Development and External Validation of an Imaging-Clinical Multimodal Fusion Model for Predicting Postoperative Prognosis After Partial Nephrectomy in Patients With Endophytic Renal Cell Carcinoma
| Registry | NCT07697417, acronym PN-ProMM |
|---|---|
| Status | Active not recruiting |
| Study type | Observational |
| Enrollment | 406 (Actual) |
| Sponsor | Tianjin Medical University Second Hospital (Other) |
| Conditions | Renal Cell Carcinoma, Kidney Neoplasms, Endophytic Renal Tumor, Postoperative Renal Function, Partial Nephrectomy Outcome |
| Interventions | Imaging-clinical multimodal prognostic modeling (Other) |
| Dates | start 2026-01-01, primary completion 2026-05-30, completion 2026-12-01 (estimated), first posted 2026-07-13, last updated 2026-07-13 |
| Locations | 1 site in China |
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All trials for Renal Cell Carcinoma All trials for Kidney Neoplasms All trials of Imaging-clinical multimodal prognostic modeling
This retrospective observational cohort study aims to develop and externally validate an imaging-clinical multimodal fusion model for predicting postoperative prognosis in patients with endophytic renal cell carcinoma undergoing partial nephrectomy. Preoperative computed tomography imaging features, three-dimensional reconstruction-derived tumor characteristics, radiomics features, and clinical variables will be integrated using machine learning and deep learning approaches. The primary objective is to evaluate whether the multimodal model improves prediction of postoperative prognostic outcomes compared with single-modality models based on clinical or imaging features alone.
Partial nephrectomy is a standard nephron-sparing treatment for localized renal cell carcinoma. However, postoperative functional and oncologic outcomes remain heterogeneous, especially in patients with endophytic renal tumors, in whom tumor complexity may increase surgical difficulty and affect postoperative recovery. Conventional clinical variables and anatomical scoring systems may not fully capture the multidimensional risk profile of these patients.
This study will retrospectively collect clinical, pathological, perioperative, and imaging data from patients with endophytic renal cell carcinoma who underwent partial nephrectomy. Preoperative multiphase computed tomography images will be used for radiomics feature extraction and deep learning-based image representation. Three-dimensional reconstruction-derived tumor features and conventional clinical variables will also be incorporated.
The study will develop and validate multimodal prediction models, including clinical models, radiomics models, deep learning imaging models, and imaging-clinical fusion models. Model performance will be assessed using discrimination, calibration, and clinical utility metrics, including the area under the receiver operating characteristic curve, calibration curves, decision curve analysis, and external validation across independent cohorts.
| Arm | Type | Interventions | Description |
|---|---|---|---|
| Development Cohort | Other: Imaging-clinical multimodal prognostic modeling | Patients with endophytic renal cell carcinoma who underwent partial nephrectomy and were included for model development and internal validation. | |
| External Validation Cohort 1 | Other: Imaging-clinical multimodal prognostic modeling | An independent cohort of patients with endophytic renal cell carcinoma who underwent partial nephrectomy and were used for external validation of the prediction model. | |
| External Validation Cohort 2 | Other: Imaging-clinical multimodal prognostic modeling | An independent validation cohort used to evaluate the generalizability of the multimodal prognostic prediction model. | |
| External Validation Cohort 3 | Other: Imaging-clinical multimodal prognostic modeling | An additional independent validation cohort used to assess model robustness across different patient populations or institutions. |
Sex: All, ages: 18 Years to any
Inclusion Criteria: * Patients diagnosed with renal cell carcinoma. Patients with endophytic renal tumors based on preoperative imaging assessment. Patients who underwent partial nephrectomy. Available preoperative contrast-enhanced computed tomography images. Available clinical, pathological, perioperative, and postoperative follow-up data. Age 18 years or older at the time of surgery. Exclusion Criteria: * Patients who underwent radical nephrectomy as the primary surgical treatment. Patients with missing or poor-quality preoperative imaging data. Patients with incomplete key clinical, pathological, or follow-up information. Patients with hereditary renal cancer syndromes. Patients with bilateral renal tumors or solitary kidney. Patients who received neoadjuvant systemic therapy before partial nephrectomy.
| Facility | City | Country | Status |
|---|---|---|---|
| Tianjin Medical University Second Hospital | Tianjin, Tianjin Municipality | China |