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Prediction of Benign and Malignant Small Renal Masses Using CT-Derived Extracellular Volume Fraction: An

Yunyu Guo1, Qiaofang Fang1, Youling Li1

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Summary

A machine learning model using extracellular volume (ECV) and CT scan data accurately distinguishes malignant from benign small renal masses (SRMs). This AI tool aids in personalized treatment decisions for kidney cancer.

Keywords:
Extracellular matrixMachine learningRenal cell carcinoma

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Small renal masses (SRMs) require accurate characterization for appropriate management.
  • Distinguishing malignant from benign SRMs preoperatively is clinically significant.
  • Current imaging methods may have limitations in differentiating SRM subtypes.

Purpose of the Study:

  • To develop and validate a machine learning model for differentiating malignant and benign SRMs.
  • To integrate morphological characteristics, enhancement dynamics, and extracellular volume (ECV) fractions into a diagnostic model.
  • To support personalized management strategies for patients with SRMs.

Main Methods:

  • Retrospective analysis of 230 patients with preoperative contrast-enhanced CT imaging.
  • Inclusion of eleven multiphasic CT parameters, including ECV fraction, clinical, and laboratory data.
  • Application of feature selection techniques and machine learning classifiers, with SHapley Additive exPlanations (SHAP) for model interpretation.

Main Results:

  • The Extreme Gradient Boosting model incorporating ECV demonstrated high performance in distinguishing malignant (n=183) from benign (n=47) SRMs.
  • Area under the curve values reached 0.993 (training), 0.986 (validation), and 0.951 (external test).
  • SHAP analysis identified ECV fraction as the most significant contributor to SRM characterization.

Conclusions:

  • Multiphasic contrast-enhanced CT-derived ECV fraction, combined with conventional CT parameters, shows significant diagnostic efficacy.
  • The developed machine learning model effectively differentiates malignant and benign SRMs.
  • This approach holds promise for improving personalized management of SRMs.