An observational study in Renal Cell Carcinoma, Kidney Neoplasms and Endophytic Renal Tumor, sponsored by Tianjin Medical University Second Hospital. Active, not recruiting at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-07-13.
Sponsored by Tianjin Medical University Second Hospital · Observational
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.
1,965 studies on the registry are indexed under Carcinoma, Renal Cell; 378 are open to participants now.
This study's enrollment of 406 is above the median of 146 across 360 observational studies indexed under Carcinoma, Renal Cell.
Browse Carcinoma, Renal Cell studies →Tianjin Medical University Second Hospital is the lead sponsor of 57 studies on the registry; 33 are open to participants now.
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The study population will include adult patients with endophytic renal cell carcinoma who underwent partial nephrectomy at participating medical centers and had available preoperative imaging, clinical, pathological, perioperative, and postoperative follow-up data. Patients will be identified retrospectively from institutional databases and electronic medical records.
Age 18 years or older at the time of surgery.
Exclusion Criteria:
Patients with endophytic renal cell carcinoma who underwent partial nephrectomy and were included for model development and internal validation.
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.
Other: Imaging-clinical multimodal prognostic modeling
An independent validation cohort used to evaluate the generalizability of the multimodal prognostic prediction model.
Other: Imaging-clinical multimodal prognostic modeling
An additional independent validation cohort used to assess model robustness across different patient populations or institutions.
Other: Imaging-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.
Modified Pentafecta Achievement After Partial Nephrectomy
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.
Time frame: From the date of partial nephrectomy to the last available postoperative follow-up, up to 12 months after surgery.
Postoperative eGFR Decline Greater Than 20%
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.
Time frame: From baseline to 3-12 months after partial nephrectomy
Major Postoperative Complications
Major postoperative complications will be defined as Clavien-Dindo grade III or higher complications occurring within the predefined postoperative period.
Time frame: Within 30 or 90 days after partial nephrectomy
Positive Surgical Margin
Positive surgical margin will be determined according to the postoperative pathological report.
Time frame: At final pathological evaluation after partial nephrectomy
Area Under the Receiver Operating Characteristic Curve of the Multimodal Model
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.
Time frame: At completion of model development and external validation, using postoperative outcome data up to 12 months after surgery.
Plan to share: No — Individual participant data will not be publicly shared because of privacy, ethical, and institutional data-use restrictions. De-identified data may be made available from the corresponding investigator upon reasonable request and with appropriate institutional approval, where applicable.
No publications or documents are linked to this record.
This study is active, not recruiting, as verified in Jul 2026. You cannot join it, but the record below documents what was studied.
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Tianjin Medical University Second Hospital