Benign Prostate Hypertrophy(BPH), Prostate Cancer (Diagnosis), Prostate Neoplasm
Conditions
Keywords
Early diagnosis, cfDNA
Brief summary
This research project aims to develop a novel blood testing method integrating cutting-edge quantum sensing and artificial intelligence technologies to achieve precise, non-invasive early diagnosis of prostate cancer. The research will employ quantum sensors to perform ultra-high-sensitivity measurements of circulating free DNA (cfDNA) in blood, thereby training a dedicated AI diagnostic model. The ultimate objective is to establish the diagnostic efficacy of this approach through clinical validation, providing clinicians with a novel diagnostic tool capable of significantly reducing unnecessary prostate biopsy procedures.
Interventions
DIAGNOSTIC_TESTQuantum Detection
This cohort will utilize archived plasma samples from a historical patient population with confirmed diagnoses (prostate cancer vs. controls). The objective is model development. The intervention involves analyzing these stored samples using the quantum sensing platform to extract multi-modal cfDNA features (e.g., fragmentomics, methylation). This data is then used to train and optimize the initial AI diagnostic algorithm, establishing the core model before prospective validation.
Sponsors
West China Hospital
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
The First Affiliated Hospital of Guangzhou Medical University
First Affiliated Hospital of Ningbo University
Jiangsu Provincial People's Hospital
The First Affiliated Hospital of Soochow University
Shanghai Changzheng Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
MALE
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
1. Male, aged 18-80 years; 2. PSA \> 4 ng/ml; 3. Patients meeting criteria for prostate biopsy: * fPSA/PSA \< 0.16 or PSA D \> 0.15 or PSA V \> 0.75; ② Positive digital rectal examination (DRE); ③ Imaging studies (ultrasound/MRI) showing suspicious lesions.
Exclusion criteria
1. Patients diagnosed with any malignant tumour within the past five years; 2. Patients who have undergone transurethral resection or enucleation of the prostate; 3. Patients who have previously received treatment for prostate cancer, including but not limited to endocrine therapy, targeted therapy, or immunotherapy; 4. Patients on long-term anticoagulant or antiplatelet therapy (anticoagulants discontinued for less than one week); 5. Patients who have received any form of tumour treatment prior to enrolment blood sampling, including surgery, radiotherapy/chemotherapy, endocrine therapy, targeted therapy, or immunotherapy; 6. Concurrent severe systemic diseases deemed by the investigator likely to interfere with trial treatment, evaluation, or compliance, including serious respiratory, circulatory, neurological, psychiatric, gastrointestinal, endocrine, immunological, or urological disorders; 7. Organ transplant recipients or individuals with prior non-autologous (allogeneic) bone marrow or stem cell transplantation; 8. Subjects who have undergone blood transfusion within one month prior to blood sampling; 9. Patients currently participating in other clinical trials, or who have participated in other clinical trials within the past year; 10. Patients deemed unsuitable for this clinical trial by the investigator; 11. Patients meeting any of the above criteria shall not be eligible for inclusion as subjects.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Area under the receiver operating characteristic curve (AUC-ROC) for the predictive model in the general population for prostate cancer. | Through primary completion which may take 12 months. |
| Sensitivity of the predictive model in detecting prostate cancer within the general population. | Through primary completion which may take 12 months. |
| Specificity of the predictive model in detecting prostate cancer within the general population. | Through primary completion which may take 12 months. |
Secondary
| Measure | Time frame |
|---|---|
| Area under the ROC curve for the predictive model in identifying prostate cancer within the PSA grey zone cohort. | Through primary completion which may take 12 months. |
| Sensitivity of the predictive model in identifying prostate cancer within the PSA grey zone cohort. | Through primary completion which may take 12 months. |
| The specificity of the predictive model in identifying prostate cancer among individuals in the PSA grey zone. | Through primary completion which may take 12 months. |
Countries
China
Contacts
Primary ContactShancheng Ren, MD,PhD
Backup ContactDuocai Li
Outcome results
None listed