Development and Validation of a Non-Invasive AI Diagnostic Model for Prostate Cancer Using Multiparametric MRI and PSMA PET/CT
A Retrospective, Multicenter Study to Develop and Validate a Non-Invasive Artificial Intelligence Diagnostic Model for Prostate Cancer Using Multiparametric MRI and PSMA PET/CT, With Pathologically Confirmed Diagnosis as the Reference Standard
Visão geral do estudo
Status
Status
Condições
Condições
Descrição detalhada
Study Design:
This is a retrospective and prospective, multicenter, case-control study. The study aims to develop and validate a non-invasive artificial intelligence (AI) diagnostic model for prostate cancer by integrating multiparametric MRI (mpMRI) and PSMA PET/CT imaging.
Participants:
Patients who underwent mpMRI, PSMA PET/CT, and prostate biopsy or radical prostatectomy at participating centers will be retrospectively enrolled. Eligible participants include pathologically confirmed prostate cancer patients (cases) and benign prostatic hyperplasia (BPH) patients (controls). Inclusion criteria include age ≥18 years, ECOG performance status 0-2, life expectancy >6 months, and availability of complete clinical data (PSA, Gleason score, PI-RADS, SUVmax, prostate volume, etc.). Exclusion criteria include prior prostate cancer treatment (endocrine therapy or radiotherapy), previous prostate surgery (e.g., TURP), severe renal insufficiency, other malignancies. Informed consent is waived for retrospective patients, while signed informed consent is required for prospective patients.
Sample Size:
Approximately 1,000 to 1,500 participants will be enrolled from six hospitals in China: Xiangya Hospital of Central South University, Qilu Hospital of Shandong University, Chinese PLA General Hospital, The First Affiliated Hospital of Guangzhou Medical University, Beijing Hospital and Renji Hospital, Shanghai Jiao Tong University School of Medicine.
AI Model Development:
The AI model will be developed using deep learning and radiomics techniques. mpMRI sequences (including T2-weighted, DWI/ADC, and DCE) and PSMA PET/CT images will be preprocessed, coregistered, and fused. The model will be trained to distinguish clinically significant prostate cancer (csPCa) from non-csPCa or benign conditions. The reference standard is histopathology from prostate biopsy or radical prostatectomy.
Outcome Measures:
Primary outcome measures include the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the AI model for csPCa detection. Secondary outcome measures include positive predictive value (PPV), negative predictive value (NPV), overall accuracy, decision curve analysis (DCA) net benefit, diagnostic performance in the PSA gray zone (4-20 ng/mL) subgroup, and the proportion of patients who could avoid biopsy at 100% specificity threshold.
Statistical Analysis:
Model performance will be evaluated using internal cross-validation and external validation on data from different centers. Calibration curves and decision curve analysis will be used to assess clinical utility. Subgroup analyses will be performed for PSA gray zone patients and different Gleason grade groups.
Tipo de estudo
Tipo de estudo
Inscrição (Estimado)
Inscrição
Contactos e Locais
Locais de estudo
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Beijing Municipality
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Beijing, Beijing Municipality, China, 100853
- Chinese PLA General Hospital
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Beijing, Beijing Municipality, China, 100730
- Beijing Hospital
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Guangdong
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Guangzhou, Guangdong, China, 510120
- The First Affiliated Hospital of Guangzhou Medical University
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Shandong
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Jinan, Shandong, China, 250012
- Qilu Hospital of Shandong University
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Shanghai Municipality
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Shanghai, Shanghai Municipality, China, 200127
- Renji Hospital, Shanghai Jiao Tong University School of Medicine
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California
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Los Angeles, California, Estados Unidos, 90095
- David Geffen School of Medicine University of California, Los Angeles(UCLA)
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Critérios de participação
Critérios de elegibilidade
Critérios de elegibilidade
Idades elegíveis para estudo
- Adulto
- Adulto mais velho
Aceita Voluntários Saudáveis
Método de amostragem
População do estudo
Descrição
Inclusion Criteria:
- Age ≥ 18 years.
- ECOG performance status 0-2.
- Life expectancy > 6 months.
- Underwent mpMRI and PSMA PET/CT before systemic treatment or radical prostatectomy, with original DICOM data available for export.
- Has pathological diagnosis from prostate biopsy or radical prostatectomy as the gold standard.
- Complete clinical data available, including pre-treatment PSA (tPSA, fPSA), TNM stage, Gleason score, PI-RADS score, SUVmax, prostate volume (from MRI/PSMA PET), age, and BMI.
- Informed consent for data use for research purposes according to each center's ethics requirements.
Exclusion Criteria:
- History of other malignant tumors
- Previous prostate surgery (e.g., TURP)
- Prior endocrine therapy or radiotherapy
- Severe renal insufficiency
- Major organ dysfunction or life expectancy < 1 year
Plano de estudo
Como o estudo é projetado?
Detalhes do projeto
Número de grupos/coortes
Coortes e Intervenções
Grupo / CoorteGrupo / Coorte |
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Grupo de Câncer de Próstata
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Non-cancer (BPH) Control
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O que o estudo está medindo?
Medidas de resultados primários
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
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Area Under the Curve (AUC) of the AI Model for Detecting Clinically Significant Prostate Cancer
Prazo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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The AUC (Area Under the Receiver Operating Characteristic Curve) will be calculated to evaluate the overall diagnostic performance of the AI model in distinguishing clinically significant prostate cancer (csPCa) from non-csPCa or benign conditions.
The gold standard is histopathology from prostate biopsy or radical prostatectomy.
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At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Specificity of the AI Model for Detecting Clinically Significant Prostate Cancer
Prazo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Specificity (true negative rate) will be calculated to evaluate the model's ability to correctly identify patients without clinically significant prostate cancer.
High specificity is a primary goal to reduce unnecessary prostate biopsies.
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At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Sensitivity of the AI Model for Detecting Clinically Significant Prostate Cancer
Prazo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Sensitivity (true positive rate) will be calculated to evaluate the model's ability to correctly identify patients with clinically significant prostate cancer.
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At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Medidas de resultados secundários
Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
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Overall Diagnostic Accuracy
Prazo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Overall accuracy (proportion of true results among all cases) will be calculated for the AI model in detecting clinically significant prostate cancer.
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At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Net Benefit of the AI Model for Detecting Clinically Significant Prostate Cancer
Prazo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Net benefit will be calculated across a range of threshold probabilities to evaluate the clinical utility of the AI model for detecting clinically significant prostate cancer, using histopathological diagnosis from prostate biopsy or radical prostatectomy as the reference standard.
Results will be compared with default strategies of biopsying all patients or no patients.
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At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Proportion of Patients Who Could Avoid Biopsy at 100% Specificity Threshold
Prazo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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At a specificity of 100% (no false positives), the proportion of patients who would be correctly identified as non-csPCa and thus could safely avoid prostate biopsy will be calculated.
Sensitivity at this threshold will also be reported.
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At histopathological diagnosis by prostate biopsy or radical prostatectomy
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AUC in PSA Gray Zone (4-20 ng/mL)
Prazo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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The area under the receiver operating characteristic curve (AUC) of the AI model will be evaluated in patients with PSA levels between 4 and 20 ng/mL to assess discrimination between clinically significant prostate cancer and non-clinically significant or benign disease, using histopathological diagnosis as the reference standard.
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At histopathological diagnosis by prostate biopsy or radical prostatectomy
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Specificity in PSA Gray Zone (4-20 ng/mL)
Prazo: At histopathological diagnosis (prostate biopsy or radical prostatectomy)
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Specificity of the AI model will be evaluated in patients with PSA levels between 4 and 20 ng/mL using histopathological diagnosis as the reference standard.
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At histopathological diagnosis (prostate biopsy or radical prostatectomy)
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Outras medidas de resultado
Outras medidas de resultado
Medida de resultado |
Descrição da medida |
Prazo |
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Sensitivity in PSA Gray Zone (4-20 ng/mL)
Prazo: At histopathological diagnosis (prostate biopsy or radical prostatectomy)
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Sensitivity of the AI model will be evaluated in patients with PSA levels between 4 and 20 ng/mL, using histopathological diagnosis as the reference standard.
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At histopathological diagnosis (prostate biopsy or radical prostatectomy)
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Colaboradores e Investigadores
Patrocinador
Patrocinador
Colaboradores
Colaboradores
Datas de registro do estudo
Datas Principais do Estudo
Início do estudo (Real)
Início do estudo
Conclusão Primária (Estimado)
Conclusão Primária
Conclusão do estudo (Estimado)
Conclusão do estudo
Datas de inscrição no estudo
Enviado pela primeira vez
Enviado pela primeira vez
Enviado pela primeira vez que atendeu aos critérios de CQ
Enviado pela primeira vez que atendeu aos critérios de CQ
Primeira postagem (Real)
Primeira postagem
Atualizações de registro de estudo
Última Atualização Postada (Real)
Última Atualização Postada
Última atualização enviada que atendeu aos critérios de controle de qualidade
Última atualização enviada que atendeu aos critérios de controle de qualidade
Última verificação
Última verificação
Mais Informações
Termos relacionados a este estudo
Palavras-chave
Termos MeSH relevantes adicionais
Outros números de identificação do estudo
Outros números de identificação do estudo
- 2026010083_2
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