Prospective Validation of an AI-Assisted Multimodal Imaging-Pathology Fusion System for Precision Diagnosis and Biopsy Guidance in Patients With Suspected Prostate Cancer
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 500
- 试验地点
- 1
研究概览
简要总结
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.
详细描述
Background: Prostate cancer (PCa) is the second most common malignancy in men worldwide. In China, the average annual growth rate of PCa incidence is as high as 7.2%. Current diagnostic pathways rely on transrectal ultrasound (TRUS)-guided prostate biopsy. However, serum PSA, the main decision-making indicator for biopsy, is not cancer-specific and has severely insufficient specificity. Many men with elevated PSA undergo unnecessary invasive biopsies. The diagnostic challenge is particularly prominent in the PSA "gray zone" of 4-10 ng/mL.
Objectives: This study 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. Specific objectives include: (1) improving screening efficiency to quickly identify high-risk individuals and avoid over-examination; (2) solving the diagnostic gray zone problem; (3) reducing unnecessary biopsies through non-invasive or minimally invasive precision tools; and (4) achieving personalized management through risk stratification.
Study Design: Prospective enrollment of suspected prostate cancer patients. Total sample size is no less than 500 cases, divided into training set (approximately 400 cases) and validation set (approximately 100 cases) at an 8:2 ratio.
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 45 Years 至 —（Adult, Older Adult）
- 性别
- Male
- 接受健康志愿者
- 是
入选标准
- •Age ≥45 years, male
- •Presenting with abnormal serum PSA (≥4 ng/mL), abnormal digital rectal examination, or suspicious lesions on prostate ultrasound
- •Undergoing prostate biopsy with definitive pathological results
- •Signed informed consent
排除标准
- •Previously diagnosed with prostate cancer and receiving surgery, radiotherapy, or endocrine therapy
- •With other malignancies
- •Critical missing clinical data (e.g., missing PSA value, incomplete ultrasound report)
研究组 & 干预措施
Validation Set
Approximately 100 cases. This independent validation set will be used to evaluate the diagnostic performance of the multimodal fusion decision system. Sensitivity, specificity, positive predictive value, negative predictive value, and AUC will be calculated using pathological results as the gold standard. DeLong test will be used to compare AUC with PSA alone. Decision curve analysis (DCA) will be used to evaluate clinical net benefit. Subgroup analysis will be performed for the PSA 4-10 ng/mL gray zone.
Training Set
Approximately 400 cases. This group will be used to develop and internally validate the three specialized models: (1) ctDNA multimodal AI prediction model, (2) routine laboratory data-assisted decision model, and (3) prostate ultrasound image AI-assisted diagnostic model. Five-fold cross-validation will be used for algorithm comparison and hyperparameter tuning.
研究者
Fubo Wang
Doctor of Medicine
Guangxi Medical University