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Related Experiment Video

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Deep learning algorithm for pathological grading of renal cell carcinoma based on multi-phase enhanced CT.

Haozhong Chen1, Jun Liu2, Kai Deng2

  • 1Department of Radiology, Second Xiangya Hospital, Central South University, Changsha 410011, China. haozhong.chen@csu.edu.cn.

Zhong Nan Da Xue Xue Bao. Yi Xue Ban = Journal of Central South University. Medical Sciences
|August 11, 2025
PubMed
Summary

This study introduces a novel multi-modal deep learning algorithm for renal cell carcinoma (RCC) pathological grading. The new method integrates multi-phase CT scans and clinical data, improving diagnostic accuracy for better treatment planning.

Keywords:
3-dimensional ResNetcross-modal fusiondeep learningpathological gradingrenal cell carcinoma

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Renal cell carcinoma (RCC) is a significant health threat, necessitating accurate preoperative pathological grading for effective treatment.
  • Current deep learning methods for RCC grading often rely on single-phase CT, leading to limitations like missed lesions and incomplete evaluations.
  • Integrating multi-phase CT imaging and clinical data offers a promising avenue to overcome these limitations.

Purpose of the Study:

  • To develop and validate a multi-modal deep learning algorithm for predicting RCC pathological grading.
  • To enhance the accuracy and comprehensiveness of RCC grading by integrating multi-phase enhanced CT images with clinical variables.
  • To provide a robust tool for personalized treatment planning in RCC patients.

Main Methods:

  • A multi-modal deep learning approach was developed, incorporating four-phase enhanced CT images (plain, arterial, venous, delayed) and clinical variables.
  • An embedding encoding module processed clinical variables, while a 3D ResNet50 model extracted spatial features from CT data.
  • A Fusion module, utilizing a cross-self-attention mechanism, integrated multi-modal and multi-phase features for comprehensive analysis.

Main Results:

  • The proposed algorithm achieved superior performance compared to traditional radiomics and existing deep learning methods.
  • Key performance metrics included an accuracy of 83.87%, a recall rate of 95.04%, and an F1-score of 82.23%.
  • These results demonstrate the algorithm's effectiveness in accurately predicting RCC pathological grading.

Conclusions:

  • The developed algorithm shows strong stability and sensitivity in predicting RCC pathological grading.
  • This multi-modal, multi-phase approach significantly enhances predictive performance, offering a novel solution for accurate RCC diagnosis.
  • The findings support the algorithm's potential for improving personalized treatment strategies for RCC patients.