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.
Study Type
OBSERVATIONAL
Enrollment
1,100
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.
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.
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.
Cancer Hospital, Chinese Academy of Medical Sciences
Beijing, Beijing Municipality, China
The First Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, China
Jiangsu Provincial People's Hospital
Nanjing, Jiangsu, China
The First Affiliated Hospital of Soochow University
Suzhou, Jiangsu, China
Shanghai Changzheng Hospital
Shanghai, Shanghai Municipality, China
West China Hospital, Sichuan University
Chengdu, Sichuan, China
Ningbo No. 1 Hospital
Ningbo, Zhejiang, China
Area under the receiver operating characteristic curve (AUC-ROC) for the predictive model in the general population for prostate cancer.
Time frame: Through primary completion which may take 12 months.
Sensitivity of the predictive model in detecting prostate cancer within the general population.
Time frame: Through primary completion which may take 12 months.
Specificity of the predictive model in detecting prostate cancer within the general population.
Time frame: Through primary completion which may take 12 months.
Area under the ROC curve for the predictive model in identifying prostate cancer within the PSA grey zone cohort.
Time frame: Through primary completion which may take 12 months.
Sensitivity of the predictive model in identifying prostate cancer within the PSA grey zone cohort.
Time frame: 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.
Time frame: Through primary completion which may take 12 months.
Duocai Li
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