Improved Breast 2D SWE Algorithm to Eliminate False-Negative Cases

Downgrading Breast Imaging Reporting and Data System categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study

OBJECTIVE

To determine whether adding elastography strain ratio (SR) and a deep learning based computer-aided diagnosis (CAD) system to breast ultrasound (US) can help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a-c categories and avoid unnecessary biopsies.

METHODS

This prospective, multicentre study included 1049 masses (691 benign, 358 malignant) with assigned BI-RADS 3 and 4a-c between 2020 and 2022. CAD results was dichotomized possibly malignant vs. benign. All patients underwent SR and CAD examinations and histopathological findings were the standard of reference. Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after reclassified (new BI-RADS 3) with SR and CAD were the outcome measures.

RESULTS

Following the routine conventional breast US assessment, 48.6% (336 of 691 masses) underwent unnecessary biopsies. After reclassifying BI-RADS 4a masses (SR cut-off <2.90, CAD dichotomized possibly benign), 25.62% (177 of 691 masses) underwent an unnecessary biopsies corresponding to a 50.14% (177 vs. 355) reduction of unnecessary biopsies. After reclassification, only 1.72% (9 of 523 masses) malignancies were missed in the new BI-RADS 3 group.

CONCLUSION

Adding SR and CAD to clinical practice may show an optimal performance in reclassifying BI-RADS 4a to 3 categories, and 50.14% masses would be benefit by keeping the rate of undetected malignancies with an acceptable value of 1.72%.

ADVANCES IN KNOWLEDGE

Leveraging the potential of SR in conjunction with CAD holds immense promise in substantially reducing the biopsy frequency associated with BI-RADS 3 and 4A lesions, thereby conferring substantial advantages upon patients encompassed within this cohort.

Current status of optoacoustic breast imaging and future trends in clinical application: is it ready for prime time?

Optoacoustic imaging (OAI) is an emerging field with increasing applications in patients and exploratory clinical trials for breast cancer. Optoacoustic imaging (or photoacoustic imaging) employs non-ionizing, laser light to create thermoelastic expansion in tissues and detect the resulting ultrasonic emission. By combining high optical contrast capabilities with the high spatial resolution and anatomic detail of grayscale ultrasound, OAI offers unique opportunities for visualizing biological function of tissues in vivo. Over the past decade, human breast applications of OAI, including benign/malignant mass differentiation, distinguishing cancer molecular subtype, and predicting metastatic potential, have significantly increased. We discuss the current state of optoacoustic breast imaging, as well as future opportunities and clinical application trends. CLINICAL RELEVANCE STATEMENT: Optoacoustic imaging is a novel breast imaging technique that enables the assessment of breast cancer lesions and tumor biology without the risk of ionizing radiation exposure, intravenous contrast, or radionuclide injection. KEY POINTS: • Optoacoustic imaging (OAI) is a safe, non-invasive imaging technique with thriving research and high potential clinical impact. • OAI has been considered a complementary tool to current standard breast imaging techniques. • OAI combines parametric maps of molecules that absorb light and scatter acoustic waves (like hemoglobin, melanin, lipids, and water) with anatomical images, facilitating scalable and real-time molecular evaluation of tissues.

The "Halo Appearance" of Shear Wave Elastography in the Setting of Fat Necrosis: A Potentially Useful Sonographic Discriminator

OBJECTIVE

To describe the morphologic sonographic appearances and frequency of the "halo sign" in the setting of fat necrosis on shear wave elastography (SWE).

METHODS

Patients with clinically suspected fat necrosis were prospectively scanned using SWE in addition to standard gray-scale and Doppler images. Cases were qualitatively grouped into one of three sonographic appearances: focal hypoechoic lesion with increased internal tissue stiffness ("focal stiffness"), focal hypoechoic lesion with isoechoic or hyperechoic periphery demonstrating increased tissue stiffness relative to the central hypoechoic lesion ("halo stiffness"), heterogeneously echogenic lesion with diffusely increased stiffness ("heterogeneous stiffness").

RESULTS

Exactly 19 patients met inclusion criteria (female n = 14; male n = 5). Shear wave velocities were recorded and retrospectively evaluated. The mean clinical follow-up was 11.4 months (range 3.0-25.5). Lesions demonstrated higher average tissue stiffness than background tissue (overall mass shear wave velocity 3.26 m/s, background 1.42 m/s, P < .001; lesion Young's modulus 40.85 kPa vs background 7.22 kPa, P < .001). The halo sign was identified in 10/19 (55%) patients.

CONCLUSION

The halo sign is a potentially useful sign in the setting of fat necrosis seen in the majority of clinically suspected cases.

A conditional inference tree model for predicting cancer risk of non-mass lesions detected on breast ultrasound

OBJECTIVES

To generate and validate a prediction model based on imaging features for cancer risk of non-mass lesions (NMLs) detected on breast ultrasound (US).

METHODS

In this single-center study, consecutive women with 503 NMLs detected on breast US between 2012 and 2019 were retrospectively identified. The lesions were randomly assigned to the training or testing dataset with a 70/30 split. Age, symptoms, lesion size, and US features were collected. Multivariate analyses were employed to identify risk factors associated with malignancy. The predictive model was developed by using conditional inference trees (CTREE).

RESULTS

There were 498 patients (50.9 ± 13.29 years; range, 22-88 years) with 503 NMLs with histopathologic results or > 2-year follow-up, including 224 (44.5%) benign and 279 (55.5%) malignant lesions. At multivariate analysis, age (odds ratio (OR) = 1.08, 95% confidence interval (CI), 1.06-1.11, p < 0.001), NMLs with focal mass effect (OR = 3.03, 95% CI, 1.59-5.81, p = 0.001), indistinct glandular-fat interface (GFI) (OR = 4.23, 95% CI, 2.31-7.73, p < 0.001), geographic (OR = 3.47, 95% CI, 1.20-10.8, p = 0.022) and mottled (OR = 3.67, 95% CI, 1.32-10.21, p = 0.013) patterns, and calcifications (OR = 2.15, 95% CI, 1.16-4.01, p = 0.016) were associated with malignancy. The GFI status, architectural patterns, general morphology, and calcifications were consistently identified as the strongest US predictors of malignancy using CTREE analysis. Based on these factors, individuals were stratified into six risk groups. The predictive model showed an area under the curve of 0.797 in the testing dataset.

CONCLUSION

The CTREE model efficiently aids in interpreting and managing ultrasound-detected breast NMLs, overcoming BI-RADS limitations by refining cancer risk stratification.

CLINICAL RELEVANCE STATEMENT

The CTREE model allows for the reclassification of BI-RADS categories into subgroups with varying malignancy probabilities, thus providing a valuable enhancement to the BI-RADS assessment for the diagnosis of ultrasound-detected NMLs, with the potential to minimize unnecessary biopsies.

KEY POINTS

• The indistinct glandular-fat interface (GFI) status, NML with focal mass effect, geographic or mottled patterns, and calcifications are the strongest imaging predictors of malignant non-mass lesions (NMLs) detected on breast US. • A practical system has been created to categorize NMLs found in breast US; each classification is associated with a degree of diagnostic certainty. • The model may contribute to patient stratification by determining the relative likelihood of malignancy and thus support clinical decision-making and evidence-based management.

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ

OBJECTIVE

To investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment.

METHODS

Consecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established.

RESULTS

A total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867.

CONCLUSION

Multi-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization.