Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study - Takeaways - MDSpire

Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study

  • By

  • Zhanpeng Yuan

  • Yuhua Mei

  • Xiang Peng

  • Zongjie Wei

  • Yingjie Xv

  • Bangxin Xiao

  • Mingzhao Xiao

  • June 23, 2026

  • 0 min

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  • 1

    A multicenter study developed a machine learning model using radiomic features from CTU images to predict pathological grade in UTUC.

  • 2

    The study involved 338 patients who underwent radical nephroureterectomy and preoperative CTU from June 2015 to June 2024.

  • 3

    LGBM outperformed other algorithms, achieving an AUC of 0.945 in the training dataset and 0.829 in the testing dataset.

  • 4

    Key predictive features were derived from the venous and arterial phases of CTU, enhancing the model's interpretability.

  • 5

    The research highlights the potential of radiomics in improving preoperative diagnosis and treatment strategies for UTUC.

Original Source(s)

Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study

  • by Zhanpeng Yuan, Yuhua Mei, Xiang Peng, Zongjie Wei, Yingjie Xv, Bangxin Xiao, Mingzhao Xiao

  • June 23, 2026

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