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Development and Validation of a Non-Invasive AI Diagnostic Model for Prostate Cancer Using Multiparametric MRI and PSMA PET/CT

14 de septiembre de 2026 actualizado por: Xiangya Hospital of Central South University

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

Prostate cancer is one of the most common malignancies in men. Currently, due to the limited diagnostic accuracy of existing imaging tests, there is a risk of missed diagnosis or unnecessary prostate biopsy. This study aims to develop and validate a non-invasive artificial intelligence (AI) diagnostic model using two advanced imaging techniques: multiparametric MRI (mpMRI) and PSMA PET/CT. By integrating information from both imaging modalities, the AI model is expected to improve the diagnostic accuracy of prostate cancer, reduce unnecessary biopsies, and assist physicians in making better clinical decisions. This is a retrospective, multicenter study that plans to collect imaging and pathology data from approximately 1,000 to 1,500 patients across six major hospitals in China. The diagnostic performance of the model will be evaluated, including its ability to identify clinically significant prostate cancer and its value in assisting diagnosis in patients with PSA levels in the gray zone (4-20 ng/mL).

Descripción general del estudio

Estado

Inscripción por invitación

Condiciones

Descripción detallada

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 estudio

De observación

Inscripción (Estimado)

1500

Contactos y Ubicaciones

Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.

Ubicaciones de estudio

  • Estados Unidos
    • California
      • Los Angeles, California, Estados Unidos, 90095
        • David Geffen School of Medicine University of California, Los Angeles(UCLA)
  • Porcelana
    • Beijing Municipality
      • Beijing, Beijing Municipality, Porcelana, 100853
        • Chinese PLA General Hospital
      • Beijing, Beijing Municipality, Porcelana, 100730
        • Beijing Hospital
    • Guangdong
      • Guangzhou, Guangdong, Porcelana, 510120
        • The First Affiliated Hospital of Guangzhou Medical University
    • Shandong
      • Jinan, Shandong, Porcelana, 250012
        • Qilu Hospital of Shandong University
    • Shanghai Municipality
      • Shanghai, Shanghai Municipality, Porcelana, 200127
        • Renji Hospital, Shanghai Jiao Tong University School of Medicine

Criterios de participación

Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.

Criterio de elegibilidad

Edades elegibles para estudiar

  • Adulto
  • Adulto Mayor

Acepta Voluntarios Saludables

No

Método de muestreo

Muestra no probabilística

Población de estudio

This study will enroll patients who underwent mpMRI, PSMA PET/CT, and prostate biopsy or radical prostatectomy at six major 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. The study population includes two groups: patients with pathologically confirmed prostate cancer (cases) and patients with pathologically confirmed benign prostatic hyperplasia (BPH, controls). All participants are male, aged 18 years or older, with no prior prostate cancer treatment (endocrine therapy or radiotherapy) or prostate surgery. Patients with severe renal insufficiency or other malignancies are excluded. A total of approximately 1,000 to 1,500 participants will be enrolled retrospectively from the participating centers.

Descripción

Inclusion Criteria:

  1. Age ≥ 18 years.
  2. ECOG performance status 0-2.
  3. Life expectancy > 6 months.
  4. Underwent mpMRI and PSMA PET/CT before systemic treatment or radical prostatectomy, with original DICOM data available for export.
  5. Has pathological diagnosis from prostate biopsy or radical prostatectomy as the gold standard.
  6. 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.
  7. Informed consent for data use for research purposes according to each center's ethics requirements.

Exclusion Criteria:

  1. History of other malignant tumors
  2. Previous prostate surgery (e.g., TURP)
  3. Prior endocrine therapy or radiotherapy
  4. Severe renal insufficiency
  5. Major organ dysfunction or life expectancy < 1 year

Plan de estudios

Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.

¿Cómo está diseñado el estudio?

Detalles de diseño

Cohortes e Intervenciones

Grupo / Cohorte
Grupo de cáncer de próstata
Non-cancer (BPH) Control

¿Qué mide el estudio?

Medidas de resultado primarias

Medida de resultado
Medida Descripción
Periodo de tiempo
Area Under the Curve (AUC) of the AI Model for Detecting Clinically Significant Prostate Cancer
Periodo de tiempo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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.
At histopathological diagnosis by prostate biopsy or radical prostatectomy
Specificity of the AI Model for Detecting Clinically Significant Prostate Cancer
Periodo de tiempo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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.
At histopathological diagnosis by prostate biopsy or radical prostatectomy
Sensitivity of the AI Model for Detecting Clinically Significant Prostate Cancer
Periodo de tiempo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Sensitivity (true positive rate) will be calculated to evaluate the model's ability to correctly identify patients with clinically significant prostate cancer.
At histopathological diagnosis by prostate biopsy or radical prostatectomy

Medidas de resultado secundarias

Medida de resultado
Medida Descripción
Periodo de tiempo
Overall Diagnostic Accuracy
Periodo de tiempo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Overall accuracy (proportion of true results among all cases) will be calculated for the AI model in detecting clinically significant prostate cancer.
At histopathological diagnosis by prostate biopsy or radical prostatectomy
Net Benefit of the AI Model for Detecting Clinically Significant Prostate Cancer
Periodo de tiempo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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.
At histopathological diagnosis by prostate biopsy or radical prostatectomy
Proportion of Patients Who Could Avoid Biopsy at 100% Specificity Threshold
Periodo de tiempo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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.
At histopathological diagnosis by prostate biopsy or radical prostatectomy
AUC in PSA Gray Zone (4-20 ng/mL)
Periodo de tiempo: At histopathological diagnosis by prostate biopsy or radical prostatectomy
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.
At histopathological diagnosis by prostate biopsy or radical prostatectomy
Specificity in PSA Gray Zone (4-20 ng/mL)
Periodo de tiempo: At histopathological diagnosis (prostate biopsy or radical prostatectomy)
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.
At histopathological diagnosis (prostate biopsy or radical prostatectomy)

Otras medidas de resultado

Medida de resultado
Medida Descripción
Periodo de tiempo
Sensitivity in PSA Gray Zone (4-20 ng/mL)
Periodo de tiempo: At histopathological diagnosis (prostate biopsy or radical prostatectomy)
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.
At histopathological diagnosis (prostate biopsy or radical prostatectomy)

Colaboradores e Investigadores

Aquí es donde encontrará personas y organizaciones involucradas en este estudio.

Patrocinador

Xiangya Hospital of Central South University

Colaboradores

Chinese PLA General Hospital
RenJi Hospital
Qilu Hospital of Shandong University
The First Affiliated Hospital of Guangzhou Medical University

Fechas de registro del estudio

Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados ​​por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.

Fechas importantes del estudio

Inicio del estudio (Actual)

13 de enero de 2026

Finalización primaria (Estimado)

30 de septiembre de 2026

Finalización del estudio (Estimado)

31 de diciembre de 2026

Fechas de registro del estudio

Enviado por primera vez

16 de junio de 2026

Primero enviado que cumplió con los criterios de control de calidad

1 de julio de 2026

Publicado por primera vez (Actual)

8 de julio de 2026

Actualizaciones de registros de estudio

Última actualización publicada (Actual)

15 de septiembre de 2026

Última actualización enviada que cumplió con los criterios de control de calidad

14 de septiembre de 2026

Última verificación

1 de mayo de 2026

Más información

Términos relacionados con este estudio

Palabras clave

Términos MeSH relevantes adicionales

Otros números de identificación del estudio

  • 2026010083_2

Plan de datos de participantes individuales (IPD)

¿Planea compartir datos de participantes individuales (IPD)?

NO

Información sobre medicamentos y dispositivos, documentos del estudio

Estudia un producto farmacéutico regulado por la FDA de EE. UU.

No

Estudia un producto de dispositivo regulado por la FDA de EE. UU.

No

Esta información se obtuvo directamente del sitio web clinicaltrials.gov sin cambios. Si tiene alguna solicitud para cambiar, eliminar o actualizar los detalles de su estudio, comuníquese con register@clinicaltrials.gov. Tan pronto como se implemente un cambio en clinicaltrials.gov, también se actualizará automáticamente en nuestro sitio web. .

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