Decoding Hepatocellular Carcinoma Heterogeneity with Multimodal Ultrasound and Acoustic Radiomics

Abstract Hepatocellular carcinoma (HCC) is characterized by intratumoral heterogeneity (ITH), in which spatially and temporally evolving tumor subclones differ in angiogenesis, hypoxia, immune composition, proliferation, metabolism, and differentiation. This diversity determines tumor progression, recurrence, therapeutic resistance, and clinical outcomes among patients with similar disease stage. Molecular profiling has established ITH as a hallmark of HCC biology, while radiomics, radiogenomics, and habitat imaging based on computed tomography (CT) and magnetic resonance imaging (MRI) have demonstrated that clinically relevant heterogeneity can be assessed noninvasively. Ultrasound offers unique advantages for longitudinal tumor characterization because it is real-time, radiation-free, widely available, portable, and repeatable. Beyond conventional B-mode imaging, modern ultrasound interrogates complementary biological dimensions through contrast-enhanced ultrasound (CEUS), shear-wave elastography (SWE), quantitative ultrasound and radiofrequency (QUS/RF) analysis, and advanced microvascular imaging. Together, these techniques characterize tumor perfusion, biomechanical properties, tissue microstructure, and vascular architecture, providing a multidimensional assessment of intratumoral heterogeneity beyond conventional morphology. This narrative review summarizes the biological foundations of intratumoral heterogeneity in HCC and the benchmark established by CT- and MRI-based radiomics before examining how multimodal ultrasound may provide a biologically informed characterization of tumor heterogeneity. We discuss the emerging role of acoustic radiomics and artificial intelligence in integrating acoustic biomarkers to predict microvascular invasion, histological grade, recurrence risk, treatment response, and longitudinal changes in tumor biology. We propose a testable framework-the acoustic heterogeneity signatures-rather than an established clinical tool. The challenge is no longer to demonstrate that ultrasound contains biologically relevant heterogeneity, but to establish standardized acquisition protocols, biologically validated imaging biomarkers, and multicenter external validation. If these challenges can be overcome, multimodal ultrasound may evolve from a diagnostic imaging modality into a dynamic and interpretable biomarker platform for precision management of HCC, enabling individualized risk stratification, treatment selection, response assessment, and longitudinal disease monitoring.

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Journal
Advanced ultrasound in diagnosis and therapy
Published
2026-10-29
DOI
https://doi.org/10.26599/audt.2026.260028
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Decoding Hepatocellular Carcinoma Heterogeneity with Multimodal Ultrasound and Acoustic Radiomics

Jiaying Cao, PhD Yi Dong, MD Ying Wang, Wei Li et al.
2 links
Advanced ultrasound in diagnosis and therapy
Radiomics and Machine Learning in Medical Imaging
article

Decoding Hepatocellular Carcinoma Heterogeneity with Multimodal Ultrasound and Acoustic Radiomics

Jiaying Cao, PhD Yi Dong, MD Ying Wang, Wei Li, ChristophF Dietrich, Yijie Qiu, Li Wei, Yunlin Huang
article en

Abstract

Abstract Hepatocellular carcinoma (HCC) is characterized by intratumoral heterogeneity (ITH), in which spatially and temporally evolving tumor subclones differ in angiogenesis, hypoxia, immune composition, proliferation, metabolism, and differentiation. This diversity determines tumor progression, recurrence, therapeutic resistance, and clinical outcomes among patients with similar disease stage. Molecular profiling has established ITH as a hallmark of HCC biology, while radiomics, radiogenomics, and habitat imaging based on computed tomography (CT) and magnetic resonance imaging (MRI) have demonstrated that clinically relevant heterogeneity can be assessed noninvasively. Ultrasound offers unique advantages for longitudinal tumor characterization because it is real-time, radiation-free, widely available, portable, and repeatable. Beyond conventional B-mode imaging, modern ultrasound interrogates complementary biological dimensions through contrast-enhanced ultrasound (CEUS), shear-wave elastography (SWE), quantitative ultrasound and radiofrequency (QUS/RF) analysis, and advanced microvascular imaging. Together, these techniques characterize tumor perfusion, biomechanical properties, tissue microstructure, and vascular architecture, providing a multidimensional assessment of intratumoral heterogeneity beyond conventional morphology. This narrative review summarizes the biological foundations of intratumoral heterogeneity in HCC and the benchmark established by CT- and MRI-based radiomics before examining how multimodal ultrasound may provide a biologically informed characterization of tumor heterogeneity. We discuss the emerging role of acoustic radiomics and artificial intelligence in integrating acoustic biomarkers to predict microvascular invasion, histological grade, recurrence risk, treatment response, and longitudinal changes in tumor biology. We propose a testable framework-the acoustic heterogeneity signatures-rather than an established clinical tool. The challenge is no longer to demonstrate that ultrasound contains biologically relevant heterogeneity, but to establish standardized acquisition protocols, biologically validated imaging biomarkers, and multicenter external validation. If these challenges can be overcome, multimodal ultrasound may evolve from a diagnostic imaging modality into a dynamic and interpretable biomarker platform for precision management of HCC, enabling individualized risk stratification, treatment selection, response assessment, and longitudinal disease monitoring.

Advanced ultrasound in diagnosis and therapyVol. 10(4)
Shanghai Jiao Tong University (CN), XinHua Hospital (CN), University Hospital Frankfurt (DE)
Good health and well-being
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
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