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NCT07631806
Intelligent Screening and Precision Diagnosis of Prostate Cancer Based on Multimodal Data
Conditions: Prostate Cancer With PSA Gray Zone
Sex: Male
Ages: 45 Years – N/A
Healthy volunteers: Yes
Enrollment: 500
Sponsor: Guangxi Medical University
Summary
This project aims to develop a precision screening and diagnostic solution for prostate cancer based on multimodal artificial intelligence, focusing on addressing the diagnostic challenge in patients within the PSA "gray zone" of 4-10 ng/mL. The project will integrate multidimensional information including ctDNA liquid biopsy, routine laboratory data, and prostate ultrasound images to develop three models: a ctDNA-based multimodal AI prediction model, a routine laboratory data-assisted decision model, and an ultrasound image AI-assisted diagnostic model. On this basis, a multimodal AI fusion decision system will be established to automatically generate individualized risk assessment reports and diagnostic recommendations. Additionally, a closed-loop mechanism of "clinical use - data feedback - model optimization" will be constructed to continuously iterate model parameters using pathological gold standards, thereby improving predictive accuracy in our hospital population. The project will form a generalizable precision diagnostic workflow, reduce unnecessary biopsies in "gray zone" patients, and provide an implementable in-hospital solution for precision medicine in prostate cancer.
Eligibility Criteria
Inclusion Criteria:
1. Age ≥45 years, male
2. Presenting with abnormal serum PSA (≥4 ng/mL), abnormal digital rectal examination, or suspicious lesions on prostate ultrasound
3. Undergoing prostate biopsy with definitive pathological results
4. Signed informed consent
Exclusion Criteria:
1. Previously diagnosed with prostate cancer and receiving surgery, radiotherapy, or endocrine therapy
2. With other malignancies
3. Critical missing clinical data (e.g., missing PSA value, incomplete ultrasound report)
Source: ClinicalTrials.gov (NCT07631806). StuddyBuddy aggregates publicly available trial information.