Breast US: assessment of technical quality and image interpretation

Evaluation of artificial intelligent breast ultrasound on lesion detection and characterization compared with hand-held ultrasound in asymptomatic women

Introduction

To compare the accuracy of Artificial Intelligent Breast Ultrasound (AIBUS) with hand-held breast ultrasound (HHUS) in asymptomatic women and to offer recommendations for screening in regions with limited medical resources.

Methods

852 participants who underwent both HHUS and AIBUS were enrolled between December 2020 and June 2021. Two radiologists, who were unaware of the HHUS results, reviewed the AIBUS data and scored the image quality on a separate workstation. Breast imaging reporting and data system (BI-RADS) final recall assessment, breast density category, quantified lesion features, and examination time were evaluated for both devices. The statistical analysis included McNemar's test, paired t-test, and Wilcoxon test. The kappa coefficient and consistency rate were calculated in different subgroups.

Results

Subjective satisfaction with AIBUS image quality reached 70%. Moderate agreements were found between AIBUS with good quality images and HHUS for the BI-RADS final recall assessment (κ = 0.47, consistency rate = 73.9%) and breast density category (κ = 0.50, consistency rate = 74.8%). The lesions measured by AIBUS were statistically smaller and deeper than those measured by HHUS (P < 0.001), though they were not significant in clinical diagnosis (all < 3 mm). The total time required for the AIBUS examination and image interpretation was 1.03 (95% CI (0.57, 1.50)) minutes shorter than that of HHUS per case.

Conclusion

Moderate agreement was obtained for the description of the BI-RADS final recall assessment and breast density category. With image quality comparable to that of HHUS, AIBUS was superior for the efficiency of primary screening.

Efficiency and impact factors of anatomical intelligence for breast and hand-held ultrasound in lesion detection

PURPOSE

To investigate the efficiency and impact factors of anatomical intelligence for breast (AI-Breast) and hand-held ultrasound (HHUS) in lesion detection.

METHODS

A total of 172 outpatient women were randomly selected, underwent AI-Breast ultrasound (Group AI) once and HHUS twice. HHUS was performed by breast imaging radiologists (Group A) and general radiologists (Group B). For the AI-Breast examination, a trained technician performed the whole-breast scan and data acquisition, while other general radiologists performed image interpretation. The examination time and lesion detection rate were recorded. The impact factors for breast lesion detection, including breast cup size, number of lesions, and benign or malignant lesions were analyzed.

RESULTS

The detection rates of Group AI, A, and B were 92.8 ± 17.0%, 95.0 ± 13.6%, and 85.0 ± 22.9%, respectively. Comparable lesion detection rates were observed in Group AI and Group A (P > 0.05), but a significantly lower lesion detection rate was observed in Group B compared to the other two (both P < 0.05). Regarding missed diagnosis rates of malignant lesions, comparable performance was observed in Group AI, Group A, and Group B (8% vs. 4% vs. 14%, all P > 0.05). Scan times of Groups AI, A, and B were 262.15 ± 40.4 s, 237.5 ± 110.3 s, 281.2 ± 86.1 s, respectively. The scan time of Group AI was significantly higher than Group A (P < 0.01), but was slightly lower than Group B (P > 0.05). We found a strong linear correlation between scan time and cup size in Group AI (r = 0.745). No impacts of cup size and number of lesions were found on the lesion detection rate in Group AI (P > 0.05).

CONCLUSIONS

With the assist of AI-Breast system, the lesion detection rate of AI-Breast ultrasound was comparable to that of a breast imaging radiologist and superior to that of the general radiologist. AI-Breast ultrasound may be used as a potential approach for breast lesions surveillance.

Frequency-Dependent Spatial Coherence in Conventional and Chirp Transmissions

The development of adaptive imaging techniques is contingent on the accurate and repeatable characterization of ultrasonic image quality. Adaptive transmit frequency selection, filtering, and frequency compounding all offer the ability to improve target conspicuity by balancing the effects of imaging resolution, the signal-to-clutter ratio, and speckle texture, but these strategies rely on the ability to capture image quality at each desired frequency. We investigate the use of broadband linear frequency-modulated transmissions, also known as chirps, to expedite the interrogation of frequency-dependent tissue spatial coherence for real-time implementations of frequency-based adaptive imaging strategies. Chirp-collected measurements of coherence are compared to those acquired by individually transmitted conventional pulses over a range of fundamental and harmonic frequencies, in order to evaluate the ability of chirps to recreate conventionally acquired coherence. Simulation and measurements in a uniform phantom free of acoustic clutter indicate that chirps replicate not only the mean coherence in a region-of-interest but also the distribution of coherence values over frequency. Results from acquisitions in porcine abdominal and human liver models show that prediction accuracy improves with chirp length. Chirps are also able to predict frequency-dependent decreases in coherence in both porcine abdominal and human liver models for fundamental and pulse inversion harmonic imaging. This work indicates that the use of chirps is a viable strategy to improve the efficiency of variable frequency coherence mapping, thus presenting an avenue for real-time implementations for frequency-based adaptive strategies.

An Automated ALARA Method for Ultrasound: An Obstetric Ultrasound Feasibility Study

OBJECTIVES

Ultrasound users are advised to observe the ALARA (as low as reasonably achievable) principle, but studies have shown that most do not monitor acoustic output metrics. We developed an adaptive ultrasound method that could suggest acoustic output levels based on real-time image quality feedback using lag-one coherence (LOC).

METHODS

Lag-one coherence as a function of the mechanical index (MI) was assessed in 35 healthy volunteers in their second trimester of pregnancy. While imaging the placenta or the fetal abdomen, the system swept through 16 MI values ranging from 0.15 to 1.20. The LOC-versus-MI data were fit with a sigmoid curve, and the ALARA MI was selected as the point at which the fit reached 98% of its maximum.

RESULTS

In this study, the ALARA MI values were between 0.35 and 1.03, depending on the acoustic window. Compared to a default MI of 0.8, the pilot acquisitions suggested a lower ALARA MI 80% of the time. The contrast, contrast-to-noise ratio, generalized contrast-to-noise ratio, and LOC all followed sigmoidal trends with an increasing MI. The R2 of the fit was statistically significantly greater for LOC than the other metrics (P < .017).

CONCLUSIONS

These results suggest that maximum image quality can be achieved with acoustic output levels lower than the US Food and Drug Administration limits in many cases, and an automated tool could be used in real time to find the ALARA MI for specific imaging conditions. Our results support the feasibility of an automated, LOC-based implementation of the ALARA principle for obstetric ultrasound.

Low-toxicity FePt nanoparticles for the targeted and enhanced diagnosis of breast tumors using few centimeters deep whole-body photoacoustic imaging

A considerable amount of early breast tumors grown at a depth over 2 cm in breast tissues. With high near-infrared absorption of iron-platinum (FePt) nanoparticles, we achieved few centimeters deep photoacoustic (PA) imaging for the diagnosis of breast tumors. The imaging depth can extend over 5 cm in chicken breast tissues at the low laser energy density of 20 mJ/cm² (≤ ANSI safety limit). After anti-VEGFR conjugation and the tail-vein injection, we validated their targeting on tumor sites by the confocal microscopy and PA imaging. Using a home-made whole-body in vivo PA imaging, we found that the nanoparticles were rapidly cleared away from the site of the tumor and majorly metabolized through the liver. These results validated the clinical potential of the FePt nanoparticles in the low-toxicity PA theragnosis of early breast cancer and showed the capacity of our whole-body PA imaging technique on monitoring the dynamic biodistribution of nanoparticles in the living body.

Boundary-aware Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis

Breast ultrasound (US) is an effective imaging modality for breast cancer diagnosis. US computer-aided diagnosis (CAD) systems have been developed for decades and have employed either conventional handcrafted features or modern automatic deep-learned features, the former relying on clinical experience and the latter demanding large datasets. In this paper, we developed a novel BASDL method that integrates clinical-approved breast lesion boundary characteristics (features) into a semi-supervised deep learning (SDL) to achieve accurate diagnosis with a small training dataset. Original breast US images are converted to boundary-oriented feature maps (BFMs) using a distance-transformation coupled with a Gaussian filter. Then, the converted BFMs are used as the input of SDL network, which is characterized as lesion classification guided unsupervised image reconstruction based on stacked convolutional auto-encode (SCAE). We compared the performance of BASDL with conventional SCAE method and SDL method that used the original images as inputs, as well as SCAE method that used BFMs as inputs. Experimental results on two breast US datasets show that BASDL ranked the best among the four networks, with classification accuracy around 92.00±2.38%, which indicated that BASDL could be promising for effective breast US lesion CAD using small datasets.

Automated Breast Ultrasound Interpretation Times: A Reader Performance Study

RATIONALE AND OBJECTIVES

This study aimed to determine the average time for breast radiologists of varied experience to interpret automated breast ultrasound (ABUS) examinations.

MATERIALS AND METHODS

A reader performance study was conducted on female patients, with ACR BI-RADS 4 breast density classifications of C or D, who received both an ABUS screening examination and a digital mammogram from 2013 to 2014 at an academic institution. Three faculty breast radiologists with varied levels of ABUS experience (advanced, intermediate, novice) read all ABUS examinations, with interpretation times and final impressions (categorized as "normal" or "abnormal") recorded for each examination.

RESULTS

Ninety-nine patients were included, with all readers demonstrating an average ABUS interpretation time of less than 3 minutes. Compared to the other two readers, the intermediate reader had a significantly longer mean interpretation time at 2.6 minutes (95% confidence interval 2.4-2.8; P < .001). In addition to having the shortest mean interpretation time, the novice reader also demonstrated reduced times in subsequent interpretations, with a significant decrease in interpretation times of 3.1 seconds (95% confidence interval 0.4-5.8) for every 10 ABUS examinations interpreted (P < .05).

CONCLUSIONS

Overall, mean ABUS interpretation time by radiologists of all experience levels was short, at less than 3 minutes per examination, which should not deter radiologists from incorporating ABUS examinations into a busy clinical environment.

The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound

Screening ultrasound (US) is increasingly used as a supplement to mammography in women with dense breasts, and more than 80% of cancers detected by US alone are 1cm or smaller. An adaptive computer-aided diagnosis (CAD) system based on tumor size was proposed to classify breast tumors detected at screening US images using quantitative morphological and textural features. In the present study, a database containing 156 tumors (78 benign and 78 malignant) was separated into two subsets of different tumor sizes (<1cm and ⩾1cm) to explore the improvement in the performance of the CAD system. After adaptation, the accuracies, sensitivities, specificities and Az values of the CAD for the entire database increased from 73.1% (114/156), 73.1% (57/78), 73.1% (57/78), and 0.790 to 81.4% (127/156), 83.3% (65/78), 79.5% (62/78), and 0.852, respectively. In the data subset of tumors larger than 1cm, the performance improved from 66.2% (51/77), 68.3% (28/41), 63.9% (23/36), and 0.703 to 81.8% (63/77), 85.4% (35/41), 77.8% (28/36), and 0.855, respectively. The proposed CAD system can be helpful to classify breast tumors detected at screening US.

Technologist-performed handheld screening breast US imaging: how is it performed and what are the outcomes to date?

Breast density-inform legislation is increasing the need for data on outcomes of tailored screening. Dense parenchyma can mask cancers, and denser tissue is also more likely to develop breast cancer than fatty tissue. Digital mammography is standard for women with dense breasts. Supplemental screening magnetic resonance imaging should be offered to women who meet high-risk criteria. Supplemental screening ultrasonographic (US) imaging may be appropriate in the much larger group of women with dense breasts. Both physician- and technologist-performed screening US imaging increases detection of node-negative invasive breast cancer. To meet anticipated demand in the United States, screening US images will most likely be acquired by trained technologists rather than physicians. While automated US offers standard documentation, there are few data on outcomes. US has been used diagnostically for decades to characterize masses seen by using mammography, but training specific to screening has been lacking. Standard approaches to training and documentation of technologist-performed handheld screening US imaging are needed. This article reviews the current status of technologist-performed handheld screening breast US imaging.

Effects of soy isoflavones on mammographic density and breast parenchyma in postmenopausal women: a randomized, double-blind, placebo-controlled clinical trial

OBJECTIVE

This study aims to evaluate the effects of soy isoflavones on breast tissue in postmenopausal women.

METHODS

In this randomized, double-blind, placebo-controlled study, 80 women (aged ≥ 45 y and with amenorrhea >12 mo) with vasomotor symptoms were randomized to receive either 250 mg of standardized soy extract corresponding to isoflavone 100 mg/day (n = 40) or placebo (n = 40) for 10 months. Breasts were evaluated through mammographic density and breast parenchyma using ultrasound (US) at baseline and 10-month follow-up. Independent t test, analysis of variance, Mann-Whitney U test, and χ2 trend test were used in statistical analysis.

RESULTS

Baseline clinical characteristics showed no significant differences between the isoflavone group and the placebo group, with mean (SD) age of 55.1 (6.0) and 56.2 (7.7) years, mean (SD) menopause duration of 6.6 (4.8) and 7.1 (4.2) years, and mean (SD) body mass index of 29.7 (5.0) and 28.5 (4.9) kg/m2, respectively (P > 0.05). The study was completed by 32 women on isoflavone and 34 women on placebo. The groups did not differ in mammographic density or breast parenchyma by US (P > 0.05). Within each group, the baseline and final moments did not differ in mammography or US parameters significantly (P > 0.05).

CONCLUSIONS

The use of soy isoflavone extract for 10 months does not affect breast density, as assessed by mammography and US, in postmenopausal women.

Interobserver and Intraobserver Agreement of Sonographic BIRADS Lexicon in the Assessment of Breast Masses

BACKGROUND

BI-RADS was first developed in 1993 for mammography and in 2003 it was redesigned for ultrasonography (US). If the observer agreement is high, the method used in the classification of lesion would be reproducible.

OBJECTIVES

The aim of this study is to evaluate the inter- and intraobserver agreement of sonographic BI-RADS lexicon in the categorization and feature characterization of nonpalpable breast lesions.

PATIENTS AND METHODS

We included 223 patients with 245 nonpalpable breast lesions who underwent ultrasound-guided wire needle localization. Two radiologists retrospectively described each lesion using sonographic BI-RADS descriptors and final assessment. The observers were blinded to mammographic images, medical history and pathologic results. Inter- and intraobserver agreement was assessed using Kappa (κ) agreement coefficient.

RESULTS

The interobserver agreement for sonographic descriptors changed between fair and substantial. The highest agreement was detected for mass orientation (κ=0.66). The lowest agreement was found in the margin (κ=0.33). The interobserver agreement for BI-RADS final category was found as fair (κ=0.35). The intraobserver agreement for sonographic descriptors changed between substantial and almost perfect. The intraobserver agreement of BI-RADS result category was found as substantial for observer 1 (κ=0.64) and excellent for observer 2 (κ=0.83).

CONCLUSION

Our results demonstrated that each observer was self-consistent in interpreting US BI-RADS classification, while interobserver agreement was relatively poor. Although it has been ten years since the description of sonographic BI-RADS lexicon, further training and periodic performance evaluations would probably help to achieve better agreement among radiologists.

Interobserver variability of ultrasound elastography and the ultrasound BI-RADS lexicon of breast lesions

BACKGROUND

Elastographpy is a newly developed noninvasive imaging technique that uses ultrasound (US) to evaluate tissue stiffness. The interpretation of the same elastographic images may be variable according to reviewers. Because breast lesions are usually reported according to American College of Radiology Breast Imaging and Data System (ACR BI-RADS) lexicons and final category, we tried to compare observer variability between lexicons and final categorization of US BI-RADS and the elasticity score of US elastography.

METHODS

From April 2009 to February 2010, 1356 breast lesions in 1330 patients underwent ultrasound-guided core biopsy. Among them, 63 breast lesions in 55 patients (mean age, 45.7 years; range, 21-79 years) underwent both conventional ultrasound and elastography and were included in this study. Two radiologists independently performed conventional ultrasound and elastography, and another three observers reviewed conventional ultrasound images and elastography videos. Observers independently recorded the elasticity score for a 5-point scoring system proposed by Itoh et al., BI-RADS lexicons and final category using ultrasound BI-RADS. The histopathologic results were obtained and used as the reference standard. Interobserver variability was evaluated.

RESULTS

Of the 63 lesions, 42 (66.7 %) were benign, and 21 (33.3 %) were malignant. The highest value of concordance among all variables was achieved for the elasticity score (k = 0.59), followed by shape (k = 0.54), final category (k = 0.48), posterior acoustic features (k = 0.44), echogenecity and orientation (k = 0.43). The least concordances were margin (k = 0.26), lesion boundary (k = 0.29) and calcification (k = 0.3).

CONCLUSION

Elasticity score showed a higher level of interobserver agreement for the diagnosis of breast lesions than BI-RADS lexicons and final category.

A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability

To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.

Evaluation of the accuracy of a computer-aided diagnosis (CAD) system in breast ultrasound according to the radiologist's experience

RATIONALE AND OBJECTIVES

The aim of this study was to evaluate the performance of a computer-aided diagnosis (CAD) system for breast ultrasound to improve the characterization of breast lesions detected on ultrasound by junior and senior radiologists.

MATERIALS AND METHODS

One hundred sixty ultrasound breast lesions were randomly reviewed blindly by four radiologists with different levels of expertise (from 20 years [radiologist A] to 4 months [radiologist D]), with and without the help of an ultrasound CAD system (B-CAD version 2). All lesions had been biopsied. Sensitivity and specificity with and without CAD were calculated for each radiologist for the following evaluation criteria: Breast Imaging Reporting and Data System category and the final diagnosis (benign or malignant). Intrinsic sensitivity, specificity, and predictive values of CAD alone were also calculated.

RESULTS

CAD detected all cancers, and its use increased radiologists' sensitivity scores when this was possible (with vs without CAD: radiologist A, 99% vs 99%; radiologist B, 96% vs 87%; radiologist C, 95% vs 88%; radiologist D, 91% vs 88%). Seven additional cancers were diagnosed. However, the low specificity of CAD (48%) decreased the specificity of radiologists, especially of the more experienced among them (with vs without CAD: radiologist A, 46% vs 70%; radiologist B, 58% vs 80%; radiologist C, 57% vs 69%; radiologist D, 71% vs 71%).

CONCLUSIONS

CAD for breast ultrasound appears to be a useful tool for improving the diagnosis of malignant lesions for junior radiologists. Nevertheless, its low specificity must be taken into account to limit biopsies of benign lesions.

Utilizing a PACS-integrated ultrasound-guided breast biopsy simulation exercise to reinforce the ACR practice guideline for ultrasound-guided percutaneous breast interventional procedures during radiology residency

RATIONALE AND OBJECTIVES

A picture archiving and communication system (PACS)-integrated ultrasound-guided (USG) breast intervention simulation exercise was designed for radiology residency education. The purpose of this study was to describe the initial experience and determine if resident understanding of the American College of Radiology (ACR) practice guideline for the performance of USG percutaneous breast interventional procedures and procedural confidence is improved with the implementation of this simulation exercise.

MATERIALS AND METHODS

Radiology residents (n = 11) volunteered to perform percutaneous USG cyst aspiration, 14-gauge automated core biopsy, and 10-gauge vacuum core biopsy on turkey breast phantoms, with an emphasis on capturing ultrasound images demonstrating appropriate documentation of the procedure and image annotation according to the ACR practice guideline for USG percutaneous interventions. The images were transmitted to the PACS for subsequent attending radiologist review. Survey responses regarding procedural confidence and knowledge of the ACR practice guideline were compared between residents with and without the simulator experience.

RESULTS

Residents with simulation exercise experience showed statistically significant increases in confidence performing USG core biopsies, operating biopsy devices and ultrasound equipment, and knowledge of appropriate needle positioning and image annotation and documentation according to the ACR practice guideline. The increased confidence seen in performing USG cyst aspiration barely missed statistical significance (P = .056), likely because of residents' baseline familiarity with the procedure.

CONCLUSIONS

A PACS-integrated USG breast intervention simulation exercise increases residents' procedural confidence and understanding of the ACR practice guideline for the performance of USG percutaneous breast interventional procedures.

ACCOMP: Augmented cell competition algorithm for breast lesion demarcation in sonography

PURPOSE

Fully automatic and high-quality demarcation of sonographical breast lesions remains a far-reaching goal. This article aims to develop an image segmentation algorithm that provides quality delineation of breast lesions in sonography with a simple and friendly semiautomatic scheme.

METHODS

A data-driven image segmentation algorithm, named as augmented cell competition (ACCOMP) algorithm, is developed to delineate breast lesion boundaries in ultrasound images. Inspired by visual perceptual experience and Gestalt principles, the ACCOMP algorithm is constituted of two major processes, i.e., cell competition and cell-based contour grouping. The cell competition process drives cells, i.e., the catchment basins generated by a two-pass watershed transformation, to merge and split into prominent components. A prominent component is defined as a relatively large and homogeneous region circumscribed by a perceivable boundary. Based on the prominent component tessellation, cell-based contour grouping process seeks the best closed subsets of edges in the prominent component structure as the desirable boundary candidates. Finally, five boundary candidates with respect to five devised boundary cost functions are suggested by the ACCOMP algorithm for user selection. To evaluate the efficacy of the ACCOMP algorithm on breast lesions with complicated echogenicity and shapes, 324 breast sonograms, including 199 benign and 125 malignant lesions, are adopted as testing data. The boundaries generated by the ACCOMP algorithm are compared to manual delineations, which were confirmed by four experienced medical doctors. Four assessment metrics, including the modified Williams index, percentage statistic, overlapping ratio, and difference ratio, are employed to see if the ACCOMP-generated boundaries are comparable to manual delineations. A comparative study is also conducted by implementing two pixel-based segmentation algorithms. The same four assessment metrics are employed to evaluate the boundaries generated by two conventional pixel-based algorithms based on the same set of manual delineations.

RESULTS

The ACCOMP-generated boundaries are shown to be comparable to the manual delineations. Particularly, the modified Williams indices of the boundaries generated by the ACCOMP algorithm and the first and second pixel-based algorithms are 1.069 +/- 0.024, 0.935 +/- 0.024, and 0.579 +/- 0.013, respectively. If the modified Williams index is greater than or equal to 1, the average distance between the computer-generated boundaries and manual delineations is deemed to be comparable to that between the manual delineations.

CONCLUSIONS

The boundaries derived by the ACCOMP algorithm are shown to reasonably demarcate sonographic breast lesions, especially for the cases with complicated echogenicity and shapes. It suggests that the ACCOMP-generated boundaries can potentially serve as the basis for further morphological or quantitative analysis.

Acurácia dos achados ultrassonográficos do câncer de mama: correlação da classificação BI-RADS® e achados histológicos

OBJETIVO: O objetivo geral do estudo é avaliar a acurácia da ultrassonografia (BI-RADS) no diagnóstico do câncer de mama, e os objetivos específicos, descrever a frequência de apresentação dos diferentes achados ultrassonográficos e a avaliação da concordância entre observadores. MATERIAIS E MÉTODOS: Exames de 110 pacientes encaminhados para biópsia, com diagnóstico prévio de nódulos, foram reanalisados independentemente por dois médicos especialistas utilizando a nomenclatura do BI-RADS. Os achados histológicos foram utilizados como padrão-ouro. A acurácia dos achados foi determinada. As diferenças nos grupos de comparação foram analisadas com teste qui-quadrado para variáveis categóricas e a concordância entre os médicos foi calculada por meio da estatística kappa (κ). RESULTADOS: Cento e dez massas mamárias foram avaliadas pelo ultrassom, sendo que 76 (69%) foram benignas e 34 (30,9%), malignas. Foram observados, entre os radiologistas, sensibilidade variando entre 70,5% e 82,3%, valor preditivo negativo entre 81,1% e 87,5%, valor preditivo positivo entre 42,1% e 45,1%, especificidade entre 56,58% e 55,2% e acurácia entre 60,9% e 63,6%. Na avaliação entre observadores foi obtida concordância global considerada moderada (κ= 0,50). CONCLUSÃO: O BI-RADS 4ª edição é um acurado sistema para auxiliar os médicos na descrição das lesões mamárias e na tomada de condutas.

[US and dense breasts: where do we stand?]

The use of ultrasonography in dense breast remains a controversial topic. It is acknowledged that ultrasound as an adjunct to mammography increases the detection rate of breast cancers. However, the main limitation of US, in addition to its operator dependent nature, is its low specificity, leading to a high rate of false positive results. Several techniques can be used to improve the performance of US and cost/effectiveness ratio, such as Doppler imaging, harmonic imaging, spatial and frequency compound imaging, all of which are routinely available, and elastosonography, contrast US and 3D US which are still in development.

Clinical application of the BI-RADS final assessment to breast sonography in conjunction with mammography

OBJECTIVE

The objective of our study was to report the results of classification of sonographic findings according to BI-RADS and to calculate the positive predictive value (PPV) for each BI-RADS assessment category.

SUBJECTS AND METHODS

We prospectively classified 4,668 breast sonograms according to BI-RADS final assessment category. Suspicious sonographic findings were divided into major and minor suspicious findings. Category 1 was normal and category 2 was a benign finding such as cyst or nodule with uniform and intense hyperechogenicity. A nodule neither category 2 nor category 4 or 5 was defined as category 3. A nodule with one or more suspicious findings, not category 5, was defined as category 4. A nodule with two or more major suspicious findings was defined as category 5.

RESULTS

Of the 4,668 cases, 321 cases failed to undergo follow-up of at least 1 year. The PPV was 0.1% in category 1 (3/2,191), 0% in category 2 (0/773), 0.8% in category 3 (6/737), 31.1% in category 4 (161/519), and 96.9% in category 5 (123/127). In palpable lesions (n = 751), the PPV was 2.2% in category 1 (2/93), 0.9% in category 3 (2/217), 54% in category 4 (107/198), and 98% in category 5 (98/100). In nonpalpable lesions (n = 3,596), the PPV was 0.05% in category 1 (1/2,098), 0.8% in category 3 (4/520), 16.8% in category 4 (54/321), and 92.6% in category 5 (25/27).

CONCLUSION

As with mammography, placing sonographic lesions into BI-RADS categories is useful for predicting the presence of malignancy.

Role of sonography in the detection of contralateral metachronous breast cancer in an Asian population

OBJECTIVE

This study was undertaken to retrospectively assess the contribution of sonographic surveillance in the early detection of metachronous contralateral breast cancer.

MATERIALS AND METHODS

We retrospectively reviewed the pathologic, mammographic, and sonographic records of 51 patients with surgically proven metachronous bilateral breast cancer in 2,498 surgically proven breast cancers during 2000-2006. We first evaluated cancer staging according to the method of detection used to identify metachronous breast cancers. The sensitivity of imaging studies to identify the lesions was also assessed. We compared cancer staging on the basis of whether the patient was included in a screened group, which was one in which a mammogram and sonogram were obtained within 12 months of the pathologic diagnosis of metachronous cancer. Within the screened group, we compared cancer staging on the basis of whether a screening sonogram was obtained within 6 months of the diagnosis of metachronous cancer.

RESULTS

The staging of metachronous cancers showed no statistically significant differences related to detection method. The sensitivity of sonography was 94% and of mammography was 80% in the detection of metachronous cancers. The cancer stage in the screened group was 0 or stage I in 81% and that in the unscreened group was stage II or III in 71% (p < 0.05). Among the screened group, no significant difference was seen in staging regardless of whether a screening sonogram was obtained in the 6 months after diagnosis of metachronous cancer (p = 0.576).

CONCLUSION

Sonography alone detected 14% of metachronous contralateral breast cancers. The results of this study suggest that annual additional sonography with mammography contributes to the early detection of metachronous cancers. However, sonography every 6 months is unlikely to be helpful for the early detection of metachronous cancer.

Computer aided classification system for breast ultrasound based on Breast Imaging Reporting and Data System (BI-RADS)

Clinically, the ultrasound findings are evaluated by its sonographic characteristics and then assigned to assessment categories according to the definitions of Breast Imaging Reporting and Data System (BI-RADS) developed by the American College of Radiology. In this study, a computer-aided classification (CAC) system was proposed to classify the masses into assessment categories 3, 4 and 5, which simulated the clinical diagnosis of radiologists. Compared with current computer-aided diagnosis systems, the proposed CAC system classifies the indeterminate cases into BI-RADS category 4 for further diagnosis. Six hundred twenty-six cases were collected from three ultrasound systems and confirmed by pathology and retrospectively classified into categories 3, 4 and 5 by radiologists. The multinomial logistic regression model was trained as the CAC system for predicting the assessment category from the computerized BI-RADS features and from a set of machine-dependent factors. By using the machine-dependent factors to indicate the adopted ultrasound systems, the same regression model could be applied for the cases acquired from different ultrasound systems. A basic CAC system was trained by using the classification result of radiologists. A weighted CAC system, to improve the capacity of the basic CAC system in differentiating benign from malignant lesions, was trained by adding the pathologic result. Between the radiologists and the basic CAC system, a substantial agreement was indicated by Cohen's kappa statistic and the differences in either the performance indices or the A(Z) of receiver operating characteristic (ROC) analysis were not statistically significant. For the weighted CAC system, the performance indices accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 73.00% (457 of 626), 98.17% (215 of 219), 59.46% (242 of 407), 56.58% (215 of 380) and 98.37% (242 of 246), respectively; the A(Z) was 0.94; and the correlation with the radiologists was also substantial agreement. The indices accuracy and specificity of weighted CAC system, compared with those of the radiologists, were improved by 5.91% and 8.85%, respectively and the indices of sensitivity and NPV, compared with those of a conventional CAD system, were improved by 10.5% and 5.21%, respectively; all improvements were statistically significant. To classify the mass into BI-RADS assessment categories by the CAC system is feasible. Moreover, the proposed CAC system is flexible because it can be used to diagnose the cases acquired from different ultrasound systems.

Solid breast mass characterisation: use of the sonographic BI-RADS classification

PURPOSE

The aim of this study was to assess the reliability of the sonographic Breast Imaging Reporting and Data System (BI-RADS) classification in differentiating benign from malignant breast masses.

MATERIALS AND METHODS

A total of 292 female patients with breast masses undergoing biopsy between November 2004 and March 2006 in our department were included in this study. All lesions were classified according to the sonographic BI-RADS lexicon. Sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) for the sonographic BI-RADS lexicon and PPV and NPV for each lesion category and each sonographic descriptor were calculated. The chi(2) test and the Fischer exact test were used to evaluate our results.

RESULTS

Univariate analysis showed a significant difference between malignant and benign groups with regard to morphology (p<0.001), horizontal-vertical diameter ratio<1 (p<0.002), orientation (p<0.001), noncircumscribed margins (p<0.001), echogenic halo (p<0.001), hypoechoic pattern (p=0.035), shadowing (p<0.001) and surrounding tissue alterations (p=0.001). The cumulative risk for malignancy was 64 and 10 times higher, respectively, in categories 5 and 4 than in category 3.

CONCLUSIONS

The sonographic BI-RADS lexicon is an important system for describing and classifying breast lesions.

[Elastography of the breast: a prospective study of 122 lesions]

OBJECTIVE

To evaluate elastography in the characterization of breast nodules.

MATERIAL AND METHODS

Elastography (Hitachi, 7.5- to 13-MHz probe; Ueno classification, scores 1-3=benign, 4-5=malignant) was evaluated in 125 subclinical lesions in 114 patients. The results were compared to those of the ACR's BI-RADS sonography categories (benign=2 and 3, malignant=4 and 5) and to the results of the percutaneous samples taken and/or surgery (122 lesions evaluated, 59%<10 mm, 61 cancers, 61 benign lesions).

RESULTS

There were three technical failures (2.4%). The elastography was in agreement with histology for 101 lesions, with 13 false-negative results and eight false-positive results (sensitivity, 78.7%; specificity, 86.9%; PPV, 85.7%; NPV, 80.3%); versus agreement with the BI-RADS classification for 98 lesions with one false-negative result and 23 false-positive results (sensitivity, 98.4%; specificity, 47.5%; PPV, 65.2%; NPV, 96.7%).

CONCLUSION

Elastography is a simple and rapid complementary method that can improve the specificity and the PPV of morphological imaging studies of breast nodules with a low level of suspicion (BI-RADS categories 3 and 4a), which should decrease the rate of unnecessary benign biopsies.

Tissue harmonic imaging, frequency compound imaging, and conventional imaging: use and benefit in breast sonography

OBJECTIVE

The purpose of this study was to evaluate different sonographic settings (tissue harmonic, frequency compounding, and conventional imaging) and to determine which setting optimizes breast lesion detection and lesion characterization.

METHODS

Four hundred thirteen consecutive breast lesions (249 benign and 164 malignant) were evaluated by sonography using 4 different modes (conventional imaging at 14 MHz, tissue harmonic imaging at 14 MHz [THI], and frequency compound imaging at 10 MHz [CI10] and 14 MHz [CI14]). The images were reviewed by consensus by 2 breast radiologists. For each image, the lesion was graded for conspicuity, mass margin assessment, echo texture assessment, overall image quality, and posterior acoustic features.

RESULTS

For lesion conspicuity, THI and CI14 were better than conventional imaging (P < .01) and CI10 (P < .01) particularly against a fatty background (P < .01 for THI versus conventional for a fatty background versus P = .13 for a dense background). Frequency compound imaging at 10 MHz performed the best in echo texture assessment (P < .01), as well as overall image quality (P < .01). For margin assessment, CI10 performed better for deep and large (> or =1.5-cm) lesions, whereas CI14 performed better for small (<1.5-cm) and superficial lesions. Finally, THI and CI14 increased posterior shadowing (P < .01) and posterior enhancement (P < .01).

CONCLUSIONS

The standard breast examination incorporates 2 distinct processes, lesion detection and lesion characterization. With respect to detection, THI is useful, especially in fatty breasts. With respect to characterization, compound imaging improves lesion echo texture assessment. No single setting in isolation can provide the necessary optimized information for both of these tasks. As such, a combination approach is best.

Breast ultrasound computer-aided diagnosis using BI-RADS features

RATIONALE AND OBJECTIVES

Based on the definitions in mass category of Breast Imaging Reporting and Data System developed by American College of Radiology, eight computerized features including shape, orientation, margin, lesion boundary, echo pattern, and posterior acoustic feature classes are proposed.

MATERIALS AND METHODS

Our experimental database consists of 265 pathology-proven cases including 180 benign and 85 malignant masses. The capacity of each proposed feature in differentiating malignant from benign masses was validated by Student's t test and the correlation between each proposed feature and the pathological result was evaluated by point biserial coefficient. Binary logistic regression model was used to relate all proposed features and pathological result as a computer-aided diagnosis (CAD) system. The diagnostic value of each proposed feature in the CAD system was further evaluated by the feature selection methods. Additionally, the likelihood of malignancy for each individual feature was also estimated by binary logistic regression.

RESULTS

On each proposed feature, the malignant cases were significantly different from the benign ones. The correlation between the angular characteristic and pathological result was indicated as very high. Three substantial correlations appear in features irregular shape, undulation characteristic, and degree of abrupt interface, but the relationship for orientation feature is low. For the constructed CAD system, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 91.70% (243 of 265), 90.59% (77 of 85), 92.22% (166 of 180), 84.62% (77 of 91), and 95.40% (166 of 174), respectively, and the area index in the ROC analysis was 0.97. Compared with the significant contribution of angular characteristic, the diagnostic values of posterior acoustic feature and orientation feature were relatively low for the CAD system. When three or more angular characteristics are discovered or the degree of abrupt interface is lower than 18, the likelihood of malignancy could be predicted as greater than 40%.

CONCLUSION

The computerized BI-RADS sonographic features conform to the sign of malignancy in the clinical experience and efficiently help the CAD system to diagnose the mass.

BIRADS ultrasonography

The fourth edition of the BIRADS mammography appeared in 2003 and is now associated with the first editions of the BIRADS ultrasound and MRI. BIRADS is a system of assistance to the drafting of the reports more and more used in the world and soon directly implemented on mammography and ultrasound units. The categories of evaluation of the BIRADS allow a clear synthesis of the descriptive data resulting from the use of the lexicon and invite the radiologist to a reasoned, objective and less intuitive step. They give an action to be taken and responsibility to the radiologist and the referring physicians in the assumption of the patients.

[Breast ultrasonography: an overview]

The most important roles of ultrasound in breast imaging include the diagnosis of cysts and the characterization of masses that have been incompletely assessed by mammography. Ultrasonography techniques such as harmonic and compound imaging have recently been introduced and are more efficient than conventional imaging in terms of lesion conspicuity and the characterization of breast nodule. The BI-RADS classification is an important system for describing and classifying breast lesions. With this approach, a population of benign solid breast lesions that does not require biopsy can be accurately defined. Ultrasonography should be performed as first-line examination in juvenile, in pregnant women and in patients with inflammatory syndrome. Ultrasound can detect mammographically occult breast the way they develop.

Operator dependence of physician-performed whole-breast US: lesion detection and characterization

PURPOSE

To prospectively examine operator dependence of lesion detection, description, and interpretation when experienced breast radiologists perform whole-breast ultrasonography (US).

MATERIALS AND METHODS

Institutional review board approval was obtained for the HIPAA-compliant study. Ten women (aged 19-53 years; mean, 37.4 years; 20 breasts) with numerous known breast lesions consented to participate. Eleven breast radiologists, who passed experience and qualification requirements for a screening breast US trial and consented to participate, scanned both breasts in all participants and documented images of each detected lesion and its size, location, features, palpability, and Breast Imaging Reporting and Data System final assessment. Intraclass correlation coefficients (ICCs) were used to measure agreement on lesion size and location, and kappa statistics were calculated for agreement on features and final assessments compared with consensus.

RESULTS

Eighty-eight unique lesions were identified by at least two investigators (five to 13 lesions per participant). Mean diameter was 6.7 mm (standard error, 0.4; range, 2-22 mm), and eight lesions (9%) were palpable. Of 968 potential detections (88 lesions, 11 investigators), 536 (55%) detections were made. Individual investigators detected between 43 (49%) and 58 (66%) lesions. Larger lesions were more consistently detected: Detection rates were six of 33 lesions (18%) at 3 mm or smaller; 164 of 374 (43.9%) at 3.1-5 mm; 145 of 275 (52.7%) at 5.1-7 mm; 119 of 176 (67.6%) at 7.1-9 mm; 38 of 44 (86%) at 9.1-11 mm; and 64 of 66 (97%) lesions larger than 11 mm (P < .001). ICCs for clockface, distance from nipple, and individual lesion diameter all exceeded 0.7, indicating high reliability. For shape, margins, and final assessments of solid lesions, kappa values were 0.62, 0.67 (substantial agreement), and 0.52 (moderate agreement), respectively. Of 110 detections of consensus cysts 8 mm and smaller, 15 (14%) detections were considered to be of solid lesions by at least one reader.

CONCLUSION

Larger lesions (>11 mm) are most consistently detected, with fewer than half of lesions 5 mm or smaller in mean diameter identified; substantial agreement was found for description of lesion size, location, and key features, and moderate agreement was found for lesion management.

Characterization of solid breast masses: use of the sonographic breast imaging reporting and data system lexicon

OBJECTIVE

The purpose of this study was to determine the reliability of sonographic American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) classification in differentiating benign from malignant breast masses.

METHODS

One hundred seventy-eight breast masses studied by sonography with a known diagnosis were reviewed. All lesions were classified according to the sonographic BI-RADS lexicon. Pathologic results were compared with sonographic features. Sensitivity, specificity, accuracy, and positive predictive value (PPV) and negative predictive value (NPV) for the sonographic BI-RADS lexicon were calculated.

RESULTS

Twenty-six cases were assigned to class 3, 73 to class 4, and 79 to class 5. Pathologic results revealed 105 malignant and 73 benign lesions. The sonographic BI-RADS lexicon showed 71.3% accuracy, 98.1% sensitivity, 32.9% specificity, 67.8% PPV, and 92.3% NPV. The NPV for class 3 was 92.3%. The PPVs for classes 4 and 5 were 46.6% and 87.3%. Typical signs of malignancy were irregular shape, antiparallel orientation, noncircumscribed margin, echogenic halo, and decreased sound transmission. Typical signs of benignity were oval shape and circumscribed margin.

CONCLUSIONS

The sonographic BI-RADS lexicon is an important system for describing and classifying breast lesions.

Multiparametric and multinuclear magnetic resonance imaging of human breast cancer: current applications

The exploration of novel imaging methods that have the potential to improve specificity for the identification of malignancy is still critically needed in breast imaging. Changes in physiologic alterations of soft tissue water associated with breast cancer can be visualized by magnetic resonance (MR) imaging. However, it is unlikely that a single MR parameter can characterize the complexity of breast tissue. Techniques such as multiparametric MR imaging, proton magnetic resonance spectroscopic (MRSI) imaging, and 23Na sodium MR imaging when used in combination provide a comprehensive data set with potentially more power to diagnose breast disease than any single measure alone. A combination of MR, MRSI, and 23Na sodium MR parameters may be examined in a single MR imaging examination, potentially resulting in improved specificity for radiologic evaluation of malignancy.

Likelihood of malignancy in breast lesions characterised by ultrasound with a combined diagnostic score

To determine the positive predictive value of breast ultrasound (US) categories and US features, isolated and in combination, 398 consecutive sonographically diagnosed breast tumours with histologic or cytologic diagnosis were reviewed. Tumour characterisation and the sonographer's diagnoses were recorded prospectively using the diagnostic classification of the European Society of Mastology (EUSOMA) (U2 = probably benign lesion, U3 = an abnormality present of indeterminate significance, U4 = features suspicious of malignancy). In addition, based on the likelihood of malignancy of each US characteristic, a diagnostic score was developed. These two measures were compared. US-guided biopsy revealed 338 benign and 60 (55 invasive and 5 noninvasive) malignant lesions. EUSOMA and diagnostic score classifications did not differ significantly. If all breast tumours classified U3 and U4 were to be tested, every second biopsy (48.3%) would have revealed a carcinoma with a negative predictive value of 99.3%. The frequency of carcinoma in sonographically benign lesions (U2 or score 1) was 0.7 and 2.2%, respectively, an incidence similar to that with mammographic lesions classified as BI-RADS 3 (Breast Imaging Reporting and Data System, probably benign, short interval follow-up suggested). Thus, given that clinical symptoms and real-time imaging influence the sonographer's interpretation, the proposed diagnostic score can improve the diagnostic accuracy of the breast sonogram with the result of reducing invasive testing and maintaining a high detection rate.

Current uses of ultrasound in the evaluation of the breast

Ultrasound is an important imaging modality in evaluating the breast. One of the most common uses of ultrasound is to help distinguish benign from malignant breast disease, primarily with gray-scale ultrasound but also with Doppler ultrasound. Another common use is to provide guidance for interventional procedures. Less common uses include assisting in staging of breast cancer and evaluating patients with implants. Recently there has been an interest in using ultrasound to screen asymptomatic women for breast cancer, as is done with mammography. Further studies must be performed to assess if this reduces mortality from breast cancer. Although primarily used to image the female breast, ultrasound also can be used to evaluate breast-related concerns in men. Uses of contrast-enhanced ultrasound are still experimental and would add an invasive component to an otherwise noninvasive study.