Quantitative Ultrasound Imaging in Oncology

Quantitative ultrasound (QUS) imaging has emerged as a non-invasive modality for characterising tissue microstructure and assessing therapeutic response in oncology. By analysing backscattered radiofrequency signals, QUS quantifies acoustic properties such as scatterer size, concentration and distribution, providing metrics beyond conventional B-mode imaging. These spectral and parametric measures, including mid-band fit, spectral slope and intercept, enable discrimination between benign and malignant lesions, mapping of intra-tumour heterogeneity and early detection of treatment-induced changes. Texture analysis and radiomic approaches further extract spatial features from parametric maps, revealing subtle alterations in tumour architecture that correlate with histopathology and clinical outcomes. QUS techniques have been applied to breast, liver and other organs to predict response to neoadjuvant chemotherapy, monitor ablative procedures and guide personalised treatment planning. Their portability, cost-effectiveness and absence of ionising radiation support broad clinical translation, particularly in resource-limited settings. As hardware and signal-processing algorithms advance, QUS stands poised to integrate with multiparametric imaging paradigms, offering real-time biomarkers for precision oncology and longitudinal patient monitoring.

Research from Nature Portfolio

Recent studies have refined statistical parametric imaging by introducing entropy-based approaches that overcome limitations of window size and boundary artefacts. Small-window entropy parametric imaging has demonstrated superior performance in phantom and clinical breast tumour models, yielding higher classification accuracy than conventional Nakagami-based methods. Other investigations have explored quantitative analysis of echoes from peritumoral tissue, revealing that combining tumour and surrounding-tissue parameters enhances discrimination of benign and malignant breast lesions, with multi-parametric classifiers achieving area-under-curve values exceeding 0.9. Foundational work has also shown that pre-treatment QUS features from tumour cores and margins can predict neoadjuvant chemotherapy response with accuracy approaching 90 % and provide prognostic information on recurrence-free survival, underscoring the prognostic and predictive power of QUS biomarkers.

Quantitative Ultrasound Imaging in Oncology publication trend

The graph below shows the total number of articles in quantitative ultrasound imaging in oncology across all publications each year (not limited to Nature Index journals).

Technical terms

Quantitative Ultrasound (QUS): Analysis of backscattered radiofrequency signals to derive numerical metrics of tissue microstructure.

Spectral Parametric Imaging: Generation of images by mapping frequency-domain features such as spectral slope and mid-band fit across the region of interest.

Nakagami Imaging: A statistical model describing echo amplitude distributions, used to compute parameters reflecting scatterer concentration and arrangement.

Radiomics: Extraction of high-dimensional quantitative features from medical images for characterisation and prediction of disease behaviour.

Texture Analysis: Quantitative assessment of spatial variations in pixel intensity or parametric values, revealing structural heterogeneity.

Acoustic Scatterer: Microscopic structures within tissue (cells, fibres) that reflect or scatter ultrasound waves, influencing backscatter signals.

References

1. Monitoring Radiofrequency Ablation Using Real-Time Ultrasound Nakagami Imaging Combined with Frequency and Temporal Compounding Techniques. PLOS ONE (2015).
2. Small-window parametric imaging based on information entropy for ultrasound tissue characterization. Scientific Reports (2017).
3. Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue. Scientific Reports (2019).
4. A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound. Scientific Reports (2017).
5. Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Probability Maps Derived from Quantitative Ultrasound Parametric Images. IEEE Transactions on Biomedical Engineering (2024).
6. Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results. PLOS ONE (2020).