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Updated: Jun 12, 2026

An In vitro System to Gauge the Thrombolytic Efficacy of Histotripsy and a Lytic Drug

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Published on: June 4, 2021

Quantification of Histotripsy Dosage Using Machine Learning-Enhanced Ultrasound Imaging Analysis: Correlation with

Haowei Tai¹, Tejaswi Worlikar¹, Zhen Xu²

¹Department of Radiology, University of Michigan, Ann Arbor, Michigan (H.T., T.W., J.B.F., M.Z.).

Academic Radiology

|June 10, 2026

Summary

This summary is machine-generated.

Histotripsy (ultrasound therapy) dose can now be noninvasively assessed. Ultrasound texture analysis combined with machine learning accurately predicts tumor cell damage, correlating with histology for precise treatment.

Keywords:

Hepatocellular carcinoma Histotripsy Texture analysis Tumor ablation Ultrasound

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Published on: October 10, 2018

Area of Science:

Medical physics
Biomedical engineering
Oncology

Background:

Histotripsy is a noninvasive ultrasound therapy for liver tumors.
Current treatment uses a fixed dose, ignoring tumor heterogeneity and mechanical properties.
A method is needed to noninvasively measure histotripsy-induced cellular damage.

Purpose of the Study:

To develop a quantitative, noninvasive, image-based approach for evaluating histotripsy-induced tumor cellular damage.
To correlate ultrasound texture analysis with machine learning predictions and histological outcomes.

Main Methods:

Immunocompetent rats with liver tumors received varying histotripsy doses.
B-mode ultrasound images were captured pre- and post-treatment.
Texture analysis and feature reduction were performed, followed by histological quantification of cellular damage.

Main Results:

Strong correlations were found between nuclear debris area and ultrasound texture features (kurtosis R²=0.9, autocorrelation R²=0.8).
A random forest classifier accurately predicted histotripsy doses based on texture features (R²=0.9, p<0.05).

Conclusions:

Ultrasound image texture changes reliably indicate dose-dependent tumor cell disruption from histotripsy.
This approach strongly correlates with histological damage assessment.
Integrating quantitative imaging feedback could enable precise, patient-specific histotripsy dosing.