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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Noninvasive Prediction of Ki-67 Expression in Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics: A

Linlin Zhang1,2, Shaobo Duan2,3, Qinghua Qi4

  • 1Department of Ultrasound, Henan University People's Hospital, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China.

Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine
|November 22, 2022
PubMed
Summary

Ultrasomics shows promise as a noninvasive tool for predicting Ki-67 expression in hepatocellular carcinoma (HCC). Combining ultrasomics with clinical data further enhances prediction accuracy for this cancer biomarker.

Keywords:
Ki-67hepatocellular carcinomamachine learningradiomicsultrasonography

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Area of Science:

  • Medical Imaging
  • Oncology
  • Biomarker Analysis

Background:

  • Hepatocellular carcinoma (HCC) is a primary liver cancer with significant global health impact.
  • Ki-67 expression is a crucial biomarker for assessing tumor proliferation and aggressiveness in HCC.
  • Accurate prediction of Ki-67 levels is vital for guiding treatment decisions and prognosis.

Purpose of the Study:

  • To evaluate the efficacy of ultrasomics in predicting Ki-67 expression in HCC.
  • To develop and validate predictive models using ultrasomics and clinical data.
  • To assess the noninvasive potential of ultrasomics for HCC biomarker assessment.

Main Methods:

  • Retrospective analysis of 244 HCC patients across three hospitals.
  • Extraction and selection of 1409 ultrasomics features from ultrasound images.
  • Development of clinical, ultrasomics, and combined predictive models using machine learning (Support Vector Machine).

Main Results:

  • The ultrasomics model demonstrated strong predictive performance across training, testing, and validation datasets (AUCs ranging from 0.665 to 0.955).
  • Combining ultrasomics with clinical features significantly improved predictive accuracy (AUCs up to 0.986).
  • The combined model achieved high sensitivity and specificity in predicting Ki-67 expression.

Conclusions:

  • Ultrasomics is a viable noninvasive method for predicting Ki-67 expression in HCC.
  • The integration of ultrasomics with clinical data offers a powerful approach for enhancing HCC biomarker prediction.
  • This approach may lead to improved noninvasive assessment and management of HCC.