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NCT Number: NCT07697417

Multimodal Prediction of Postoperative Prognosis After Partial Nephrectomy for Endophytic Renal Cell Carcinoma

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Renal Cell Carcinoma Adenocarcinoma Carcinoma Carcinoma, Renal Cell Endophytic Renal Tumor Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Kidney Diseases Kidney Neoplasms Male Urogenital Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Partial Nephrectomy Outcome Postoperative Renal Function Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Tianjin Medical University Second Hospital

Tianjin, Tianjin Municipality, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Imaging-clinical multimodal prognostic modeling

Other

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.

Primary outcomes

  1. Modified Pentafecta Achievement After Partial Nephrectomy

    Time frame: 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 outcomes

  1. Postoperative eGFR Decline Greater Than 20%

    Time frame: 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.

  2. Major Postoperative Complications

    Time frame: 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.

  3. Positive Surgical Margin

    Time frame: At final pathological evaluation after partial nephrectomy

    Positive surgical margin will be determined according to the postoperative pathological report.

  4. Area Under the Receiver Operating Characteristic Curve of the Multimodal Model

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Tianjin Medical University Second Hospital

Other

Registry information

Official study title

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

Acronym: PN-ProMM

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jul 13, 2026
Registry last updated
Jul 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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