Helbildsbild och CT-radiomikbaserat djupinlärningssystem för prognostiseringsförutsägelse vid blåscancer
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.
Studieöversikt
Status
Betingelser
Intervention / Behandling
Detaljerad beskrivning
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.
Studietyp
Inskrivning (Beräknad)
Kontakter och platser
Studiekontakt
- Namn: QuanHao He
- Telefonnummer: 800-555-5555
- E-post: 2020120460@stu.cqmu.edu.cn
Studera Kontakt Backup
- Namn: Mingzhao Xiao, PHD
- Telefonnummer: 800-555-5555
- E-post: 2023140134@stu.cqmu.edu.cn
Studieorter
-
-
Chongqing Municipality
-
Chongqing, Chongqing Municipality, Kina, 400016
- Rekrytering
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University
-
Kontakt:
- QuanHao He, PHD
- Telefonnummer: 800-555-5555
- E-post: 2020120460@stu.cqmu.edu.cn
-
Kontakt:
- Mingzhao Xiao, PHD
- Telefonnummer: 023-89012557
- E-post: 2023140134@stu.cqmu.edu.cn
-
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Deltagandekriterier
Urvalskriterier
Åldrar som är berättigade till studier
- Barn
- Vuxen
- Äldre vuxen
Tar emot friska volontärer
Kön som är behöriga för studier
Testmetod
Studera befolkning
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.
Beskrivning
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
Studieplan
Hur är studien utformad?
Designdetaljer
- Observationsmodeller: COHORT
- Tidsperspektiv: OTHER
Kohorter och interventioner
Grupp / Kohort |
Intervention / Behandling |
|---|---|
|
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. |
Vad mäter studien?
Primära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
|
Overall survival
Tidsram: 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
|
Sekundära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
|
Recurrence free survival
Tidsram: 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
|
|
progression-free survival
Tidsram: up to 10 years
|
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
|
Samarbetspartners och utredare
Sponsor
Studieavstämningsdatum
Studera stora datum
Studiestart (Faktisk)
Primärt slutförande (Beräknad)
Avslutad studie (Beräknad)
Studieregistreringsdatum
Först inskickad
Först inskickad som uppfyllde QC-kriterierna
Första postat (Faktisk)
Uppdateringar av studier
Senaste uppdatering publicerad (Faktisk)
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
Senast verifierad
Mer information
Termer relaterade till denna studie
Ytterligare relevanta MeSH-villkor
Andra studie-ID-nummer
- BLCA_CMUFH
- K2024-187-01 (Annan identifierare: The First Affiliated Hospital of Chongqing Medical University)
Plan för individuella deltagardata (IPD)
Planerar du att dela individuella deltagardata (IPD)?
IPD-planbeskrivning
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.
Läkemedels- och apparatinformation, studiedokument
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