방광암의 예후 예측을 위한 전체 슬라이드 영상 및 CT Radiomics 기반 딥러닝 시스템
Bladder cancer (BLCA), with its diverse histopathological features and varying patient outcomes, poses significant challenges in diagnosis and prognosis. Postoperative survival stratification based on radiomics feature and whole slide image feature may be useful for treatment decisions to improve prognosis. In this research, we aim to develop a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with BLCA.This observational study evaluates a deep learning system that combines CT images and digitized pathology slides to predict outcomes in patients with bladder cancer. It includes a retrospective cohort and a prospective cohort of newly enrolled patients. The prospective cohort will assess the system in patients enrolled during (2024-2026), with follow-up for survival and recurrence.
연구 개요
상태
상태
정황
정황
개입 / 치료
개입 / 치료
상세 설명
Study structure The study includes an initial retrospective phase and a subsequent prospective observational phase involving newly enrolled patients. The two phases are analyzed separately according to the specified analysis plan.
Retrospective phase The retrospective phase included patients from [2012-2024] and was used for model construction. Results from this phase were reported in npj series.
Prospective phase Enrollment of newly recruited patients began on [2024]. The prospective phase follows protocol, with ethics approval from the first affliated hospital of chongqing medical, university. Recruitment is consecutive, as specified in the protocol.
Model evaluation The prospective cohort is used for validation. The model version, preprocessing procedures and prediction thresholds used in this phase. Any model updating using this cohort is documented separately.
Registry update This update distinguishes the retrospective phase from the subsequently enrolled cohort. The prospective phase enrollment dates and the dates of protocol development and ethics approval are reported separately.
연구 유형
연구 유형
등록 (추정된)
등록
연락처 및 위치
연구 연락처
연구 연락처
- 이름: QuanHao He
- 전화번호: 800-555-5555
- 이메일: 2020120460@stu.cqmu.edu.cn
연구 연락처 백업
- 이름: Mingzhao Xiao, PHD
- 전화번호: 800-555-5555
- 이메일: 2023140134@stu.cqmu.edu.cn
연구 장소
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Chongqing Municipality
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Chongqing, Chongqing Municipality, 중국, 400016
- 모병
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University
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연락하다:
- QuanHao He, PHD
- 전화번호: 800-555-5555
- 이메일: 2020120460@stu.cqmu.edu.cn
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연락하다:
- Mingzhao Xiao, PHD
- 전화번호: 023-89012557
- 이메일: 2023140134@stu.cqmu.edu.cn
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참여기준
자격 기준
자격 기준
공부할 수 있는 나이
- 어린이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
샘플링 방법
연구 인구
We included patients who had surgery only or who had neoadjuvant chemotherapy before surgery. We excluded patients with a postoperative diagnosis of non-urothelial carcinoma.
설명
Inclusion Criteria:
- patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT)
- contrast-CT scan less than two weeks before surgery
- complete CT image data and clinical data
- complete whole slide image data
Exclusion Criteria:
- patients with a postoperative diagnosis of non-urothelial carcinoma
- poor quality of CT images
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 관찰 모델: COHORT
- 시간 관점: OTHER
그룹/코호트 수
코호트 및 개입
그룹/코호트그룹/코호트 |
개입 / 치료개입 / 치료 |
|---|---|
|
Retrospective cohort
Patients with bladder cancer included in the retrospective phase of the study, using clinical records, CT images, and whole-slide images obtained during [2012-2024]. This cohort was used for model development and/or validation and was reported in our npj publications. |
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images. |
|
Prospective cohort of newly enrolled patients
Newly enrolled patients with bladder cancer recruited at four centers between 2024 and2026. Eligibility is assessed at enrollment, and participants are followed according to protocol version and date for the study outcomes. This cohort is distinct from the previously reported retrospective cohort. |
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images. |
연구는 무엇을 측정합니까?
주요 결과 측정
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Overall survival
기간: up to 10 years
|
the time from the date of surgery to death from any cause or the date of last contact (censored observation) at the date of data cut-off. |
up to 10 years
|
2차 결과 측정
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Recurrence free survival
기간: up to 10 years
|
the time from the date of surgery to the date of first documented disease recurrence. Patients without recurrence at the time of analysis will be censored |
up to 10 years
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progression-free survival
기간: up to 10 years
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The length of time during and after the treatment of a disease, such as cancer, that a patient lives with the disease but it does not get worse. In a clinical trial, measuring the progression-free survival is one way to see how well a new treatment works. Also called PFS. |
up to 10 years
|
공동 작업자 및 조사자
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
연구 시작
기본 완료 (추정된)
기본 완료
연구 완료 (추정된)
연구 완료
연구 등록 날짜
최초 제출
최초 제출
QC 기준을 충족하는 최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
처음 게시됨
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
마지막 업데이트 게시됨
QC 기준을 충족하는 마지막 업데이트 제출
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
키워드
추가 관련 MeSH 약관
기타 연구 ID 번호
기타 연구 ID 번호
- BLCA_CMUFH
- K2024-187-01 (기타 식별자: The First Affiliated Hospital of Chongqing Medical University)
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
The datasets analyzed during the current study are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request.
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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