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Volume 47 Issue 3
Mar.  2024
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Article Navigation >  Journal of Molecular Imaging  > 2024  >  47(3): 271-276
WANG Dan, REN Ruimin, REN Wen, CHEN Xiubin, YAO Fucheng, XUE Jiping. Intermediate value of grayscale ultrasound image-based radiomics in discriminating the pathological grade of bladder urothelial carcinoma[J]. Journal of Molecular Imaging, 2024, 47(3): 271-276. doi: 10.12122/j.issn.1674-4500.2024.03.08
Citation: WANG Dan, REN Ruimin, REN Wen, CHEN Xiubin, YAO Fucheng, XUE Jiping. Intermediate value of grayscale ultrasound image-based radiomics in discriminating the pathological grade of bladder urothelial carcinoma[J]. Journal of Molecular Imaging, 2024, 47(3): 271-276. doi: 10.12122/j.issn.1674-4500.2024.03.08
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Intermediate value of grayscale ultrasound image-based radiomics in discriminating the pathological grade of bladder urothelial carcinoma

doi: 10.12122/j.issn.1674-4500.2024.03.08
  • 1.

    Department of Ultrasound, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan 030032, China

  • 2.

    Department of Urinary Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan 030032, China

  • 3.

    Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, China

  • Received Date: 2024-01-04
    Available Online: 2024-04-17
  • Publish Date: 2024-03-20
    Abstract
  •   Objective   To discriminate the pathological grade of bladder urothelial carcinoma through grayscale ultrasound image- based radiomics analysis.   Methods   A retrospective analysis was conducted on 153 patients with bladder urothelial carcinoma confirmed by pathology in our hospital from April 2016 to May 2023. The grayscale ultrasound images were manually delineated to outline the tumor region of interest and extract radiomics features. LASSO feature selection was utilized for dimensionality reduction, followed by modeling with three machine learning methods to identify the best model. The performance of the models was evaluated using ROC curves, and the goodness-of-fit was assessed using the Hosmer-Lemeshow test and calibration curve. Furthermore, decision curve analysis was conducted to explore the clinical utility of the model.   Results   Among the three machine learning models, the Support vector machine model exhibited the best performance, with an AUC of 0.858 (95% CI: 0.787-0.928) on the training set and 0.832 (95% CI: 0.708-0.936) on the test set. The calibration curve demonstrated good consistency. The decision curve analysis also showed a high net benefit.   Conclusion   Grayscale ultrasound image- based radiomics has preoperative diagnostic value in distinguishing the pathological grading of bladder urothelial carcinoma, which contributes to precise clinical diagnosis and treatment.

     

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