- ICH GCP
- Registro de ensayos clínicos de EE. UU.
- Ensayo clínico NCT07614256
Multimodal Imaging and Digital Pathology for Prostate Cancer Prediction
A Multicenter Study of a Deep Learning Model Based on Spatial Registration of Multimodal Imaging and Digital Pathology for Predicting Clinically Significant Prostate Cancer
Descripción general del estudio
Estado
Intervención / Tratamiento
Descripción detallada
This prospective and retrospective multicenter observational study enrolls patients with suspected prostate cancer who receive standardized preoperative multiparametric magnetic resonance imaging, transrectal ultrasound examination, followed by prostate biopsy or radical prostatectomy. Complete clinical data including age, BMI, prostate specific antigen indicators, PI-RADS v2.1 scores, Gleason score and ISUP grading are collected from all eligible participants.
Biomechanically constrained non-rigid spatial registration technique is applied to achieve precise alignment between preoperative multimodal images and postoperative digital pathological whole slide images using high-quality multicenter datasets. A transformer-based multimodal deep learning fusion model is developed to analyze correlations between macroscopic imaging features and microscopic pathological heterogeneity, thereby establishing an interpretable artificial intelligence framework for clinically significant prostate cancer prediction.
Comprehensive model validation is conducted via internal cross-validation, external multicenter independent verification and international public datasets. Decision curve analysis and clinical impact curve are applied to assess clinical applicability. The model serves as an intelligent auxiliary tool to refine biopsy strategies, avoid redundant puncture and excessive treatment, and facilitate early precise diagnosis and risk stratification of prostate cancer.
Tipo de estudio
Inscripción (Estimado)
Contactos y Ubicaciones
Estudio Contacto
- Nombre: Caigou Shi, MD
- Número de teléfono: +86 13677729003
- Correo electrónico: shicaigou@sr.gxmu.edu.cn
Ubicaciones de estudio
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Guangxi
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Liuzhou, Guangxi, Porcelana, 545006
- Reclutamiento
- Liuzhou People's Hospital Affiliated to Guangxi Medical University
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Contacto:
- Caigou Shi, MD
- Número de teléfono: +86 13677729003
- Correo electrónico: shicaigou@sr.gxmu.edu.cn
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Método de muestreo
Población de estudio
This is a prospective and retrospective multicenter cohort study. The study population consists of consecutive male subjects aged 40-90 years who are scheduled to undergo or have undergone prostate biopsy or radical prostatectomy, with complete standard-of-care preoperative multiparametric MRI (mpMRI), transrectal ultrasound (TRUS) images, and corresponding pathological diagnosis results. The collected data include:
- Preoperative mpMRI and TRUS images
- Digital whole-slide images of prostate biopsy specimens
- Digital whole-slide images of radical prostatectomy specimens (if performed) The prospective cohort will include newly enrolled subjects who provide written informed consent, while the retrospective cohort will include historical subjects with complete imaging, pathology slide, and clinical data from participating centers.
Descripción
Inclusion Criteria:
- Subjects who are scheduled to undergo or have undergone prostate biopsy or radical prostatectomy.
- Subjects who have completed standard-of-care preoperative multiparametric MRI (mpMRI) and transrectal ultrasound (TRUS) examinations.
- Subjects with complete pathological diagnosis results available.
- Age between 40 and 90 years.
- Able and willing to provide written informed consent (for prospective cohort participants only).
Exclusion Criteria:
- Prior history of pelvic radiation therapy or radical prostatectomy.
- Incomplete or poor-quality mpMRI or TRUS images (e.g., motion artifacts, insufficient sequences).
- Concurrent other primary malignant tumors.
- Severe systemic diseases that may affect the evaluation of the prostate.
- Subjects with incomplete clinical or pathological data.
- Contraindications to MRI examination (e.g., incompatible metallic implants, severe claustrophobia).
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Area Under the Receiver Operating Characteristic Curve (AUC) for predicting clinically significant prostate cancer (csPCa)
Periodo de tiempo: Baseline (at the time of imaging/pathology data collection)
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The diagnostic performance of the multimodal deep learning model in predicting clinically significant prostate cancer using preoperative imaging data from this prospective and retrospective multicenter cohort.
The AUC will be calculated to evaluate the model's discriminative ability.
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Baseline (at the time of imaging/pathology data collection)
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Colaboradores e Investigadores
Patrocinador
Investigadores
- Investigador principal: Fubo Wang, MD, Guangxi Medical University
Publicaciones y enlaces útiles
Publicaciones Generales
- Zeng H, Chen W, Zheng R, Zhang S, Ji JS, Zou X, Xia C, Sun K, Yang Z, Li H, Wang N, Han R, Liu S, Li H, Mu H, He Y, Xu Y, Fu Z, Zhou Y, Jiang J, Yang Y, Chen J, Wei K, Fan D, Wang J, Fu F, Zhao D, Song G, Chen J, Jiang C, Zhou X, Gu X, Jin F, Li Q, Li Y, Wu T, Yan C, Dong J, Hua Z, Baade P, Bray F, Jemal A, Yu XQ, He J. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. Lancet Glob Health. 2018 May;6(5):e555-e567. doi: 10.1016/S2214-109X(18)30127-X.
- Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. doi: 10.3322/caac.21834. Epub 2024 Apr 4.
- Shao L, Liang C, Yan Y, Zhu H, Jiang X, Bao M, Zang P, Huang X, Zhou H, Nie P, Wang L, Li J, Zhang S, Ren S. An MRI-pathology foundation model for noninvasive diagnosis and grading of prostate cancer. Nat Cancer. 2025 Oct;6(10):1621-1637. doi: 10.1038/s43018-025-01041-x. Epub 2025 Sep 2.
- Rusu M, Jahanandish H, Vesal S, Li CX, Bhattacharya I, Venkataraman R, Zhou SR, Kornberg Z, Sommer ER, Khandwala YS, Hockman L, Zhou Z, Choi MH, Ghanouni P, Fan RE, Sonn GA. ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies. Eur Urol Oncol. 2025 Apr;8(2):477-485. doi: 10.1016/j.euo.2024.12.012. Epub 2025 Jan 28.
- Saha A, Hosseinzadeh M, Huisman H. End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction. Med Image Anal. 2021 Oct;73:102155. doi: 10.1016/j.media.2021.102155. Epub 2021 Jun 29.
- Lee YJ, Moon HW, Choi MH, Eun Jung S, Park YH, Lee JY, Kim DH, Eun Rha S, Kim SH, Lee KW, Choi YJ, Lee YS, Lee W, Lee S, Grimm R, von Busch H, Han D, Lou B, Kamen A. MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study. Radiology. 2025 Mar;314(3):e232788. doi: 10.1148/radiol.232788.
- Twilt JJ, Saha A, Bosma JS, Padhani AR, Bonekamp D, Giannarini G, van den Bergh R, Kasivisvanathan V, Obuchowski N, Yakar D, Elschot M, Veltman J, Futterer J, Huisman H, de Rooij M; PI-CAI Consortium. AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images. JAMA Netw Open. 2025 Jun 2;8(6):e2515672. doi: 10.1001/jamanetworkopen.2025.15672.
- Goel S, Shoag JE, Gross MD, Al Hussein Al Awamlh B, Robinson B, Khani F, Baltich Nelson B, Margolis DJ, Hu JC. Concordance Between Biopsy and Radical Prostatectomy Pathology in the Era of Targeted Biopsy: A Systematic Review and Meta-analysis. Eur Urol Oncol. 2020 Feb;3(1):10-20. doi: 10.1016/j.euo.2019.08.001. Epub 2019 Sep 4.
- Pham THN, Schulze-Hagen MF, Rahnama'i MS. Targeted multiparametric magnetic resonance imaging/transrectal ultrasound-guided (mpMRI/TRUS) fusion prostate biopsy versus systematic random prostate biopsy: A comparative real-life study. Cancer Rep (Hoboken). 2024 Feb;7(2):e1962. doi: 10.1002/cnr2.1962. Epub 2024 Jan 12.
- Drost FH, Osses DF, Nieboer D, Steyerberg EW, Bangma CH, Roobol MJ, Schoots IG. Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev. 2019 Apr 25;4(4):CD012663. doi: 10.1002/14651858.CD012663.pub2.
- Moliere S, Hamzaoui D, Ploussard G, Mathieu R, Fiard G, Baboudjian M, Granger B, Roupret M, Delingette H, Renard-Penna R. A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging. Eur Urol Oncol. 2025 Aug;8(4):1182-1202. doi: 10.1016/j.euo.2024.11.001. Epub 2024 Nov 14.
- Epstein JI, Amin MB, Reuter VE, Humphrey PA. Contemporary Gleason Grading of Prostatic Carcinoma: An Update With Discussion on Practical Issues to Implement the 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol. 2017 Apr;41(4):e1-e7. doi: 10.1097/PAS.0000000000000820.
- Schafer EJ, Laversanne M, Sung H, Soerjomataram I, Briganti A, Dahut W, Bray F, Jemal A. Recent Patterns and Trends in Global Prostate Cancer Incidence and Mortality: An Update. Eur Urol. 2025 Mar;87(3):302-313. doi: 10.1016/j.eururo.2024.11.013. Epub 2024 Dec 11.
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Actual)
Finalización primaria (Estimado)
Finalización del estudio (Estimado)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
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Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
- Enfermedades urogenitales
- Enfermedades Genitales
- Procesos Patológicos
- Neoplasias Genitales Masculinas
- Neoplasias urogenitales
- Neoplasias por sitio
- Neoplasias
- Enfermedades Genitales Masculinas
- Enfermedades prostáticas
- Enfermedades urogenitales masculinas
- Condiciones Patológicas, Signos y Síntomas
- Neoplasias prostáticas
- Enfermedad
Otros números de identificación del estudio
- GXMU-PCa-AI-KY20260016
Plan de datos de participantes individuales (IPD)
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Descripción del plan IPD
Información sobre medicamentos y dispositivos, documentos del estudio
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