Endophytic Renal Tumor, Kidney Neoplasms, Partial Nephrectomy Outcome, Postoperative Renal Function, Renal Cell Carcinoma
Conditions
Keywords
Partial nephrectomy, Endophytic renal cell carcinoma, Multimodal prediction model, Radiomics, Deep learning, Three-dimensional reconstruction, Postoperative prognosis, Renal function, Pentafecta, External validation
Brief summary
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.
Detailed description
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.
Interventions
OTHERImaging-clinical multimodal prognostic modeling
Preoperative CT imaging features, radiomics features, three-dimensional reconstruction-derived features, and clinical variables will be retrospectively analyzed to develop and validate a multimodal model for predicting postoperative prognosis after partial nephrectomy. No intervention will be assigned to participants.
Sponsors
Tianjin Medical University Second Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Modified Pentafecta Achievement After Partial Nephrectomy | From the date of partial nephrectomy to the last available postoperative follow-up, up to 12 months after surgery. | Modified pentafecta achievement will be defined as the simultaneous fulfillment of predefined postoperative outcome criteria, including negative surgical margin, absence of major postoperative complications, preservation of renal function, absence of significant perioperative adverse events, and absence of early tumor recurrence or other prespecified unfavorable outcomes. Patients who do not meet all criteria will be classified as modified pentafecta failure. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Postoperative eGFR Decline Greater Than 20% | From baseline to 3-12 months after partial nephrectomy | A clinically significant decline in renal function will be defined as a decrease in estimated glomerular filtration rate greater than 20% compared with the preoperative baseline value. |
| Major Postoperative Complications | Within 30 or 90 days after partial nephrectomy | Major postoperative complications will be defined as Clavien-Dindo grade III or higher complications occurring within the predefined postoperative period. |
| Positive Surgical Margin | At final pathological evaluation after partial nephrectomy | Positive surgical margin will be determined according to the postoperative pathological report. |
| Area Under the Receiver Operating Characteristic Curve of the Multimodal Model | At completion of model development and external validation, using postoperative outcome data up to 12 months after surgery. | The discriminatory performance of the multimodal model for predicting modified pentafecta achievement after partial nephrectomy will be evaluated using the area under the receiver operating characteristic curve. |
Countries
China
Outcome results
None listed