3D Harmonic and Subharmonic Imaging for Characterizing Breast Lesions: A Multi-Center Clinical Trial.

OBJECTIVE: Breast cancer is the most frequent type of cancer among women. This multi-center study assessed the ability of 3D contrast-enhanced ultrasound to characterize suspicious breast lesions using clinical assessments and quantitative parameters. METHODS: Women with suspicious breast lesions scheduled for biopsy were enrolled in this prospective, study. Following 2D grayscale ultrasound and power Doppler imaging (PDI), a contrast agent (Definity; Lantheus) was administrated. Contrast-enhanced 3D harmonic imaging (HI; transmitting/receiving at 5.0/10.0 MHz), as well as 3D subharmonic imaging (SHI; transmitting/receiving at 5.8/2.9 MHz), were performed using a modified Logiq 9 scanner (GE Healthcare). Five radiologists independently scored the imaging modes (including standard-of-care imaging) using a 7-point BIRADS scale as well as lesion vascularity and diagnostic confidence. Parametric volumes were constructed from time-intensity curves for vascular heterogeneity, perfusion, and area under the curve. Diagnostic accuracy was determined relative to pathology using receiver operating characteristic (ROC) and reverse, step-wise logistical regression analyses. The κ-statistic was calculated for inter-reader agreement. RESULTS: Data were successfully acquired in 219 cases and biopsies indicated 164 (75%) benign and 55 (25%) malignant lesions. SHI depicted more anastomoses and vascularity than HI (P < .021), but there were no differences by pathology (P > .27). Ultrasound achieved accuracies of 82 to 85%, which was significantly better than standard-of-care imaging (72%; P < .03). SHI increased diagnostic confidence by 3 to 6% (P < .05), but inter-reader agreements were medium to low (κ < 0.52). The best regression model achieved 97% accuracy by combining clinical reads and parametric SHI. CONCLUSIONS: Combining quantitative 3D SHI parameters and clinical assessments improves the characterization of suspicious breast lesions.

Characterization of indeterminate breast lesions on B-mode ultrasound using automated machine learning models.

Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue but also increases false positives, resulting in unnecessary biopsies. Our study investigated the use of Google AutoML Vision (Mountain View, California), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound. Approach: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model, were evaluated, while also investigating training strategies to enhance model performances. Pathology from the patient's biopsy was used as a reference standard. Results: The image classification models trained under different conditions demonstrated areas under the precision-recall curve (AUC) ranging from 0.85 to 0.96 during internal validation. Once deployed, the model with highest internal performance demonstrated a sensitivity of 100% [95% confidence interval (CI) of 73.5% to 100%], specificity of 83.3% (CI=51.6% to 97.9%), positive predictive value (PPV) of 85.7% (CI=62.9% to 95.5%), and negative predictive value (NPV) of 100% (CI non-evaluable) in an independent dataset. The object detection model demonstrated lower performance internally during development (AUC=0.67) and during prediction in the independent dataset [sensitivity=75% (CI=42.8 to 94.5), specificity=80% (CI=51.9 to 95.7), PPV=75% (CI=50.8 to 90.0), and NPV=80% (CI=59.3% to 91.7%)], but was able to demonstrate the location of the lesion within the image. Conclusions: Two models appear to be useful tools for identifying and classifying suspicious areas on B-mode images of indeterminate breast lesions.

Characterizing Breast Lesions Using Quantitative Parametric 3D Subharmonic Imaging: A Multicenter Study.

RATIONALE AND OBJECTIVES: Breast cancer is the leading type of cancer among women. Visualization and characterization of breast lesions based on vascularity kinetics was evaluated using three-dimensional (3D) contrast-enhanced ultrasound imaging in a clinical study. MATERIALS AND METHODS: Breast lesions (n = 219) were imaged using power Doppler imaging (PDI), 3D contrast-enhanced harmonic imaging (HI), and 3D contrast-enhanced subharmonic imaging (SHI) with a modified Logiq 9 ultrasound scanner using a 4D10L transducer. Quantitative metrics of vascularity derived from 3D parametric volumes (based on contrast perfusion; PER and area under the curve; AUC) were generated by off-line processing of contrast wash-in and wash-out. Diagnostic accuracy of these quantitative vascular parameters was assessed with biopsy results as the reference standard. RESULTS: Vascularity was observed with PDI in 93 lesions (69 benign and 24 malignant), 3D HI in 8 lesions (5 benign and 3 malignant), and 3D SHI in 83 lesions (58 benign and 25 malignant). Diagnostic accuracy for vascular heterogeneity, PER, and AUC ranged from 0.52 to 0.75, while the best logistical regression model (vascular heterogeneity ratio, central PER, and central AUC) reached 0.90. CONCLUSION: 3D SHI successfully detects contrast agent flow in breast lesions and characterization of these lesions based on quantitative measures of vascular heterogeneity and 3D parametric volumes is promising.

Quantitative analysis of vascular heterogeneity in breast lesions using contrast-enhanced 3-D harmonic and subharmonic ultrasound imaging.

Ability to visualize breast lesion vascularity and quantify the vascular heterogeneity using contrast-enhanced 3-D harmonic (HI) and subharmonic (SHI) ultrasound imaging was investigated in a clinical population. Patients (n = 134) identified with breast lesions on mammography were scanned using power Doppler imaging, contrast-enhanced 3-D HI, and 3-D SHI on a modified Logiq 9 scanner (GE Healthcare). A region of interest corresponding to ultrasound contrast agent flow was identified in 4D View (GE Medical Systems) and mapped to raw slice data to generate a map of time-intensity curves for the lesion volume. Time points corresponding to baseline, peak intensity, and washout of ultrasound contrast agent were identified and used to generate and compare vascular heterogeneity plots for malignant and benign lesions. Vascularity was observed with power Doppler imaging in 84 lesions (63 benign and 21 malignant). The 3-D HI showed flow in 8 lesions (5 benign and 3 malignant), whereas 3-D SHI visualized flow in 68 lesions (49 benign and 19 malignant). Analysis of vascular heterogeneity in the 3-D SHI volumes found benign lesions having a significant difference in vascularity between central and peripheral sections (1.71 ± 0.96 vs. 1.13 ± 0.79 dB, p < 0.001, respectively), whereas malignant lesions showed no difference (1.66 ± 1.39 vs. 1.24 ± 1.14 dB, p = 0.24), indicative of more vascular coverage. These preliminary results suggest quantitative evaluation of vascular heterogeneity in breast lesions using contrast-enhanced 3-D SHI is feasible and able to detect variations in vascularity between central and peripheral sections for benign and malignant lesions.