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Study on the Application of Hyperspectral Imaging Technique in CTX Treatment of IMN

Study on the Application of Hyperspectral Imaging Technique in CTX Treatment of IMN

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05850845
Enrollment
40
Registered
2023-05-09
Start date
2023-06-25
Completion date
2023-12-30
Last updated
2023-05-09

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cyclophosphamide, Hyperspectral Imaging, Idiopathic Membranous Nephropathy

Brief summary

Investigators propose hyperspectral imaging analysis as a method to distinguish the efficacy of hormone-combined cyclophosphamide therapy for PMN, and classify sensitive and insensitive patients treated with hormone-combined cyclophosphamide 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.

Detailed description

Renal puncture pathological sections from patients with nephropathy. ENVI Classic software was used to process the hyperspectral images and delineate the region of interest, and 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.

Interventions

CTX for idiopathic membranous nephropathy

Sponsors

Qianfoshan Hospital
Lead SponsorOTHER

Study design

Observational model
OTHER
Time perspective
OTHER

Eligibility

Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
No

Inclusion criteria

* Over 18 years old; * Patients with idiopathic membranous nephropathy confirmed by renal biopsy; * Had not received hormone and/or immunosuppressive therapy before renal biopsy; * Complete clinical data, all signed the Admission Certificate of Qianfoshan Hospital of Shandong Province, and agreed to use relevant medical information, biological specimen examination and examination results for scientific research.

Exclusion criteria

* 1.There are factors causing secondary membranous nephropathy, such as immune diseases (systemic lupus erythematosus), tumors/infections (viral hepatitis), drugs or poisons, etc. ; * Severe infection: fever, cough and expectoration, sore throat, abdominal pain, diarrhea, carbuncle and furuncle and other clinical manifestations of skin and soft tissue infection, blood routine white blood cell count beyond the normal range (10×109/L); * Severe cardiovascular disease: including chronic heart failure grade 3 or above and various arrhythmias; * Infectious diseases: active hepatitis, AIDS, syphilis, etc. ; * Tumor evidence: it has been found that there is a certain tumor or clinical manifestations, tumor markers, etc., suggesting the possibility of tumor; * Patients with follow-up time less than 6 months, incomplete data or missed diagnosis;

Design outcomes

Primary

MeasureTime frameDescription
Microhyperspectral image of a transrenal specimen2023.3-2023.12The microscopic hyperspectral images could accurately distinguish the remission group from the remission group with an accuracy of more than 80%

Contacts

Primary ContactWang Zunsong, doctor
wzsong3@163.com18660190175

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026