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This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's predictive ability with traditional models.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Non-recurrence group | |||
| Recurrence group |
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| Measure | Description | Time Frame |
|---|---|---|
| disease-free survival (DFS) | the interval from the date of surgery to disease recurrence, all-cause mortality or the last visit | recruitment occurred between June 2013 and March 2020 |
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Inclusion Criteria:
Exclusion Criteria:
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Patients admitted to urology departments at participating medical centres
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Yingjie Xv | Recruiting | Chongqing | Chongqing Municipality | 400016 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38896853 | Derived | Xv Y, Wei Z, Jiang Q, Zhang X, Chen Y, Xiao B, Yin S, Xia Z, Qiu M, Li Y, Tan H, Xiao M. Three-dimensional deep learning model complements existing models for preoperative disease-free survival prediction in localized clear cell renal cell carcinoma: a multicenter retrospective cohort study. Int J Surg. 2024 Nov 1;110(11):7034-7046. doi: 10.1097/JS9.0000000000001808. |
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| ID | Term |
|---|---|
| D002292 | Carcinoma, Renal Cell |
| ID | Term |
|---|---|
| D000230 | Adenocarcinoma |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D007680 | Kidney Neoplasms |
| D014571 | Urologic Neoplasms |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052801 | Male Urogenital Diseases |
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