Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist

Purpose

The purpose of this study was to evaluate the diagnostic performance of S-Detect when applied to breast ultrasonography (US), and the agreement with an experienced radiologist specializing in breast imaging.

Methods

From June to August 2015, 192 breast masses in 175 women were included. US features of the breast masses were retrospectively analyzed by a radiologist who specializes in breast imaging and S-Detect, according to the fourth edition of the American College of Radiology Breast Imaging Reporting and Data System lexicon and final assessment categories. Final assessments from S-Detect were in dichotomized form: possibly benign and possibly malignant. Kappa statistics were used to analyze the agreement between the radiologist and S-Detect. Diagnostic performance of the radiologist and S-Detect was calculated, including sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and area under the receiving operator characteristics curve.

Results

Of the 192 breast masses, 72 (37.5%) were malignant, and 120 (62.5%) were benign. Benign masses among category 4a had higher rates of possibly benign assessment on S-Detect for the radiologist, 63.5% to 36.5%, respectively (P=0.797). When the cutoff was set at category 4a, the specificity, PPV, and accuracy was significantly higher in S-Detect compared to the radiologist (all P<0.05), with a higher area under the receiver operator characteristics curve of 0.725 compared to 0.653 (P=0.038). Moderate agreement (k=0.58) was seen in the final assessment between the radiologist and S-Detect.

Conclusion

S-Detect may be used as an additional diagnostic tool to improve the specificity of breast US in clinical practice, and guide in decision making for breast masses detected on US.

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions.

A pilot study to determine the diagnostic criteria of spiculated masses for BI-RADS MRI category 5: when to perform re-biopsy after discordant pathologic result?

BACKGROUND

This study aimed to investigate (1) the MRI and clinical findings useful to differentiate malignant from benign spiculated masses and (2) the diagnostic criteria of spiculated masses for BI-RADS MRI category 5, for which any non-malignant biopsy result is considered discordant and a re-biopsy is recommended.

MATERIALS AND METHODS

Spiculated breast masses, depicted by 3.0/1.5-T contrast-enhanced MRI between June 2008 and March 2014, were retrospectively analyzed. Patient's age, lesion size, minimum/average apparent diffusion coefficient values (ADCmin/ADCave), and BI-RADS descriptors were compared between malignant and benign lesions. Based on these results, we assessed criteria to define category 5 spiculated masses with a ≥95 % probability of malignancy and evaluated their diagnostic performance.

RESULTS

A total of 140 lesions (Malignant group, n = 131; Benign group, n = 9) were analyzed. Patient's age, lesion size, ADCmin and ADCave showed significant differences between the two groups, while none of the BI-RADS descriptors, including kinetic curve assessment, showed any significant difference in frequency. Multivariate logistic regression analysis demonstrated that patient's age and lesion size were the significant predictive factors of malignancy. Of all the assessed criteria for category 5 spiculated masses, "age >50 years or size >9 mm, or both" were selected as the best criteria to minimize the possibility of unnecessary re-biopsies and inappropriate follow-up for malignancies.

CONCLUSIONS

Patient's age and lesion size are useful to differentiate malignant from benign spiculated breast masses. In cases with non-malignant biopsy results, spiculated masses with "age >50 years or size >9 mm, or both" are more likely malignant.

Dynamic contrast-enhanced breast MRI at 7T and 3T: an intra-individual comparison study

The aim of this study is to compare the current state of lesion identification, the BI-RADS classification and the contrast-enhancement behavior at 7T and 3T breast MRI in the same patient group. Twenty-seven patients with thirty suspicious lesions were selected for this prospective study and underwent both 7T and 3T MRI. All examinations were rated by two radiologists (R1 and R2) independently on image quality, lesion identification and BI-RADS classification. We assessed sensitivity, specificity, NPV and PPV, observer agreement, lesion sizes, and contrast-enhancement-to-noise ratios (CENRs) of mass lesions. Fifteen of seventeen histopathological proven malignant lesions were detected at both field strengths. Image quality of the dynamic series was good at 7T, and excellent at 3T (P = 0.001 for R1 and P = 0.88 for R2). R1 found higher rates of specificity, NPV and PPV at 7T when compared to 3T, while R2 found the same results for sensitivity, specificity, NPV and PPV for both field strengths. The observers showed excellent agreement for BI-RADS categories at 7T (κ = 0.86) and 3T (κ = 0.93). CENRs were higher at 7T (P = 0.015). Lesion sizes were bigger at 7T according to R2 (P = 0.039). Our comparison study shows that 7T MRI allows BI-RADS conform analysis. Technical improvements, such as acquisition of T2w sequences and adjustment of B1+ field inhomogeneity, are still necessary to allow clinical use of 7T breast MRI.

Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

RATIONALE AND OBJECTIVES

The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors.

MATERIALS AND METHODS

Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method.

RESULTS

The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P < 0.001) and the genetic model (AUC = .601; P < 0.001).

CONCLUSIONS

BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy.

Breast Density Estimation with Fully Automated Volumetric Method: Comparison to Radiologists' Assessment by BI-RADS Categories

RATIONALE AND OBJECTIVES

The objective of our study was to calculate mammographic breast density with a fully automated volumetric breast density measurement method and to compare it to breast imaging reporting and data system (BI-RADS) breast density categories assigned by two radiologists.

MATERIALS AND METHODS

A total of 476 full-field digital mammography examinations with standard mediolateral oblique and craniocaudal views were evaluated by two blinded radiologists and BI-RADS density categories were assigned. Using a fully automated software, mean fibroglandular tissue volume, mean breast volume, and mean volumetric breast density were calculated. Based on percentage volumetric breast density, a volumetric density grade was assigned from 1 to 4.

RESULTS

The weighted overall kappa was 0.895 (almost perfect agreement) for the two radiologists' BI-RADS density estimates. A statistically significant difference was seen in mean volumetric breast density among the BI-RADS density categories. With increased BI-RADS density category, increase in mean volumetric breast density was also seen (P < 0.001). A significant positive correlation was found between BI-RADS categories and volumetric density grading by fully automated software (ρ = 0.728, P < 0.001 for first radiologist and ρ = 0.725, P < 0.001 for second radiologist). Pairwise estimates of the weighted kappa between Volpara density grade and BI-RADS density category by two observers showed fair agreement (κ = 0.398 and 0.388, respectively).

CONCLUSIONS

In our study, a good correlation was seen between density grading using fully automated volumetric method and density grading using BI-RADS density categories assigned by the two radiologists. Thus, the fully automated volumetric method may be used to quantify breast density on routine mammography.

Breast image pre-processing for mammographic tissue segmentation

During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

Ultrasound Elastography Combined With BI-RADS-US Classification System: Is It Helpful for the Diagnostic Performance of Conventional Ultrasonography?

PURPOSE

To evaluate the additive diagnostic performance of ultrasound elastography (UE) to ultrasound (US) with the 2003 or 2013 Breast Imaging Reporting and Data System (BI-RADS)-US classification systems for the differentiation of benign and malignant breast lesions.

METHODS

From June 2010 to December 2012, 738 women with 770 breast lesions were recruited into this retrospective study. Breast lesions were evaluated separately by US, UE, and both. US assessment was based on the 2003 or 2013 BI-RADS-US, and UE assessment was based on a previously reported 5-point scale. Diagnostic performance of US, UE, and both was compared.

RESULTS

Before category 4 lesions were subdivided, the area under the receiver operating characteristic curve (AUC) for US, UE, and both were, respectively, 0.735, 0.877, 0.878 (P < .01). When subcategories of 4 lesions were considered, the AUC for US, UE, and both were, respectively, 0.865, 0.877, and 0.883 (P > .05). Adding UE to analysis of 4A lesions can decrease the percentages of malignancy to 2.56%.

CONCLUSION

When the 2003 BI-RADS was considered, UE could give US some help in differentiating breast lesions. However, when the 2013 BI-RADS was considered, UE gave little help to US, although it reduced unnecessary biopsies of benign category 4A lesions.

A history of breast cancer and older age allow risk stratification of mammographic BI-RADS 3 ratings in the diagnostic setting

OBJECTIVE

The objective was to investigate whether risk stratification of mammographic Breast Imaging: Reporting and Data System (BI-RADS) 3 can be accomplished in the diagnostic setting.

METHODS

We analyzed 4941 BI-RADS-3-rated patients (23 malignant outcomes) and built logistic-regression models with age, personal and family history of breast cancer, fibroglandular density, and additional mammographic findings as predictive variables.

RESULTS

A personal history of breast cancer (odds ratio: 5.53) and older age (odds ratio: 12.44/10.93 for age 50-64/>64) are independent risk factors. Patients with both risk factors have a risk >2%.

CONCLUSION

Biopsy may be warranted in older patients with a history of breast cancer who would be otherwise assigned BI-RADS 3.

Breast Imaging-Reporting and Data System (BI-RADS) classification in 51 excised palpable pediatric breast masses

INTRODUCTION

The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) classification was developed to risk stratify breast lesions and guide surgical management based on imaging. Previous studies validating BI-RADS for US do not include pediatric patients. Most pediatric breast masses present as palpable lesions and frequently undergo ultrasound, which is often accompanied with a BI-RADS classification. Our study aimed to correlate BI-RADS with pathology findings to assess applicability of the classification system to pediatric patients.

METHODS

We performed a retrospective review of all patients who underwent excision of a breast mass at a single center from July 2010 to November 2013. We identified all patients who underwent preoperative ultrasound with BI-RADS classification. Demographic data, imaging results, and surgical pathology were analyzed and correlated.

RESULTS

A total of 119 palpable masses were excised from 105 pediatric patients during the study period. Of 119 masses, 81 had preoperative ultrasound, and BI-RADS categories were given to 51 masses. Of these 51, all patients were female and the average age was 15.9 years. BI-RADS 4 was given to 25 of 51 masses (49%), and 100% of these lesions had benign pathology, the most common being fibroadenoma.

CONCLUSIONS

Treatment algorithm based on BI-RADS classification may not be valid in pediatric patients. In this study, all patients with a BI-RADS 4 lesion had benign pathology. BI-RADS classification may overstate the risk of malignancy or need for biopsy in this population. Further validation of BI-RADS classification with large scale studies is needed in pediatric and adolescent patients.

Subcategorization of Suspicious Breast Lesions (BI-RADS Category 4) According to MRI Criteria: Role of Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging

OBJECTIVE

The purposes of this study were to investigate whether dynamic contrast-enhanced MRI is adequate for subcategorization of suspicious lesions (BI-RADS category 4) and to evaluate whether use of DWI improves diagnostic performance.

MATERIALS AND METHODS

The study group was composed of 103 suspicious lesions found in 83 subjects. Patient ages and lesion sizes were compiled, and two radiologists reanalyzed the images; subcategorized the findings as BI-RADS 4A, 4B, or 4C; and calculated apparent diffusion coefficient (ADC) values. The stratified variables were tested by univariate analysis and inserted in two multivariate predictive models, which were used to generate ROC curves and compare AUCs. Positive predictive values (PPVs) for each subcategory and ADC level were calculated, and interobserver agreement was tested.

RESULTS

Forty-four (42.7%) suspicious findings proved malignant. Except for age (p = 0.08), all stratified predictor variables were significant in univariate analyses (p < 0.01). Logistic regression models did not differ substantially after comparison of the ROC curves (p = 0.09), but the one including ADC values was slightly better: AUC of 0.89 (95% CI, 0.82-0.95) against AUC of 0.85 (95% CI, 0.78-0.93). PPV increased progressively in each BI-RADS 4 subcategory (4A, 0.15; 4B, 0.37; 4C, 0.84). ADC values of 1.10 × 10(-3) mm(2)/s or less had the second highest PPV (0.77). Interobserver agreement was substantial at a kappa value of 0.80 (95% CI, 0.70-0.90; p < 0.01).

CONCLUSION

Risk stratification of suspicious lesions (BI-RADS category 4) can be satisfactorily performed with DCE-MRI and slightly improved when DWI is introduced.

Complete internal audit of a mammography service in a reference institution for breast imaging

Objective

Undertaking of a complete audit of the service of mammography, as recommended by BI-RADS^®, in a private reference institution for breast cancer diagnosis in the city of São Paulo, SP, Brazil, and comparison of results with those recommended by the literature.

Materials and Methods

Retrospective, analytical and cross-sectional study including 8,000 patients submitted to mammography in the period between April 2010 and March 2011, whose results were subjected to an internal audit. The patients were followed-up until December 2012.

Results

The radiological classification of 7,249 screening mammograms, according to BI-RADS, was the following: category 0 (1.43%), 1 (7.82%), 2 (80.76%), 3 (8.35%), 4 (1.46%), 5 (0.15%) and 6 (0.03%). The breast cancer detection ratio was 4.8 cases per 1,000 mammograms. Ductal carcinoma in situ was found in 22.8% of cases. Positive predictive values for categories 3, 4 and 5 were 1.3%, 41.3% and 100%, respectively. In the present study, the sensitivity of the method was 97.1% and specificity, 97.4%.

Conclusion

The complete internal audit of a service of mammography is essential to evaluate the quality of such service, which reflects on an early breast cancer detection and reduction of mortality rates.

[Serpiginous calcifications in breast filariasis: A descriptor not included in the BI-RADS classification system]

Filariasis is a parasitic disease with a benign course caused by nematodes. Filariasis is endemic in some tropical regions, and immigration has made it increasingly common in some centers in Spain. The death of the parasites can lead to calcifications that are visible in mammograms; these calcifications have specific characteristics and should not be confused with those arising in other diseases. However, the appearance of calcifications due to filariasis is not included in the most common systems used for the classification of calcifications on mammograms (BI-RADS), and this can lead to confusion. In this article, we discuss the need to update classification systems and warn radiologists about the appearance of these calcifications to ensure their correct diagnosis and avoid confusion with other diseases.

Prognostic importance of ultrasound BI-RADS classification in breast cancer patients

OBJECTIVE

We investigated the prognostic importance of pre-operative Breast Imaging Reporting and Data System classification in ultrasound imaging.

METHODS

Histopathological differences and disease-free survival were analyzed in Breast Imaging Reporting and Data System classification subgroups. Univariate and multivariate analyses were used to identify the prognostic factors.

RESULTS

We identified 531 invasive breast cancer patients eligible for this study. Most patients classified as Breast Imaging Reporting and Data System 5 had large tumors and a higher rate of lymph node metastasis. However, hormonal receptor or HER-2 status did not differ according to Breast Imaging Reporting and Data System classification. During a median post-operative follow-up of 42.0 months, 43 patients were diagnosed with a disease-specific event. Disease-free survival was significantly lower in patients with Breast Imaging Reporting and Data System 5 than in patients with Breast Imaging Reporting and Data System 3-4. Subgroup analysis of patients with invasive breast cancer of Stage I showed that Breast Imaging Reporting and Data System 5 was an independent negative prognostic indicator of disease-free survival (hazard ratio 9.195; 95% confidence interval, 1.175-71.955; P = 0.035).

CONCLUSIONS

Breast Imaging Reporting and Data System classification might be considered as prognostic factors especially in Stage I breast cancer. Further confirmatory studies are needed.

Lesion type and reader experience affect the diagnostic accuracy of breast MRI: a multiple reader ROC study

PURPOSE

To evaluate the influence of lesion type (mass versus non-mass) and reader experience on the diagnostic performance of breast MRI (BMRI) in a non-screening setting.

MATERIALS AND METHODS

Consecutive patients (mean age, 55 ± 12 years) with breast lesions that were verified by biopsy or surgery, and who had had BMRI as part of their diagnostic workup, were eligible for this retrospective single-center study. Cancers diagnosed by biopsy before BMRI were excluded to eliminate biological and interpretation bias due to biopsy or chemotherapy effects (n=103). Six blinded readers (experience level, high (HE, n=2); intermediate (IE, n=2); and low (LE, n=2)) evaluated all examinations and assigned independent MRI BI-RADS ratings. Lesion type (mass, non-mass, focal) was noted. Receiver operating characteristics (ROC) and logistic regression analysis was performed to compare diagnostic accuracies.

RESULTS

There were 259 histologically verified lesions (123 malignant, 136 benign) investigated. There were 169 mass (103 malignant, 66 benign) and 48 non-mass lesions (19 malignant, 29 benign). Another 42 lesions that met the inclusion criteria were biopsied due to conventional findings (i.e., microcalcifications, architectural distortions), but did not enhance on MRI (41 benign, one DCIS). ROC analysis revealed a total area under the curve (AUC) between 0.834 (LE) and 0.935 (HI). Logistic regression identified a significant effect of non-mass lesions (P<0.0001) and reader experience (P=0.005) on diagnostic performance.

CONCLUSIONS

Non-mass lesion type and low reader experience negatively affect the diagnostic performance of breast MRI in a non-screening setting.

Non-mass-like breast lesions at ultrasonography: feature analysis and BI-RADS assessment

OBJECTIVE

To analyze the features of non-mass-like (NML) breast lesions on ultrasound (US) and determine their corresponding malignancy rate and to stratify these lesion patterns according to US BI-RADS categories.

MATERIALS AND METHODS

One hundred sixty-four consecutive lesions were retrospectively classified into four types according to the US features, the corresponding positive predictive values (PPVs) were obtained. Clinical, imaging, and histopathological findings were reviewed.

RESULTS

Among the 164 lesions, 39 (24%) were classified as type Ia, 14 (8%) as type Ib, 39 (24%) as type IIa, 19 (12%) as type IIb, 19 (12%) as type III, and 34 (21%) as type IV. The PPVs for malignancy were 21% for type Ia, 79% for type Ib, 10% for type IIa, 58% for type IIb, 16% for type III, and 21% for type IV. All NML lesions were classified as BI-RADS category 4a (type IIa), 4b (type Ia, III and IV) and 4c (type Ib and IIb) according to their PPVs. There was a significantly higher frequency of malignancy among lesions of type Ib and type IIb compared with the other types (P<0.01 for each). Lesions with associated calcifications, presence of abnormal axillary nodes, or a mammographic finding of suspected malignancy had a higher probability of malignancy (P<0.05 for each).

CONCLUSION

US is useful in clarifying the indication for biopsy of NML lesions. The types of US classifications used in our study establish reliable references for the NML patterns when stratified according to the BI-RADS categories.

Comparison of strain and shear wave elastography for the differentiation of benign from malignant breast lesions, combined with B-mode ultrasonography: qualitative and quantitative assessments

Our aim was to compare the diagnostic performance of strain elastography (SE) and shear-wave elastography (SWE), combined with B-mode ultrasonography (US), in breast cancer. For 79 breast lesions that underwent SE and SWE, two radiologists reviewed five data sets (B-mode US, SWE, SE and two combined sets). Qualitative and quantitative elastographic data and Breast Imaging Reporting and Data System (BI-RADS) categories were recorded. The area under the receiver operating characteristic curve (AUC) was evaluated. No significant difference in the AUC between the two elastography methods was noted. After subjective assessment by reviewers, the AUC for the combined sets was improved (SWE, 0.987; SE, 0.982; B-mode US, 0.970; p < 0.05). When SE and SWE were added, 38% and 56% of benign BI-RADS category 4a lesions with a low suspicion of cancer were downgraded without false-negative results, respectively. SE and SWE performed similarly. Therefore, addition of SE or SWE improved the diagnostic performance of B-mode US, potentially reducing unnecessary biopsies.

BI-RADS update

The updated American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) has been newly released. This article summarizes the changes and updates that have been made to BI-RADS. The goal of the revised edition continues to be the same: to improve clarification in image interpretation, maintain reporting standardization, and simplify the monitoring of outcomes. The new BI-RADS also introduces new terminology to provide a more universal lexicon across all 3 imaging modalities.

Underestimation of invasive lesions in patients with ductal carcinoma in situ of the breast diagnosed by ultrasound-guided biopsy: a comparison between patients with and without HER2/neu overexpression

PURPOSE

To determine the rate of underestimation of ductal carcinoma in situ (DCIS) diagnosed at imaging-guided biopsy and to analyze its association with HER2/neu oncogene, an important biomarker in assessing the tumour aggressiveness and guiding hormone therapy for breast cancer.

METHODS

We retrospectively reviewed 162 patients with DCIS diagnosed by imaging-guided core needle biopsy between January 2008 and March 2013. All of these patients received surgical excision, and in 25, the diagnosis was upgraded to invasive breast cancer. In this study, we examined the ultrasound, mammographic features and histopathological results for each patient, and compared these parameters between those with and without HER2/neu overexpression.

RESULTS

Of the 162 DCIS lesions, 110 (67.9%) overexpressed HER2/neu. Nineteen patients with HER2/neu overexpressing DCIS (n=19/110, 17.3%) were upgraded after surgery to a diagnosis of invasive breast cancer. In this group, the upgrade rate was highest in patients with a dilated mammary duct pattern (42.1%, n=8/19, p=0.02) and the presence of abnormal axillary nodes (40.0%, n=12/30, p<0.01) at ultrasound and was significantly associated with comedo tumour type on pathology.

CONCLUSIONS

Biopsy may underestimate the invasive component in DCIS patients. Sonographic findings of dilated mammary ducts and presence of abnormal axillary lymph nodes may help predicting the invasive components and possibly driving more targeted biopsy procedures.

Breast Diagnosis: Concordance Analysis Between the BI-RADS Classification and Tsukuba Sonoelastography Score

AIMS

To establish the correlations between the ultrasound (US) BI-RADS classification and Tsukuba elastography score when assessing breast lesions. To determine which type of breast lesion (BI-RADS category) would benefit most from an elastographic assessment.

PATIENTS AND METHODS

The investigated sample of imaging comprised a number of 129 images belonging to 92 subjects examined with a Hitachi 8500 US device. Each lesion was assessed according to the BI-RADS and Tsukuba elastography score. Histopathology was obtained by means of percutaneous biopsy or post-surgery. Fibroadenoma-like lesions unchanged over a period of 3 years were considered benign.

RESULTS

The 1, 2 and BGR Tsukuba scores mostly correlated with BI-RADS II and III lesions such as cysts, hamartomas, lipomas, hematomas, non-palpable fibroadenomas. Palpable fibroadenomas initially included in BI-RADS IVa/b category, usually received benign elasticity scores (1 or 2), the exception being represented by a minority of cases of old, fibrotic or calcified lesions (elastic score 3 or 4). Non-specific BI-RADS IVa/b lesions, such as mastopathic nodules demonstrated rather soft, elastic properties on elastogram (score 1 or 2). The 4 and 5 Ueno-Itoh scores were predominantly correlated with BI-RADS IVc and V categories represented by high risk lesions (radial scar, papillomas, atypical epithelial ductal hyperplasia) and in situ or invasive carcinomas.

CONCLUSIONS

Generally the BI-RADS classification correlates well with the Tsukuba elasticity score, the main exception being represented by fibrotic, calcified lesions which falsely appear more suspicious post-elastography. BI-RADS III and IV lesions would benefit most from an elastographic assessment, a low Tsukuba score allowing a less invasive approach, while a high score imposes histopathological evaluation.

[Performance of users in tropical areas with the BI-RADS classification of breast lesions for predicting malignancy]

OBJECTIVES

To evaluate the diagnostic performance of radiologists in Cameroon using the BI-RADS classification to interpret mammograms and ultrasound scans together for the prediction of malignant breast lesions.

METHODS

This cross-sectional study took place at the Women's and Children's Hospital in Yaounde from July 2009 to April 2010 and included 211 women with breast lesions identified on mammograms during a breast screening campaign and subsequently assessed with ultrasonography and histology. The BI-RADS classifications of these lesions were compared to the corresponding histology results to evaluate the accuracy of predictions of malignancy from the mammograms and ultrasound scans interpreted with the BI-RADS system. The rate of malignancy in each ACR-classified category was also compared to the standard ACR categories as stipulated in the ACR classification.

RESULTS

In all, 339 women aged from 16 to 78 years were screened, and lesions requiring biopsies were identified for 211. The age group included most often was the 41-50 year-old group (n = 98, 46.4%). Overall, 135 (64%) women had benign lesions and 76 (36%) malignant. Invasive carcinoma was found in 49 (65%) of the malignant lesions, in situ intraductal carcinoma in 23 (30%), and sarcoma in 4 (5%). Based on the BI-RADS classification, 124 (58.7%) breast lesions were classified as ACR2, 15 (7.1%) as ACR3, 44 (20.8%) as ACR4, and 28 (13.3%) as ACR5. Comparison of the BI-RADS classification and the histological findings showed that 19% of ACR2-classified lesions were malignant, 13% of those classified ACR3, 66% ACR4, and 75% ACR5. The global accuracy in the prediction of malignancy the BI-RADS classification was 77.3%.

CONCLUSION

The accuracy of the radiologists using the BI-RADS classification in our hospital was good at 77.3%, although shortcomings in the evaluation and interpretation of some lesions resulted in a relatively high prevalence of malignant lesions in categories ACR2 and ACR3.

The MPATH-Dx reporting schema for melanocytic proliferations and melanoma

BACKGROUND

The histologic diagnosis of melanoma and nevi can be subject to discordance and errors, potentially leading to inappropriate treatment and harm. Diagnostic terminology is not standardized, creating confusion for providers and patients and challenges for investigators.

OBJECTIVE

We sought to describe the development of a pathology reporting form for more precise research on melanoma and a diagnostic-treatment mapping tool for improved patient care and consistency in treatment.

METHODS

Three dermatopathologists independently reviewed melanocytic lesions randomly selected from a dermatopathology database. Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) reporting schema evolved from iterative case review and form revision.

RESULTS

Differences in diagnostic thresholds, interpretation, and nomenclature contributed to development of the MPATH-Dx histology reporting form, which groups lesions by similarities in histogenesis and degrees of atypia. Because preliminary results indicate greater agreement regarding suggested treatments than for specific diagnoses, the diverse terminologies of the MPATH-Dx histology reporting form were stratified by commonalities of treatments in the MPATH-Dx diagnostic-treatment mapping scheme.

LIMITATIONS

Without transformative advances in diagnostic paradigms, the interpretation of melanocytic lesions frequently remains subjective.

CONCLUSIONS

The MPATH-Dx diagnostic-treatment mapping scheme could diminish confusion for those receiving reports by categorizing diverse nomenclature into a hierarchy stratified by suggested management interventions.

Clinical application of qualitative assessment for breast masses in shear-wave elastography

PURPOSE

To evaluate the interobserver agreement and the diagnostic performance of various qualitative features in shear-wave elastography (SWE) for breast masses.

MATERIALS AND METHODS

A total of 153 breast lesions in 152 women who underwent B-mode ultrasound and SWE before biopsy were included. Qualitative analysis in SWE was performed using two different classifications: E values (Ecol; 6-point color score, Ehomo; homogeneity score and Esha; shape score) and a four-color pattern classification. Two radiologists reviewed five data sets: B-mode ultrasound, SWE, and combination of both for E values and four-color pattern. The BI-RADS categories were assessed B-mode and combined sets. Interobserver agreement was assessed using weighted κ statistics. Areas under the receiver operating characteristic curve (AUC), sensitivity, and specificity were analyzed.

RESULTS

Interobserver agreement was substantial for Ecol (κ=0.79), Ehomo (κ=0.77) and four-color pattern (κ=0.64), and moderate for Esha (κ=0.56). Better-performing qualitative features were Ecol and four-color pattern (AUCs, 0.932 and 0.925) compared with Ehomo and Esha (AUCs, 0.857 and 0.864; P<0.05). The diagnostic performance of B-mode ultrasound (AUC, 0.950) was not significantly different from combined sets with E value and with four color pattern (AUCs, 0.962 and 0.954). When all qualitative values were negative, leading to downgrade the BI-RADS category, the specificity increased significantly from 16.5% to 56.1% (E value) and 57.0% (four-color pattern) (P<0.001) without improvement in sensitivity.

CONCLUSION

The qualitative SWE features were highly reproducible and showed good diagnostic performance in suspicious breast masses. Adding qualitative SWE to B-mode ultrasound increased specificity in decision making for biopsy recommendation.

Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer

OBJECTIVE

To predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) using features derived from dynamic contrast-enhanced (DCE) MRI.

MATERIALS AND METHODS

60 patients with triple-negative early-stage breast cancer receiving NAC were evaluated. Features assessed included clinical data, patterns of tumor response to treatment determined by DCE-MRI, MRI breast imaging-reporting and data system descriptors, and quantitative lesion kinetic texture derived from the gray-level co-occurrence matrix (GLCM). All features except for patterns of response were derived before chemotherapy; GLCM features were determined before and after chemotherapy. Treatment response was defined by the presence of residual invasive tumor and/or positive lymph nodes after chemotherapy. Statistical modeling was performed using Lasso logistic regression.

RESULTS

Pre-chemotherapy imaging features predicted all measures of response except for residual tumor. Feature sets varied in effectiveness at predicting different definitions of treatment response, but in general, pre-chemotherapy imaging features were able to predict pathological complete response with area under the curve (AUC)=0.68, residual lymph node metastases with AUC=0.84 and residual tumor with lymph node metastases with AUC=0.83. Imaging features assessed after chemotherapy yielded significantly improved model performance over those assessed before chemotherapy for predicting residual tumor, but no other outcomes.

CONCLUSIONS

DCE-MRI features can be used to predict whether triple-negative breast cancer patients will respond to NAC. Models such as the ones presented could help to identify patients not likely to respond to treatment and to direct them towards alternative therapies.

Updates and revisions to the BI-RADS magnetic resonance imaging lexicon

This article summarizes the updates and revisions to the second edition of the BI-RADS MRI lexicon. A new feature in the lexicon is background parenchymal enhancement and its descriptors. Another major focus is on revised terminology for masses and non-mass enhancement. A section on breast implants and associated lexicon terms has also been added. Because diagnostic breast imaging increasingly includes multimodality evaluation, the new edition of the lexicon also contains revised recommendations for combined reporting with mammography and ultrasound if these modalities are included as comparison, and clarification on the use of final assessment categories in MR imaging.