Many feedbacks to my last post reflected radiologists’ perception of ultrasound as a low-tech, unreliable imaging device.

Ultrasounds most manifested limitation by radiologists is that its performance is too-much user-dependent. This opinion finds support in numerous clinical studies concluding that ultrasound-based assessment of a cancer patient varies with the operator.

How come that an imaging technology that is not only  low-cost, simple to operate and risk-free to the patient, but has also gained a leading position in certain domain, like obstetrics,  is perceived as the underdog when it comes  to cancer assessment? Could it be because of its positioning as a “multi-purpose” system, which requires only very basic training?

If indeed this is the case, it doesn’t require “rocket-science” to turn it around. It only needs designing dedicated ultrasound machines who offer a comprehensive solution to one specific clinical need. Using such machines will require highly skilled operators who will enjoy a superior workflow, reporting tools and proven clinical guidelines.

The unsatisfactory reality of mammography-based breast cancer screening, as evident by epidemiology data and expert-panels’ reports, opens the opportunity to transform ultrasound into a winner in the niche-market of breast cancer screening and diagnosis. It’s a significant market that justifies the investment in ultrasound systems dedicated to detection and characterisation of breast cancer lesions.

No doubt, that the ability to provide accurate and standardized interpretation of such ultrasound systems’ scans is a pre-requisite. Ultrasound-based tissue characterisation is a must for any application aiming at standardized image interpretation. A sample out-of present ultrasound-based technologies aiming at providing some level of tissue-characterisation are listed below. Recent clinical studies show promising results using these technologies. It is worth watching carefully to see if any of those could be part of a future ultrasound-based solution to breast cancer screening.

Solid Breast Lesions: Clinical Experience with US-guided Diffuse Optical Tomography Combined with Conventional US

Results: Of the 136 biopsied lesions, 54 were carcinomas and 82 were benign. The average total hemoglobin concentration in the malignant group was 223.3 μmol/L ± 55.8 (standard deviation), and the average hemoglobin concentration in the benign group was 122.5 μmol/L ± 80.6 (P = .005). When the maximum hemoglobin concentration of 137.8 μmol/L was used as the threshold value, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of DOT with US localization were 96.3%, 65.9%, 65.0%, 96.4%, and 76.5%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of conventional US were 96.3%, 92.6%, 89.7%, 97.4%, and 93.4%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of conventional US combined with DOT were 100%, 93.9%, 91.5%, 100%, and 96.3%, respectively.

Conclusion: US-guided DOT combined with conventional US improves accuracy compared with DOT alone.

Breast Lesions: Quantitative Elastography with Supersonic Shear Imaging—Preliminary Results

Results: All breast lesions were detected at Supersonic Shear Imaging. Malignant lesions exhibited a mean elasticity value of 146.6 kPa ± 40.05 (standard deviation), whereas benign ones had an elasticity value of 45.3 kPa ± 41.1 (P < .001). Complicated cysts were differentiated from solid lesions because they had elasticity values of 0 kPa (no signal was retrieved from liquid areas).

Conclusion: Supersonic Shear Imaging provides quantitative elasticity measurements, thus adding complementary information that potentially could help in breast lesion characterization with B-mode US.

 Distinguishing Benign from Malignant Masses at Breast US: Combined US Elastography and Color Doppler US—Influence on Radiologist Accuracy

Results: The Az of B-mode US, US elastography, and Doppler US (average, 0.844; range, 0.797–0.876) was greater than that of B-mode US alone (average, 0.771; range, 0.738–0.798) for all readers (P = .001 for readers 1, 2, and 3; P < .001 for reader 4; P = .002 for reader 5). When both elastography and Doppler scores were negative, leading to strict downgrading, the specificity increased for all readers from an average of 25.3% (75.4 of 298; range, 6.4%–40.9%) to 34.0% (101.2 of 298; range, 26.5%–48.7%) (P < .001 for readers 1, 2, 4, and 5; P = .016 for reader 3) without a significant change in sensitivity.

Conclusion: Combined use of US elastography and color Doppler US increases both the accuracy in distinguishing benign from malignant masses and the specificity in decision-making for biopsy recommendation at B-mode US.

Evaluation of breast lesions by contrast enhanced ultrasound: Qualitative and quantitative analysis

A 57-year-old woman with a no-palpable lesion in the outer upper quadrant of left breast. (a) Gray scale image show an indistinct, hypo-echoic lesion. (b) Contrast enhanced image obtained 35 s after contrast agent injection showing a homogeneously and hyper-enhanced lesion. (c) Micro flow image obtained 38 s after contrast agent injection showing the enhanced mass with several radial vessels (arrow). (d) The time-intensity curve analysis show the peak intensity is 145.69 (intensity/1000), time to peak is 15.08 s, ascending slope is 8.98, descending slope is 1.03, the area under the curve is 7783.34. Pathologic analyses show this is an invasive ductal carcinoma.

Results: Histopathologic analysis of the 91 lesions revealed 44 benign and 47 malignant. For qualitative analysis, benign and malignant lesions differ significantly in enhancement patterns (p < 0.05). Malignant lesions more often showed heterogeneous and centripetal enhancement, whereas benign lesions mainly showed homogeneous and centrifugal enhancement. The detectable rate of peripheral radial or penetrating vessels was significantly higher in malignant lesions than in benign ones (p < 0.001). For quantitative analysis, malignant lesions showed significantly higher (p = 0.031) and faster enhancement (p = 0.025) than benign ones, and its time to peak was significantly shorter (p = 0.002). The areas under the ROC curve for qualitative, quantitative and combined analysis were 0.910 (Az1), 0.768 (Az2) and 0.926(Az3) respectively. The values of Az1 and Az3 were significantly higher than that for Az2 (p = 0.024 and p = 0.008, respectively). But there was no significant difference between the values of Az1 and Az3 (p = 0.625).

Conclusions: The diagnostic performance of qualitative and combined analysis was significantly higher than that for quantitative analysis. Although quantitative analysis has the potential to differentiate benign from malignant lesions, it has not yet improved the final diagnostic accuracy.

 Breast HistoScanning: the development of a novel technique to improve tissue characterization during breast ultrasound

Results: In 17 normal testing volumes, 3% of isolated voxels were classified as abnormal. In 15 abnormal testing volumes, the subclassifiers differentiated between malignant and benign tissue. BHS in benign tissue showed <1% abnormal voxels in cyst, hamartoma, papilloma and benign fibrosis. The fibroadenomas differed showing <5% and <24% abnormal voxels. Abnormal voxels in cancers increased with the volume of cancer at pathology.

Conclusions: HistoScanning reliably discriminated normal from abnormal tissue and could distinguish between benign and malignant lesions.

Written by: Dror Nir, PhD