This study aims to investigate a novel Centre-Adaptive Multi-Modal Fusion Framework (Cad-MMFF) that integrates clinical, ultrasound, and MRI data to improve the detection of clinically significant prostate cancer (csPCa), reduce unnecessary biopsies, and optimize MRI utilization.
Approximately 400 Chinese men aged 50 years or above with suspected prostate cancer will be included.The study consists of retrospective model development and prospective model optimization and validation. Two AI models will be developed: a Clinical-US model using clinical and ultrasound data, and a Clinical-US-MRI model incorporating MRI information. The optimized models will subsequently be validated in an independent cohort.
Study Type
OBSERVATIONAL
Enrollment
400
Prince of Wales Hospital
Hong Kong, Hong Kong
North District Hospital
Sheung Shui, Hong Kong
Area under Receiver-Operating-Curve of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time frame: Through study completion, an average of 1 year
The number of unnecessary biopsy rate decreased of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time frame: Through study completion, an average of 1 year
Proportion of MRI safely saved of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time frame: Through study completion, an average of 1 year
Net-Benefit of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
By Decision Curve analysis
Time frame: Through study completion, an average of 1 year
Missed rate of clinically significant prostate cancer of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time frame: Through study completion, an average of 1 year
Detection rate of indolent prostate cancer of using the Centre-adaptive multi-modal fusion framework (Cad-MMFF)
Time frame: Through study completion, an average of 1 year
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