The goal of this real-world prospective diagnostic study is to comprehensively evaluate the value of MRI artificial intelligence (MRI-AI) in assisting the diagnosis of prostate cancer (PCa). The main questions it aims to answer are: Does MRI-AI promote the accurate diagnosis and treatment of prostate cancer? What's the capability of prostate MRI-AI in calculating the prostate volumn? What's the value of prostate MRI-AI assistant diagnosis system in detecting the suspicious lesions on MRI and guiding prostate targeted biopsy? What's the value of prostate MRI-AI assistant diagnosis system in predicting the pathological results of prostate targeted biopsy? Researchers will compare the cancer detection rates of suspicious lesions detected by MRI-AI and senior radiologists. Participants will: Receive combination of systematic biopsy and targeted biopsy.
In recent years, there have been remarkable advancements in the field of artificial intelligence (AI) techniques, particularly in the medical domain. These AI techniques have demonstrated the ability to significantly enhance various medical tasks, such as tumor detection, classification, and prognosis prediction. Increasing evidence supports the ability of AI to facilitate precise diagnosis of PCa and assist in therapeutic decisions. Compared with doctors, AI has the potential to identify not only holistic tumor morphology but also task-specific and granular radiological patterns that cannot be detected by the naked eye. Therefore, AI has great potential to reduce inconsistencies between observers and improve diagnostic accuracy. Previous AI studies at our institution have developed deep learning-based AI models trained on MR images that achieve good performance in the detection and localization of clinically significant prostate cancer (csPCa). Furthermore, the trained AI algorithms were embedded into proprietary structured reporting software, and radiologists simulated their real-life work scenarios to interpret and report the PI-RADS category of each patient using this AI-based software. However, the data is mostly retrospective. The capability of detecting the suspicious lesions on MRI, guiding the prostate targeted biopsy, and optimizing the biopsy scheme warrants further investigation. The goal of this real-world prospective diagnostic study is to comprehensively evaluate the value of MRI artificial intelligence (MRI-AI) in assisting the diagnosis of prostate cancer (PCa). The main questions it aims to answer are: Does MRI-AI promote the accurate diagnosis and treatment of prostate cancer? What's the capability of prostate MRI-AI in calculating the prostate volumn? What's the value of prostate MRI-AI assistant diagnosis system in detecting the suspicious lesions on MRI and guiding prostate targeted biopsy? What's the value of prostate MRI-AI assistant diagnosis system in predicting the pathological results of prostate targeted biopsy? Researchers will compare the cancer detection rates of suspicious lesions detected by MRI-AI and senior radiologists. Participants will: Receive combination of systematic biopsy and targeted biopsy.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
365
Before prostate biopsy, the MR images of patients were independently reviewed by MRI-AI and urogenital radiologists. Then the images with suspicious lesions highlighted by MRI-AI and urogenital radiologists. Urologists conducted targeted biopsies for all suspicious lesions and systematic biopsies. Biopsies were performed under the guidance of transrectal ultrasound (TRUS) through the transrectal or transperineal route.
Peking University First Hospital
Beijing, Beijing Municipality, China
The clinically significant prostate cancer (csPCa) detection rate for suspicious lesions found by MRI-AI and urogenital radiologists
csPCa was defined as PCa with a grade group ≥ 2 or GS ≥ 3+4. The reference standard was the pathological results of targeted biopsies for the suspicious lesions.
Time frame: One month after the biopsy procedure.
High-grade PCa detection rate
High-grade PCa was defined as PCa with a grade group ≥3 or GS ≥ 4+3. The reference standard was the pathological results of targeted biopsies for the suspicious lesions.
Time frame: One month after the biopsy procedure.
The PCa detection rate
The PCa detection rate for the suspicious lesions found by MRI-AI and urogenital radiologists.
Time frame: One month after the biopsy procedure.
clinically insignificant PCa (ciPCa) detection rate
ciPCa was defined as PCa with a grade group=1 or GS=3+3. The reference standard was the pathological results of targeted biopsies for the suspicious lesions.
Time frame: One month after the biopsy procedure.
Diagnostic performance
Diagnostic performance assessment includes accuracy, sensitivity, specificity, negative predicative value, and positive predicative value
Time frame: One month after the biopsy procedure
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