
Yang, Fan [VerfasserIn]
Li, Heng [VerfasserIn]
Chen, Jianghua [VerfasserIn]
Ni, Yuan [VerfasserIn]
Xie, Guotong [VerfasserIn]
Zeng, Caihong [VerfasserIn]

Links:

Volltext

Themen:

Allograft IgAN
Deep learning
Glomerular lesion
Intrinsic glomerular cell
Journal Article
Outcome

Anmerkungen:

Date Completed 04.03.2024

Date Revised 06.03.2024

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1080/0886022X.2024.2322043

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM369149483