The Role of Breast Ultrasound in Early Cancer Detection

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.

Diagnostic Performance of Prototype Handheld Ultrasound According to the Fifth Edition of BI-RADS for Breast Ultrasound Compared with Automated Breast Ultrasound among Females with Positive Lumps

(1) Objective: To evaluate the diagnostic performance of prototype handheld ultrasound compared to automated breast ultrasound, according to the fifth edition of BI-RADS categorization, among females with positive lumps. (2) Methods: A total of 1004 lesions in 162 participants who underwent both prototype handheld ultrasound and automated breast ultrasound were included. Two radiologists and a sonographer independently evaluated the sonographic features of each lesion according to the fifth BI-RADS edition. The kappa coefficient (κ) was calculated for each BI-RADS descriptor and final assessment category. The cross-tabulation was performed to see whether there were differences between the ABUS and prototype HHUS results. Specificity and sensitivity were evaluated and compared using the McNamar test. (3) Results: ABUS and prototype HHUS observers found the same number of breast lesions in the 324 breasts of the 162 respondents. There was no significant difference in the mean lesion size, with a maximum mean length dimension of 0.48 ± 0.33 cm. The assessment of the lesion’s shape, orientation, margin, echo pattern, posterior acoustic features, and calcification was obtained with good to excellent agreements between ABUS and prototype HHUS observers (κ = 0.70–1.0). There was absolutely no significant difference between ABUS and prototype HHUS in assessment of lesion except for lesion orientation p = 0.00. Diagnostic accuracy (99.8% and 97.7–98.9%), sensitivity (99.5% and 98.0–99.0%), specificity (99.8% and 99.6–99.8%), positive predictive value (98.1% and 90.3–96.2%), negative predictive value (90.0% and 84.4–88.7%), and areas under the curve (0.98 and 0.83–0.92; p < 0.05) were not significantly different between ABUS and prototype HHUS observers. (4) Conclusion: According to the fifth BI-RADS edition, automated breast ultrasound is not statistically significantly different from prototype handheld ultrasound with regard to interobserver variability and diagnostic performance.

Finite Element Modeling of Quantitative Ultrasound Analysis of the Surgical Margin of Breast Tumor

Ultrasound is commonly used as an imaging tool in the medical sector. Compared to standard ultrasound imaging, quantitative ultrasound analysis can provide more details about a material microstructure. In this study, quantitative ultrasound analysis was conducted through computational modeling to detect various breast duct pathologies in the surgical margin tissue. Both pulse-echo and pitch-catch methods were evaluated for a high-frequency (22–41 MHz) ultrasound analysis. The computational surgical margin modeling was based on various conditions of breast ducts, such as normal duct, ductal hyperplasia, DCIS, and calcification. In each model, ultrasound pressure magnitude variation in the frequency spectrum was analyzed through peak density and mean-peak-to-valley distance (MPVD) values. Furthermore, the spectral patterns of all the margin models were compared to extract more pathology-based information. For the pitch-catch mode, only peak density provided a trend in relation to different duct pathologies. For the pulse-echo mode, only the MPVD was able to do that. From the spectral comparison, it was found that overall pressure magnitude, spectral variation, peak pressure magnitude, and corresponding frequency level provided helpful information to differentiate various pathologies in the surgical margin.

Recognition of Heterogeneous Edges in Multiwavelength Transmission Images Based on the Weighted Constraint Decision Method

Multiwavelength light transmission imaging provides a possibility for early detection of breast cancer. However, due to strong scattering during the transmission process of breast tissue analysis, the transmitted image signal is weak and the image is blurred and this makes heterogeneous edge detection difficult. This paper proposes a method based on the weighted constraint decision (WCD) method to eliminate the erosion and checkerboard effects in image histogram equalization (HE) enhancement and to improve the recognition of heterogeneous edge. Multiwavelength transmission images of phantom are acquired on the designed experimental system and the mask image with high signal-to-noise ratio (SNR) is obtained by frame accumulation and an Otsu thresholding model. Then, during image enhancement the image is divided into low-gray-level (LGL) and high-gray-level (HGL) regions according to the distribution of light intensity in image. And the probability density distribution of gray level in the LGL and HGL regions are redefined respectively according to the WCD method. Finally, the reconstructed image is obtained based on the modified HE. The experimental results show that compared with traditional image enhancement methods, the WCD method proposed in this paper can greatly improve the contrast between heterogeneous region and normal region. Moreover, the correlation between the original image data is maintained to the greatest extent, so that the edge of the heterogeneity can be detected more accurately. In conclusion, the WCD method not only accurately identifies the edge of heterogeneity in multiwavelength transmission images, but it also could improve the clinical application of multiwavelength transmission images in the early detection of breast cancer.

An Improved Strategy for Detection and Localization of Nodules in Liver Tissues by a 16 MHz Needle Ultrasonic Probe Mounted on a Robotic Platform

This study presents an improved strategy for the detection and localization of small size nodules (down to few mm) of agar in excised pork liver tissues via pulse-echo ultrasound measurements performed with a 16 MHz needle probe. This work contributes to the development of a new generation of medical instruments to support robotic surgery decision processes that need information about cancerous tissues in a short time (minutes). The developed ultrasonic probe is part of a scanning platform designed for the automation of surgery-associated histological analyses. It was coupled with a force sensor to control the indentation of tissue samples placed on a steel plate. For the detection of nodules, we took advantage of the property of nodules of altering not only the acoustical properties of tissues producing ultrasound attenuation, but also of developing patterns at their boundary that can modify the shape and the amplitude of the received echo signals from the steel plate supporting the tissues. Besides the Correlation Index Amplitude (CIA), which is linked to the overall amplitude changes of the ultrasonic signals, we introduced the Correlation Index Shape (CIS) linked to their shape changes. Furthermore, we applied AND-OR logical operators to these correlation indices. The results were found particularly helpful in the localization of the irregular masses of agar we inserted into some excised liver tissues, and in the individuation of the regions of major interest over which perform the vertical dissections of tissues in an automated analysis finalized to histopathology. We correctly identified up to 89% of inclusions, with an improvement of about 14% with respect to the result obtained (78%) from the analysis performed with the CIA parameter only.

A new microdispersed albumin derivative potentially useful for radio-guided surgery of occult breast cancer lesions

This paper describes a new nuclear imaging agent, 2-(4-isothiocyanatobenzyl)−1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid of human albumin (HAC), potentially suitable for application in the Radio-guided Occult Lesion Localization (ROLL) of non-palpable mammalian cancerous lesions, as a tool to overtake the short radio-signal half-life of the technetium-99m based radiopharmaceutical currently used. This conjugate is a microsized powder aggregate, water-insoluble between pH 3 and 8.5, obtained by conjugating the protein with the macrocyclic chelating agent DOTA through a one-pot reaction in aqueous medium. The product has been fully characterized and is stable to the thermal conditions adopted for labeling; after radiolabeling with longer half-life radionuclides such as ¹⁷⁷Lu or ¹¹¹In, it has shown radiochemical purity (RCP) >90% and resulted stable when stored in saline or plasma for 6 days at 37 °C. A μPET/CT study, performed in vivo on adult female rats, showed that the radioactivity of HAC labeled with ⁶⁴Cu remained located in the mammary glands for at least 40 h, without diffusion or drainage in healthy tissues or in the lymphatic circulation. This new imaging agent might make the ROLL procedure more accessible, safe and flexible, promoting a significant time and cost reduction of this intervention. Moreover, HAC might also be used in other radio-guided surgical procedures in oncology.

PVCP-based anthropomorphic breast phantoms containing structures similar to lactiferous ducts for ultrasound imaging: A comparison with human breasts

The purpose of this work was to obtain an anthropomorphic phantom with acoustic properties similar to those of breast tissue, possessing lactiferous duct-like structures, which would be a first for this type of phantom. Breast lesions usually grow in glandular tissues or lactiferous ducts. Shape variations in these structures are detectable by using ultrasound imaging. To increase early diagnosis, it is important to develop computer-aided diagnosis (CAD) systems and improve medical training. Using tissue-like materials that mimic known internal structures can help achieve both of these goals. However, most breast ultrasound phantoms described in the literature emulate only fat tissues and lesion-like masses. In addition, commercially available phantoms claim to be realistic, but do not contain lactiferous duct structures. In this work, we collected reference images from both breasts of ten healthy female volunteers aged between 20 and 30 years using a 10 MHz linear transducer of a B-mode medical ultrasound system. Histograms of the grey scale distribution of each tissue component of interest, the grey level means, and standard deviations of the regions of interest were obtained. Phantoms were produced using polyvinyl chloride plastisol (PVCP) suspensions. The lactiferous duct-like structures were prepared using pure PVCP. Solid scatterers, such as alumina (mesh #100) and graphite powders (mesh #140) were added to the phantom matrix to mimic glandular and fat tissue, respectively. The phantom duct-like structure diameters observed on B-mode images (1.92 mm ± 0.44) were similar to real measures obtained with a micrometer (2.08 mm ± 0.23). The phantom ducts are easy to produce and are largely stable for at least one year. This phantom allows the researchers to elaborate the structure at their will and may be used in training and as a reference for development of CAD systems.