NCT05854940

Brief 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.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
490

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jun 2023

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

April 26, 2023

Completed
15 days until next milestone

First Posted

Study publicly available on registry

May 11, 2023

Completed
1 month until next milestone

Study Start

First participant enrolled

June 10, 2023

Completed
Same day until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 10, 2023

Completed
20 days until next milestone

Study Completion

Last participant's last visit for all outcomes

June 30, 2023

Completed
Last Updated

May 11, 2023

Status Verified

May 1, 2023

Enrollment Period

Same day

First QC Date

April 26, 2023

Last Update Submit

May 3, 2023

Conditions

Keywords

Prostate CancerRaman spectroscopyEarly diagnosisartificial intelligence

Outcome Measures

Primary Outcomes (1)

  • The accuracy of the Serum Raman Spectroscopy Intelligent System

    According to the final pathology results of prostate biopsy, count the accuracy of Serum Raman Spectroscopy Intelligent System for prostate cancer diagnosis.

    2023.6

Study Arms (1)

Eligible participants for early diagnosis of prostate cancer

According to the 2014 edition of China Prostate Cancer Diagnosis and Treatment Guidelines, patients need to undergo prostate biopsy

Diagnostic Test: Serum Raman spectroscopy intelligent diagnostic system

Interventions

Intelligent diagnostic system based on Raman spectrum of serum

Eligible participants for early diagnosis of prostate cancer

Eligibility Criteria

Age18 Years+
Sexmale
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

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.

You may qualify if:

  • Patients with suspected prostate cancer and meet the Chinese Guidelines for Prostate Cancer (2014 edition)
  • PSA≤20;
  • The ECGO score was 0-1, and the cardiopulmonary function tolerated prostate biopsy;
  • 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".

You may not qualify if:

  • Previous history of other cancer;
  • Metabolic disorders caused by chronic renal failure or metabolic diseases, obviously abnormal blood sugar, blood lipid and plasma protein;
  • Previously taking 5- α reductase inhibitor drug;
  • History of acute prostatitis or minimally invasive surgery inside the prostate cavity for 3 months prior to puncture;
  • History of multiple blood transfusion;
  • Failure to cooperate with or refuse to participate in the clinical trial later.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

RenJi hospital, school of Medicine, Shanghai Jiao Tong University

Shanghai, 200120, China

RECRUITING

MeSH Terms

Conditions

Prostatic NeoplasmsDisease

Condition Hierarchy (Ancestors)

Genital Neoplasms, MaleUrogenital NeoplasmsNeoplasms by SiteNeoplasmsGenital Diseases, MaleGenital DiseasesUrogenital DiseasesProstatic DiseasesMale Urogenital DiseasesPathologic ProcessesPathological Conditions, Signs and Symptoms

Central Study Contacts

Wei Xue, Doctor

CONTACT

Xiaoguang Shao, Doctor

CONTACT

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

April 26, 2023

First Posted

May 11, 2023

Study Start

June 10, 2023

Primary Completion

June 10, 2023

Study Completion

June 30, 2023

Last Updated

May 11, 2023

Record last verified: 2023-05

Locations