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Related Concept Videos

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  3. Ct-based Radiomics Model Using Stability Selection For Predicting The World Health Organization/international Society Of Urological Pathology Grade Of Clear Cell Renal Cell Carcinoma.
  1. Home
  3. Ct-based Radiomics Model Using Stability Selection For Predicting The World Health Organization/international Society Of Urological Pathology Grade Of Clear Cell Renal Cell Carcinoma.

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CT-based radiomics model using stability selection for predicting the World Health Organization/International Society

Haijie Zhang1, Fu Yin2, Menglin Chen3

  • 1Nuclear Medicine Department, Center of PET/CT, Shenzhen Second People's Hospital, Shenzhen 518052, China.

The British Journal of Radiology
|April 30, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
clear cell renal carcinomaradiomicsstability selectiontreatment decisionstumour grade

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Predicting clear cell renal cell carcinoma (ccRCC) grade using radiomics is feasible. Combining clinical features with non-contrast phase CT images offers an optimal, noninvasive approach for ccRCC grading.

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Clear cell renal cell carcinoma (ccRCC) grading is crucial for treatment planning.
  • Accurate noninvasive methods for ccRCC grading are needed.

Purpose of the Study:

  • To develop a predictive model for World Health Organization/International Society of Urological Pathology (WHO/ISUP) ccRCC grading.
  • To utilize 3D multiphase enhanced CT radiomics features (RFs) for ccRCC classification.

Main Methods:

  • Extracted 107 RFs from four CT phases (non-contrast, corticomedullary, nephrographic, excretory) in 138 low-grade and 60 high-grade ccRCC cases.
  • Developed and cross-validated models using various RF combinations.
  • Employed stability selection for RFs to enhance model reliability.

Main Results:

  • The Non-Contrast Phase-Excretory Phase (NCP-EP) model showed the best predictive value (AUC=0.78).
  • The Conventional Image and Clinical Features (CICFs)-NCP model achieved an AUC of 0.77, with sensitivity 0.75 and specificity 0.69.
  • The NCP model (AUC=0.76) was the second-best model.

Conclusions:

  • Combining clinical features with non-contrast CT images (CICFs-NCP model) is optimal for predicting ccRCC WHO/ISUP grade.
  • This noninvasive radiomics approach can aid in guiding ccRCC treatment decisions.
  • The study highlights the potential of radiomics for improving ccRCC management.