Application of Quantum Detection-Driven Artificial Intelligence Algorithms for Single-Molecule cfDNA Characterization in the Early Diagnosis of Prostate Cancer

ConditionsBenign Prostate Hypertrophy(BPH)Prostate Cancer (Diagnosis)Prostate Neoplasm

Trial statusNot yet recruiting

Trial phaseNot listed

Trial typeObservational

Biological sexMale

Age18-80

SponsorShanghai Changzheng Hospital

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Male, aged 18-80 years;

PSA > 4 ng/ml;

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.

Disqualifiers

Patients diagnosed with any malignant tumour within the past five years;

Patients who have undergone transurethral resection or enucleation of the prostate;

Patients who have previously received treatment for prostate cancer, including but not limited to endocrine therapy, targeted therapy, or immunotherapy;

Patients on long-term anticoagulant or antiplatelet therapy (anticoagulants discontinued for less than one week);

Trial population

Males aged 18-80 years with elevated PSA (\>4 ng/ml) and clinical indications for prostate biopsy.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

Quantum Detection
Diagnostic test
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.
Quantum Detection
Diagnostic test
This cohort will prospectively enroll new patients with suspected prostate cancer from the same institution as testing cohort. The objective is initial model validation. The intervention entails collecting pre-biopsy blood samples from these participants. The cfDNA from these fresh samples is analyzed using the locked model from the training phase. The model's predictions are then compared against the gold-standard prostate biopsy results to assess initial diagnostic performance.
Quantum Detection
Diagnostic test
This cohort will prospectively recruit patients from multiple independent clinical centers. The objective is to test the model's generalizability. The intervention involves standardized blood collection across all external sites, with samples sent to a central lab for blinded cfDNA analysis using the finalized, locked-down model.

Treatment groups

1,100 Participants

are divided into 3 treatment groups

Group A: Retrospective Testing Cohort1 intervention

Group B: Prospective Internal Validation Cohort1 intervention

Group C: Prospective external Validation Cohort1 intervention

Trial outcomes

Primary outcomes

Area under the receiver operating characteristic curve (AUC-ROC) for the predictive model in the general population for prostate cancer.

Time frame