Renal Cell Carcinoma, Clear Cell
Conditions
Keywords
Renal Cell Carcinoma, Multimodal, Artificial Intelligence, Prognosis, Deep Learning, Multicentre Study
Brief summary
The main goal of this observational study is to construct a predictive recurrence model for renal clear cell carcinoma through an artificial intelligence multimodal algorithm, which will improve the prognosis and survival time of ccRCC patients by helping clinicians to formulate individualised follow-up and to screen for appropriate adjuvant therapy candidates.
Interventions
None listed
Sponsors
Study design
Eligibility
Inclusion criteria
* Patients with malignant renal tumours must have postoperative pathological confirmation of renal clear cell carcinoma with pathological TNM stage Ⅰ-Ⅲ; * Have available preoperative imaging * Have available post-operative pathological paraffin samples or HE-stained tissue sections * Must agree to and be able to comply with the study's visit schedule and other requirements as specified in the protocol, including follow-up visits by telephone
Exclusion criteria
* The patient's tumour type cannot be determined on the basis of histopathological sections * Simultaneous combination of primary tumours at other sites; * No regular follow-up imaging data * Cases in which other factors led the investigator to believe that enrolment was inappropriate * Cases that met all the inclusion criteria of the appeal and did not meet all the
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Disease free survival | Baseline, immediately after the surgery, Tumour recurrence or further follow-up, Three months after surgery, Six months after surgery, Every year after surgery, at least follow-up 2years, up to 10 years | Defined as from surgery to tumor recurrence or follow-up deadline |
Countries
China