Sistema di apprendimento profondo basato su immagini di vetrini interi e radiomica TC per la previsione della prognosi nel cancro della vescica
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.
Panoramica dello studio
Stato
Condizioni
Intervento / Trattamento
Descrizione dettagliata
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.
Tipo di studio
Iscrizione (Stimato)
Contatti e Sedi
Contatto studio
- Nome: QuanHao He
- Numero di telefono: 800-555-5555
- Email: 2020120460@stu.cqmu.edu.cn
Backup dei contatti dello studio
- Nome: Mingzhao Xiao, PHD
- Numero di telefono: 800-555-5555
- Email: 2023140134@stu.cqmu.edu.cn
Luoghi di studio
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Chongqing Municipality
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Chongqing, Chongqing Municipality, Cina, 400016
- Reclutamento
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University
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Contatto:
- QuanHao He, PHD
- Numero di telefono: 800-555-5555
- Email: 2020120460@stu.cqmu.edu.cn
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Contatto:
- Mingzhao Xiao, PHD
- Numero di telefono: 023-89012557
- Email: 2023140134@stu.cqmu.edu.cn
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Criteri di partecipazione
Criteri di ammissibilità
Età idonea allo studio
- Bambino
- Adulto
- Adulto più anziano
Accetta volontari sani
Sessi ammissibili allo studio
Metodo di campionamento
Popolazione di studio
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.
Descrizione
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
Piano di studio
Come è strutturato lo studio?
Dettagli di progettazione
- Modelli osservazionali: COHORT
- Prospettive temporali: OTHER
Coorti e interventi
Gruppo / Coorte |
Intervento / Trattamento |
|---|---|
|
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. |
Cosa sta misurando lo studio?
Misure di risultato primarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
|
Overall survival
Lasso di tempo: 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
|
Misure di risultato secondarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
|
Recurrence free survival
Lasso di tempo: 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
Lasso di tempo: 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
|
Collaboratori e investigatori
Sponsor
Studiare le date dei record
Studia le date principali
Inizio studio (Effettivo)
Completamento primario (Stimato)
Completamento dello studio (Stimato)
Date di iscrizione allo studio
Primo inviato
Primo inviato che soddisfa i criteri di controllo qualità
Primo Inserito (Effettivo)
Aggiornamenti dei record di studio
Ultimo aggiornamento pubblicato (Effettivo)
Ultimo aggiornamento inviato che soddisfa i criteri QC
Ultimo verificato
Maggiori informazioni
Termini relativi a questo studio
Termini MeSH pertinenti aggiuntivi
- Malattie urogenitali
- Neoplasie urogenitali
- Neoplasie per sede
- Neoplasie
- Malattie urogenitali maschili
- Malattie urologiche
- Malattie urogenitali femminili
- Malattie urogenitali femminili e complicanze della gravidanza
- Neoplasie urologiche
- Malattie della vescica urinaria
- Neoplasie della vescica urinaria
Altri numeri di identificazione dello studio
- BLCA_CMUFH
- K2024-187-01 (Altro identificatore: The First Affiliated Hospital of Chongqing Medical University)
Piano per i dati dei singoli partecipanti (IPD)
Hai intenzione di condividere i dati dei singoli partecipanti (IPD)?
Descrizione del piano IPD
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.
Informazioni su farmaci e dispositivi, documenti di studio
Studia un prodotto farmaceutico regolamentato dalla FDA degli Stati Uniti
Studia un dispositivo regolamentato dalla FDA degli Stati Uniti
Queste informazioni sono state recuperate direttamente dal sito web clinicaltrials.gov senza alcuna modifica. In caso di richieste di modifica, rimozione o aggiornamento dei dettagli dello studio, contattare register@clinicaltrials.gov. Non appena verrà implementata una modifica su clinicaltrials.gov, questa verrà aggiornata automaticamente anche sul nostro sito web .