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Clinical Trial Summary

We propose hyperspectral imaging analysis as a method to identify the efficacy of hormone-tacrolimus therapy for PMN, and to classify sensitive and insensitive patients treated with hormone-tacrolimus regimen. A variety of machine learning models were used to prove that hyperspectral imaging technology could assist patients in selecting the optimal treatment plan, and further explore the predictive indicators of PMN treatment effect.


Clinical Trial Description

Renal biopsy slides of patients with idiopathic membranous nephropathy diagnosed by renal biopsy in our hospital from February 2016 to February 2022 were collected. ENVI Classic software was used to process the hyperspectral images and delineate the region of interest. The one-dimensional spectral data of each pixel in each region were derived. Machine learning and deep learning methods were used to analyze the characteristics of hyperspectral data and classify them. The data of the previous study came from the Department of Pathology and Nephrology of Qianfoshan Hospital in Shandong Province. Under the light microscope, electron microscope and immunofluorescence microscope, the pathological types of glomerular diseases in patients with proteinuria were identified. By scanning the corresponding patient's H&E stained pathological sections, the hyperspectral microscopic images were classified by machine learning and deep learning methods, and the classification accuracy was greater than 85%. It was concluded that hyperspectral imaging technology can be used as a non-invasive diagnostic method to predict treatment response. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05797038
Study type Observational
Source Qianfoshan Hospital
Contact Wang Zongsong
Phone 18660190175
Email wzsong3@163.com
Status Not yet recruiting
Phase
Start date April 25, 2023
Completion date December 30, 2023

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