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

NCT07697417 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 406

Last updated 2026-07-13

No results posted yet for this study

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.

Conditions

  • Renal Cell Carcinoma
  • Kidney Neoplasms
  • Endophytic Renal Tumor
  • Postoperative Renal Function
  • Partial Nephrectomy Outcome

Interventions

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.

Sponsors & Collaborators

  • Tianjin Medical University Second Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-01-01
Primary Completion
2026-05-30
Completion
2026-12-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07697417 on ClinicalTrials.gov