- ICH GCP
- 미국 임상 시험 레지스트리
- 임상시험 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
연구 개요
상세 설명
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.
연구 유형
등록 (추정된)
연락처 및 위치
연구 연락처
- 이름: Caigou Shi, MD
- 전화번호: +86 13677729003
- 이메일: shicaigou@sr.gxmu.edu.cn
연구 장소
-
-
Guangxi
-
Liuzhou, Guangxi, 중국, 545006
- 모병
- Liuzhou People's Hospital Affiliated to Guangxi Medical University
-
연락하다:
- Caigou Shi, MD
- 전화번호: +86 13677729003
- 이메일: shicaigou@sr.gxmu.edu.cn
-
-
참여기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
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.
설명
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).
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Area Under the Receiver Operating Characteristic Curve (AUC) for predicting clinically significant prostate cancer (csPCa)
기간: Baseline (at the time of imaging/pathology data collection)
|
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.
|
Baseline (at the time of imaging/pathology data collection)
|
공동 작업자 및 조사자
수사관
- 수석 연구원: Fubo Wang, MD, Guangxi Medical University
간행물 및 유용한 링크
일반 간행물
- 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.
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (추정된)
연구 완료 (추정된)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
추가 관련 MeSH 약관
기타 연구 ID 번호
- GXMU-PCa-AI-KY20260016
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .
전립선암(진단)에 대한 임상 시험
-
Georgetown UniversityNational Cancer Institute (NCI); American Cancer Society, Inc.; Susan G. Komen Breast Cancer...완전한
-
University of UtahNational Cancer Institute (NCI)완전한피로 | 좌식 생활 | 전이성 전립선암 | IV기 전립선암 AJCC(American Joint Committee on Cancer) v8 | IVA기 전립선암 AJCC(American Joint Committee on Cancer) v8 | IVB기 전립선암 AJCC(American Joint Committee on Cancer) v8미국
-
Weill Medical College of Cornell UniversityMillennium Pharmaceuticals, Inc.완전한신경내분비성 전립선암 | 소세포 전립선암 | Prostate Adenocarcinoma Plus > 신경내분비 표지자에 대한 50% 면역조직화학적 염색미국
-
SB Istanbul Education and Research Hospital아직 모집하지 않음Thryoid cancer | parathyrıoid 선종
-
Jonsson Comprehensive Cancer CenterNovartis Pharmaceuticals모병전립선암 | IVB기 전립선암 American Joint Committee on Cancer(AJCC) v8미국
-
Jonsson Comprehensive Cancer Center모병전립선 선암종 | 2기 전립선암 AJCC v8 | 1기 전립선암 American Joint Committee on Cancer(AJCC) v8미국
-
Jonsson Comprehensive Cancer Center빼는전립선 선암종 | 2기 전립선암 AJCC v8 | IIC기 전립선암 AJCC v8 | IIA기 전립선암 AJCC v8 | IIB기 전립선암 AJCC v8 | 1기 전립선암 American Joint Committee on Cancer(AJCC) v8미국
-
Jonsson Comprehensive Cancer CenterMiraDX모집하지 않고 적극적으로전립선 선암종 | 2기 전립선암 AJCC v8 | IIC기 전립선암 AJCC v8 | IIA기 전립선암 AJCC v8 | IIB기 전립선암 AJCC v8 | 1기 전립선암 American Joint Committee on Cancer(AJCC) v8미국
-
Society for Endocrinology초대로 등록
-
Jonsson Comprehensive Cancer Center모병거세저항성 전립선암 | 전이성 전립선암 | IVA기 전립선암 AJCC v8 | IVB기 전립선암 AJCC v8 | IV기 전립선암 American Joint Committee on Cancer(AJCC) v8미국
No Intervention: Observational Cohort에 대한 임상 시험
-
Guangzhou Women and Children's Medical Center아직 모집하지 않음NEC - 괴사성 장염
-
Monash UniversityThe Alfred; Melbourne Sexual Health Centre모병매독중국, 호주, 남아프리카, 영국
-
IRCCS San Raffaele모집하지 않고 적극적으로
-
University of CagliariUniversity of Milano Bicocca; University of Milan; University of Cagliari, Cagliari, Italy; Università Cattolica del Sacro Cuore, Rome, Italy모병연조직 감염 | Fournier 괴저 | 괴사성 근막염 | Fournier의 괴저이탈리아
-
IRCCS San Raffaele아직 모집하지 않음
-
PeriPharm완전한