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Trial Title:
Multicenter, Prospective Clinical Study of the Serum Raman Spectroscopy Intelligent System for the Diagnosis of Prostate Cancer
NCT ID:
NCT05854940
Condition:
Prostate Cancer
Conditions: Official terms:
Prostatic Neoplasms
Conditions: Keywords:
Prostate Cancer
Raman spectroscopy
Early diagnosis
artificial intelligence
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
Serum Raman spectroscopy intelligent diagnostic system
Description:
Intelligent diagnostic system based on Raman spectrum of serum
Arm group label:
Eligible participants for early diagnosis of prostate cancer
Summary:
At present, the most commonly used clinical screening tool is based on prostate-specific
antigen (PSA) examination. Because PSA is a tissue-specific rather than a tumor-specific
marker, it has low specificity and sensitivity for prostate cancer. Although these
PSA-related diagnostic models (PHI, 4Kscore) have been proved to improve the sensitivity
and specificity of the early diagnosis of prostate cancer, they still do not meet the
requirements of accurate diagnosis. Therefore, it is extremely important to develop a
diagnosis tool with higher specificity, sensitivity and accuracy in the current prostate
tumor screening strategy.
Raman spectroscopy (Raman Spectrum, RS) as a non-invasive and high specificity of
material molecular detection technology, can be obtained in the molecular level, thus
sensitive to detect biological samples tumor metabolism related proteins, nucleic acids,
lipids and sugar composition of bio-molecules changes. As scientists pointed out in a
literature in "chemical society reviews"in 2020, although SERS technology has shown good
diagnostic efficacy in lots of preclinical studies in multiple tumors, it is limited to a
generally small sample size and lacks external validation. There for, a clinical study of
Raman spectra for tumor diagnosis is needed, which meets the following requirements: 1.An
objective, fast and practical application of Raman spectral data processing is needed and
deep learning method may be the best classification method; 2. It requires multicenter
and large clinical samples to train deep learning diagnostic model, and verify its true
efficacy through external data of prospective study.
In our preliminary study,we have collected Raman spectra data from a large cohort of 2899
patients and constructed Raman intelligent diagnostic system based on CNN model. The
intelligent diagnostic system achieved accuracy of 83%. In order to obtain the highest
level of clinical evidence and truly realize clinical transformation, this prospective,
multi-center clinical study is designed to verify the intelligent diagnostic system for
early diagnosis of prostate cancer.
Criteria for eligibility:
Study pop:
Patients with suspected prostate cancer and meet the Chinese Guidelines for Prostate
Cancer (2014 edition); including:
1. Digital rectal examination found prostate nodules, any PSA.
2. B ultrasound, CT, MRI found abnormal signals, any PSA.
3. PSA> 10 ng/ml, any f / t PSA and PSAD values.
4. PSA 4 ~ 10 ng/ml, abnormal f / t PSA value or abnormal PSD value.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
1. Patients with suspected prostate cancer and meet the Chinese Guidelines for Prostate
Cancer (2014 edition)
2. PSA≤20;
3. The ECGO score was 0-1, and the cardiopulmonary function tolerated prostate biopsy;
4. After being fully informed of the purpose and possible risks of the study, the
patient agrees to participate in the trial and signed the "Informed Consent for the
use of clinical samples".
Exclusion criteria:
1. Previous history of other cancer;
2. Metabolic disorders caused by chronic renal failure or metabolic diseases, obviously
abnormal blood sugar, blood lipid and plasma protein;
3. Previously taking 5- α reductase inhibitor drug;
4. History of acute prostatitis or minimally invasive surgery inside the prostate
cavity for 3 months prior to puncture;
5. History of multiple blood transfusion;
6. Failure to cooperate with or refuse to participate in the clinical trial later.
Gender:
Male
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
RenJi hospital, school of Medicine, Shanghai Jiao Tong University
Address:
City:
Shanghai
Zip:
200120
Country:
China
Status:
Recruiting
Contact:
Last name:
Xiaoguang Shao, Doctor
Email:
shaoxgg@163.com
Investigator:
Last name:
Wei Xue, Doctor
Email:
Principal Investigator
Investigator:
Last name:
JiaHua Pan, Doctor
Email:
Principal Investigator
Start date:
June 10, 2023
Completion date:
June 30, 2023
Lead sponsor:
Agency:
RenJi Hospital
Agency class:
Other
Collaborator:
Agency:
Shanghai Zhongshan Hospital
Agency class:
Other
Collaborator:
Agency:
Changhai Hospital
Agency class:
Other
Collaborator:
Agency:
Ruijin Hospital
Agency class:
Other
Collaborator:
Agency:
Tongji Hospital
Agency class:
Other
Collaborator:
Agency:
Peking University People's Hospital
Agency class:
Other
Collaborator:
Agency:
First Affiliated Hospital Xi'an Jiaotong University
Agency class:
Other
Source:
RenJi Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05854940
