ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database

A Nomogram Based on Conventional Ultrasound Radiomics for Differentiating Between Radial Scar and Invasive Ductal Carcinoma of the Breast

ABSTRACT

We aimed to develop and validate a nomogram based on conventional ultrasound (CUS) radiomics model to differentiate radial scar (RS) from invasive ductal carcinoma (IDC) of the breast. In total, 208 patients with histopathologically diagnosed RS or IDC of the breast were enrolled. They were randomly divided in a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 63). Overall, 1316 radiomics features were extracted from CUS images. Then a radiomics score was constructed by filtering unstable features and using the maximum relevance minimum redundancy algorithm and the least absolute shrinkage and selection operator logistic regression algorithm. Two models were developed using data from the training cohort: one using clinical and CUS characteristics (Clin + CUS model) and one using clinical information, CUS characteristics, and the radiomics score (radiomics model). The usefulness of nomogram was assessed based on their differentiating ability and clinical utility. Nine features from CUS images were used to build the radiomics score. The radiomics nomogram showed a favorable predictive value for differentiating RS from IDC, with areas under the curve of 0.953 and 0.922 for the training and validation cohorts, respectively. Decision curve analysis indicated that this model outperformed the Clin + CUS model and the radiomics score in terms of clinical usefulness. The results of this study may provide a novel method for noninvasively distinguish RS from IDC.

The features associated with mammography-occult MRI-detected newly diagnosed breast cancer analysed by comparing machine learning models with a logistic regression model

PURPOSE

To compare machine learning (ML) models with logistic regression model in order to identify the optimal factors associated with mammography-occult (i.e. false-negative mammographic findings) magnetic resonance imaging (MRI)-detected newly diagnosed breast cancer (BC).

MATERIAL AND METHODS

The present single-centre retrospective study included consecutive women with BC who underwent mammography and MRI (no more than 45 days apart) for breast cancer between January 2018 and May 2023. Various ML algorithms and binary logistic regression analysis were utilized to extract features linked to mammography-occult BC. These features were subsequently employed to create different models. The predictive value of these models was assessed using receiver operating characteristic curve analysis.

RESULTS

This study included 1957 malignant lesions from 1914 patients, with an average age of 51.64 ± 9.92 years and a range of 20-86 years. Among these lesions, there were 485 mammography-occult BCs. The optimal features of mammography-occult BC included calcification status, tumour size, mammographic density, age, lesion enhancement type on MRI, and histological type. Among the different ML models (ANN, L1-LR, RF, and SVM) and the LR-based combined model, the ANN model with RF features was found to be the optimal model. It demonstrated the best discriminative performance in predicting mammography false- negative findings, with an AUC of 0.912, an accuracy of 86.90%, a sensitivity of 85.85%, and a specificity of 84.18%.

CONCLUSION

Mammography-occult MRI-detected breast cancers have features that should be considered when performing breast MRI to improve the detection rate for breast cancer and aid in clinician management.

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study

OBJECTIVES

The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts.

METHODS

A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only.

RESULTS

The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874-1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817-0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone ( P <0.05).

CONCLUSIONS

The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.

Mucinous Carcinoma of the Breast: A Case Report

This case report presents the diagnostic journey of a 65-year-old female presenting with symptoms suggestive of breast pathology, ultimately diagnosed with mucinous carcinoma, following comprehensive clinical evaluation and histopathological confirmation. Initial assessments indicated a fibroadenoma; however, subsequent histopathological examination revealed mucinous carcinoma, highlighting the importance of histopathological confirmation in establishing definitive diagnoses. The case underscores the challenges in distinguishing between benign and malignant breast lesions based on clinical presentation and imaging findings alone. The multidisciplinary approach facilitated discussions regarding treatment options tailored to the patient's clinical and pathological characteristics. This case emphasizes the significance of a comprehensive diagnostic approach, integrating clinical evaluation, imaging studies, and histopathological analysis, in ensuring accurate diagnosis and guiding optimal management strategies for patients with breast cancer.

A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms

Purpose

To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset.

Materials and Methods

iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed.

Results

The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45-65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the "low" POM group had malignant lesions, while in the "high" POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million.

Conclusion

iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs.Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by McDonald and Conant in this issue.

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

Maximum intensity projection based on high frame rate contrast-enhanced ultrasound for the differentiation of breast tumors

Objective

We explored the role of maximum intensity projection (MIP) based on high frame rate contrast-enhanced ultrasound (H-CEUS) for the differentiation of breast tumors.

Methods

MIP imaging was performed in patients with breast tumors who underwent H-CEUS examinations. The microvasculature morphology of breast tumors was assessed. The receiver operating characteristic curve was plotted to evaluate the diagnostic performance of MIP.

Results

Forty-three breast tumors were finally analyzed, consisting of 19 benign and 24 malignant tumors. For the ≤30-s and >30-s phases, dot-, line-, or branch-like patterns were significantly more common in benign tumors. A tree-like pattern was only present in the benign tumors. A crab claw-like pattern was significantly more common in the malignant tumors. Among the tumors with crab claw-like patterns, three cases of malignant tumors had multiple parallel small spiculated vessels. There were significant differences in the microvasculature morphology for the ≤30-s and >30-s phases between the benign and malignant tumors (all p < 0.001). The area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the ≤30-s phase were all higher than those of the >30-s phase for the classification of breast tumors.

Conclusion

MIP based on H-CEUS can be used for the differentiation of breast tumors, and the ≤30-s phase had a better diagnostic value. Multiple parallel small spiculated vessels were a new finding, which could provide new insight for the subsequent study of breast tumors.

BIRADS 4 - Is it possible to downgrade lesions that do not enhance on recombinant contrast-enhanced mammography images?

PURPOSE

Analysis of the morphology of lesions classified into the BI-RADS 4 category and assessment of the possibility of downgrade the BI-RADS category in those that did not show enhancement on recombinant contrast-enhanced mammography (CEM) images.

METHOD

The retrospective, single-center study included 528 patients who underwent a core needle biopsy performed from January 2017 to November 2022 due to a breast lesion classified as BI-RADS 4 on CEM. Patients' electronic records and imaging examinations were reviewed. Individual lesions were classified into the morphological categories of mass, non-mass, and microcalcifications. Sensitivity, specificity, positive as well as negative predictive values were calculated for the whole group and individual morphological categories. The influence of the lesions' diameter on the results was analyzed.

RESULTS

CEM NPV for the whole group was 93.9% (±95% CI: 90.0-96.4), for mass lesions 100% (±95% CI: 94.5-100), for non-mass lesions 97.8% (±95% CI: 87.0-99.9) and 87.9% (±95% CI: 80.3-93.0) for microcalcifications. Given that 230 out of 383 benign lesions were not contrast-enhancing, 60.1% of unnecessary CNBs would have been correctly avoided. CEM sensitivity for lesions < 20 mm was lower than for lesions ≥ 20 mm and was respectively 86.6% (±95% CI: 76.8-92.8) vs 94.6% (±95% CI: 86.0-98.2), respectively.

CONCLUSION

CEM is characterized by high sensitivity in the detection of malignant lesions in the case of lesions with mass and non-mass morphology. The high NPV for recombinant images suggests that in the case of these lesions, the lack of enhancement supports the benign nature of the lesion and may lead to a downgrade of the BI-RADS category.

Value of high-resolution full-field optical coherence tomography and dynamic cell imaging for one-stop rapid diagnosis breast clinic

BACKGROUND

Full-field optical coherence tomography combined with dynamic cell imaging (D-FFOCT) is a new, simple-to-use, nondestructive, quick technique that can provide sufficient spatial resolution to mimic histopathological analysis. The objective of this study was to evaluate diagnostic performance of D-FFOCT for one-stop rapid diagnosis breast clinic.

METHODS

Dynamic full-field optical coherence tomography was applied to fresh, untreated breast and nodes biopsies. Four different readers (senior and junior radiologist, surgeon, and pathologist) analyzed the samples without knowing final histological diagnosis or American College of Radiology classification. The results were compared to conventional processing and staining (hematoxylin-eosin).

RESULTS

A total of 217 biopsies were performed on 152 patients. There were 144 breast biopsies and 61 lymph nodes with 101 infiltrative cancers (49.27%), 99 benign lesions (48.29%), 3 ductal in situ carcinoma (1.46%), and 2 atypias (0.98%). The diagnostic performance results were as follow: sensitivity: 77% [0.7;0.82], specificity: 64% [0.58;0.71], PPV: 74% [0.68;0.78], and NPV: 75% [0.72;0.78]. A large image atlas was created as well as a diagnosis algorithm from the readers' experience.

CONCLUSION

With 74% PPV and 75% NPV, D-FFOCT is not yet ready to be used in clinical practice to identify breast cancer. This is mainly explained by the lack of experience and knowledge of this new technic by the four lectors. By training with the diagnosis algorithm and the image atlas, radiologists could have better outcomes allowing quick detection of breast cancer and lymph node involvement. Deep learning could also be used, and further investigation will follow.

Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics

The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature-based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN-based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN-based AI methods, while radiomic feature-based machine learning methods achieved inferior classification results.

Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method

BACKGROUND

In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions.

METHODS

A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2.

RESULTS

The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110).

CONCLUSION

The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses.

A YOLO-based AI system for classifying calcifications on spot magnification mammograms

OBJECTIVES

Use of an AI system based on deep learning to investigate whether the system can aid in distinguishing malignant from benign calcifications on spot magnification mammograms, thus potentially reducing unnecessary biopsies.

METHODS

In this retrospective study, we included public and in-house datasets with annotations for the calcifications on both craniocaudal and mediolateral oblique vies, or both craniocaudal and mediolateral views of each case of mammograms. All the lesions had pathological results for correlation. Our system comprised an algorithm based on You Only Look Once (YOLO) named adaptive multiscale decision fusion module. The algorithm was pre-trained on a public dataset, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), then re-trained and tested on the in-house dataset of spot magnification mammograms. The performance of the system was investigated by receiver operating characteristic (ROC) analysis.

RESULTS

We included 1872 images from 753 calcification cases (414 benign and 339 malignant) from CBIS-DDSM. From the in-house dataset, 636 cases (432 benign and 204 malignant) with 1269 spot magnification mammograms were included, with all lesions being recommended for biopsy by radiologists. The area under the ROC curve for our system on the in-house testing dataset was 0.888 (95% CI 0.868-0.908), with a sensitivity of 88.4% (95% CI 86.9-8.99%), specificity of 80.8% (95% CI 77.6-84%), and an accuracy of 84.6% (95% CI 81.8-87.4%) at the optimal cutoff value. Using the system with two views of spot magnification mammograms, 80.8% benign biopsies could be avoided.

CONCLUSION

The AI system showed good accuracy for classification of calcifications on spot magnification mammograms which were all categorized as suspicious by radiologists, thereby potentially reducing unnecessary biopsies.

A Novel Nomogram Based on Imaging Biomarkers of Shear Wave Elastography, Angio Planewave Ultrasensitive Imaging, and Conventional Ultrasound for Preoperative Prediction of Malignancy in Patients with Breast Lesions

Several studies have demonstrated the difficulties in distinguishing malignant lesions of the breast from benign lesions owing to overlapping morphological features on ultrasound. Consequently, we aimed to develop a nomogram based on shear wave elastography (SWE), Angio Planewave Ultrasensitive imaging (Angio PLUS (AP)), and conventional ultrasound imaging biomarkers to predict malignancy in patients with breast lesions. This prospective study included 117 female patients with suspicious lesions of the breast. Features of lesions were extracted from SWE, AP, and conventional ultrasound images. The least absolute shrinkage and selection operator (Lasso) algorithms were used to select breast cancer-related imaging biomarkers, and a nomogram was developed based on six of the 16 imaging biomarkers. This model exhibited good discrimination (area under the receiver operating characteristic curve (AUC): 0.969; 95% confidence interval (CI): 0.928, 0.989) between malignant and benign breast lesions. Moreover, the nomogram also showed demonstrated good calibration and clinical usefulness. In conclusion, our nomogram can be a potentially useful tool for individually-tailored diagnosis of breast tumors in clinical practice.

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies

Objectives

To investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast lesions and thereby avoids false-positive biopsies.

Methods

This retrospective study consecutively included patients with MR BI-RADS 4 lesions. The ultrafast imaging was performed using Differential sub-sampling with cartesian ordering (DISCO) technique and the tenth and fifteenth postcontrast DISCO images (DISCO-10 and DISCO-15) were selected for further analysis. An experienced radiologist used freely available software (FAE) to perform radiomics extraction. After principal component analysis (PCA), a multilayer perceptron artificial neural network (ANN) to distinguish between malignant and benign lesions was developed and tested using a random allocation approach. ROC analysis was performed to evaluate the diagnostic performance.

Results

173 patients (mean age 43.1 years, range 18-69 years) with 182 lesions (95 benign, 87 malignant) were included. Three types of independent principal components were obtained from the radiomics based on DISCO-10, DISCO-15, and their combination, respectively. In the testing dataset, ANN models showed excellent diagnostic performance with AUC values of 0.915-0.956. Applying the high-sensitivity cutoffs identified in the training dataset demonstrated the potential to reduce the number of unnecessary biopsies by 63.33%-83.33% at the price of one false-negative diagnosis within the testing dataset.

Conclusions

The ultrafast DCE-MRI radiomics-based machine learning model could classify MR BI-RADS category 4 lesions into benign or malignant, highlighting its potential for future application as a new tool for clinical diagnosis.

Mammographic calcifications undergoing percutaneous biopsy: outcome in women with and without a personal history of breast cancer

PURPOSE

To compare the positive predictive values (PPVs) of BI-RADS categories used to assess pure mammographic calcifications in women with and without a previous history of breast cancer (PHBC).

MATERIALS AND METHODS

In this retrospective study, all consecutive pure mammographic calcifications (n = 320) undergoing a stereotactic biopsy between 2016 and 2018 were identified. Mammograms were evaluated in consensus by two radiologists according to BI-RADS and blinded to patient history and pathology results. Final pathologic results were used as the standard of reference. PPV of BI-RADS categories were compared between the two groups. Data were evaluated using standard statistics, Mann-Whitney U tests and Chi-square tests.

RESULTS

Two hundred sixty-eight patients (274 lesions, median age 54 years, inter-quartile range, 50-65 years) with a PHBC (n = 46) and without a PHBC (n = 222) were included. Overall PPVs were the following: BI-RADS 2, 0% (0 of 56); BI-RADS 3, 9.1% (1 of 11); BI-RADS 4a, 16.2% (6 of 37); BI-RADS 4b, 37.5% (48 of 128); BI-RADS 4c, 47.3% (18 of 38) and BI-RADS 5, 100% (4 of 4). The PPV of BI-RADS categories was similar in patients with and without a PHBC (P = .715). Calcifications were more often malignant in patients with a PHBC older than 10 years (47.3%, 9 of 19) compared to 1-2 years (25%, 1 of 4), 2-5 years (20%, 2 of 10) and 5-10 years (0%, of 13) from the first breast cancer (P = .005).

CONCLUSION

PPV of mammographic calcifications is similar in women with or without PHBC when BI-RADS classification is strictly applied. A higher risk of malignancy was observed in patients with a PHBC longer than 10 years.

Mammographic breast density status in women aged more than 40 years in Sulaimaniyah, Iraq: a cross-sectional study

Objective

Mammography is the gold standard screening procedure for the early diagnosis of breast cancer. This study aimed to determine the distribution of breast density among women older than 40 years in Sulaimaniyah, Iraq, and to examine the correlations between breast density and various risk factors.

Methods

This cross-sectional study included 750 women who received routine mammographic breast screening at Sulaimaniyah Breast Center. Bilateral standard two-view mammographic images (craniocaudal and mediolateral oblique projections) were acquired and reported using a picture archiving and communication system. American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) assessment categories C and D were considered as dense.

Results

A total of 54.3% of breasts were classified as dense, with ACR-BI-RADS categories C or D. Breast density was significantly associated with age, body mass index, a family history of breast cancer, and pre-menopause, and women with no history of breastfeeding were more likely to have dense breasts than those with partial or complete breastfeeding.

Conclusions

This study revealed that women from Sulaimaniyah with a distinct breast-density profile at mammographic screening may have a significantly increased risk of breast cancer.

Diagnostic Performance of Digital Breast Tomosynthesis with the Two-Dimensional Synthesized Mammogram for Suspicious Breast Microcalcifications Compared to Full-Field Digital Mammography in Stereotactic Breast Biopsy

Purpose

To evaluate the diagnostic performance of digital breast tomosynthesis (DBT) with the two-dimensional synthesized mammogram (2DSM), compared to full-field digital mammography (FFDM), for suspicious microcalcifications in the breast ahead of stereotactic biopsy and to assess the diagnostic image visibility of the images.

Materials and Methods

This retrospective study involved 189 patients with microcalcifications, which were histopathologically verified by stereotactic breast biopsy, who underwent DBT with 2DSM and FFDM between January 8, 2015, and January 20, 2020. Two radiologists assessed all cases of microcalcifications based on Breast Imaging Reporting and Data System (BI-RADS) independently. They were blinded to the histopathologic outcome and additionally evaluated lesion visibility using a five-point scoring scale.

Results

Overall, the inter-observer agreement was excellent (0.9559). Under the setting of category 4A as negative due to the low possibility of malignancy and to avoid the dilution of malignancy criteria in our study, McNemar tests confirmed no significant difference between the performances of the two modalities in detecting microcalcifications with a high potential for malignancy (4B, 4C, or 5; p = 0.1573); however, the tests showed a significant difference between their performances in detecting microcalcifications with a high potential for benignancy (4A; p = 0.0009). DBT with 2DSM demonstrated superior visibility and diagnostic performance than FFDM in dense breasts.

Conclusion

DBT with 2DSM is superior to FFDM in terms of total diagnostic accuracy and lesion visibility for benign microcalcifications in dense breasts. This study suggests a promising role for DBT with 2DSM as an accommodating tool for stereotactic biopsy in female with dense breasts and suspicious breast microcalcifications.

Machine learning based on automated breast volume scanner (ABVS) radiomics for differential diagnosis of benign and malignant BI‐RADS 4 lesions

BI‐RADS category 4 represents possibly malignant lesions and biopsy is recommended to distinguish benign and malignant. However, studies revealed that up to 67%–78% of BI‐RADS 4 lesions proved to be benign, but received unnecessary biopsies, which may cause unnecessary anxiety and discomfort to patients and increase the burden on the healthcare system. In this prospective study, machine learning (ML) based on the emerging breast ultrasound technology‐automated breast volume scanner (ABVS) was constructed to distinguish benign and malignant BI‐RADS 4 lesions and compared with different experienced radiologists. A total of 223 pathologically confirmed BI‐RADS 4 lesions were recruited and divided into training and testing cohorts. Radiomics features were extracted from axial, sagittal, and coronal ABVS images for each lesion. Seven feature selection methods and 13 ML algorithms were used to construct different ML pipelines, of which the DNN‐RFE (combination of recursive feature elimination and deep neural networks) had the best performance in both training and testing cohorts. The AUC value of the DNN‐RFE was significantly higher than less experienced radiologist at Delong's test (0.954 vs. 0.776, p = 0.004). Additionally, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the DNN‐RFE were 88.9%, 83.3%, 95.2%, 83.3%, and 95.2%, which also significantly better than less experienced radiologist at McNemar's test (p = 0.043). Therefore, ML based on ABVS radiomics may be a potential method to non‐invasively distinguish benign and malignant BI‐RADS 4 lesions.

Understanding linkage to biopsy and treatment for breast cancer after a high-risk telemammography result in Peru: a mixed-methods study

OBJECTIVES

This mixed-method study aimed to understand the effectiveness of linkage to biopsy and treatment in women with a high-risk mammography result (Breast Imaging Reporting and Data System, BI-RADS 4 and 5) in the national telemammography programme and to explore women's experiences during this process.

SETTING

Quantitative component: we collected and linked health data from the telemammography reading centre, the national public health insurance, the national centre for disease control and the national referral cancer centre. Qualitative component: we interviewed participants from different regions of the country representing diverse social and geographical backgrounds.

PARTICIPANTS

Quantitative: women who underwent telemammography between July 2017 and September 2018 and had high-risk results (BI-RADS 4-5) were collected. Qualitative: women with a high-risk telemammography result, healthcare providers and administrators.

OUTCOMES MEASURES

Quantitative: we determined biopsy and treatment linkage rates and delays. Qualitative: we explored barriers and facilitators for obtaining a biopsy and initiating treatment.

RESULTS

Of 126 women with high-risk results, 48.4% had documentation of biopsy and 37.5% experienced a delay of >45 days to biopsy. Of 51 women diagnosed with breast cancer, 86.4% had evidence of treatment initiation, but 69.2% initiated treatment >45 days after biopsy. Travelling to major cities for care, administrative factors and breast cancer misconceptions, among other factors, impeded timely, continuous care for breast cancer. A multidisciplinary and culturally tailored patient education facilitated understanding of the disease and prompt decision making about subsequent medical care.

CONCLUSIONS

Strengthened breast cancer care capacity outside the capital city, standardised referral pathways, ensured financial support for travel expenses, and enhanced patient education are required to secure linkage to the breast cancer care continuum. Robust information systems are needed to track patients and to evaluate the programme's performance.

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

Improving the assessment of breast imaging reporting and data system (BI-RADS) 4 lesions and reducing unnecessary biopsies are urgent clinical issues. In this prospective study, a radiomic nomogram based on the automated breast volume scanner (ABVS) was constructed to identify benign and malignant BI-RADS 4 lesions and evaluate its value in reducing unnecessary biopsies. A total of 223 histologically confirmed BI-RADS 4 lesions were enrolled and assigned to the training and validation cohorts. A radiomic score was generated from the axial, sagittal, and coronal ABVS images. Combining the radiomic score and clinical-ultrasound factors, a radiomic nomogram was developed by multivariate logistic regression analysis. The nomogram integrating the radiomic score, lesion size, and BI-RADS 4 subcategories showed good discrimination between malignant and benign BI-RADS 4 lesions in the training (AUC, 0.959) and validation (AUC, 0.925) cohorts. Moreover, 42.5% of unnecessary biopsies would be reduced by using the nomogram, but nine (4%) malignant BI-RADS 4 lesions were unfortunately missed, of which 4A (77.8%) and small-sized (<10 mm) lesions (66.7%) accounted for the majority. The ABVS radiomics nomogram may be a potential tool to reduce unnecessary biopsies of BI-RADS 4 lesions, but its ability to detect small BI-RADS 4A lesions needs to be improved.

An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions

BACKGROUND

The NCCN clinical guidelines recommended core needle biopsy for breast lesions classified as Breast Imaging Reporting and Data System (BI-RADS) 4, while category 4A lesions are only 2-10% likely to be malignant. Thus, a large number of biopsies of BI-RADS 4A lesions were ultimately determined to be benign, and those unnecessary biopsies may incur additional costs and pains. However, it is important to emphasize that the current risk prediction model focuses primarily on the details and complex risk features of US or MG findings, which may be difficult to apply in order to benefit from the model. To stratify and manage BI-RADS 4A lesions effectively and efficiently, a more effective and practical predictive model must be developed.

METHODS

We retrospectively analyzed 465 patients with BI-RADS ultrasonography (US) category 4A lesions, diagnosed between January 2019 and July 2019 in Tianjin Medical University Cancer Institute and Hospital and National Clinical Research Center for Cancer. Univariate and multivariate logistic regression analyses were conducted to identify risk factors. To stratify and predict the malignancy of BI-RADS 4A lesions, a nomogram combining the risk factors was constructed based on the multivariate logistic regression results. In order to determine the predictive performance of our predictive model, we used the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC), and the decision curve analysis (DCA) to assess the clinical benefits.

RESULTS

Based on our analysis, 16.3% (76 out of 465) of patients were pathologically diagnosed with malignant lesions, while 83.6% (389 out of 465) were diagnosed with benign lesions. According to univariate and multivariate logistic regression analysis, age (OR = 3.414, 95%CI:1.849-6.303), nipple discharge (OR = .326, 95%CI:0.157-.835), palpable lesions (OR = 1.907, 95%CI:1.004-3.621), uncircumscribed margin (US) (OR = 1.732, 95%CI:1.033-2.905), calcification (mammography, MG) (OR = 2.384, 95%CI:1.366-4.161), BI-RADS(MG) (OR = 5.345, 95%CI:2.934-9.736) were incorporated into the predictive nomogram (C-index = .773). There was good agreement between the predicted risk and the observed probability of recurrence. Furthermore, we determined that 153 was the best cutoff score for distinguishing between patients in the low- and high-risk groups. Malignant lesions were significantly more prevalent in high-risk patients than in low-risk patients.

CONCLUSION

Based on clinical, US, and MG features, we present a predictive nomogram to reliably predict the malignancy risk of BI-RADS(US) 4A lesions, which may assist clinicians in the selection of patients at low risk of malignancy and reduce the number of false-positive biopsies.

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer's high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model's diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

Prospective assessment of adjunctive ultrasound-guided diffuse optical tomography in women undergoing breast biopsy: Impact on BI-RADS assessments

PURPOSE

To assess the impact of adjunctive ultrasound guided diffuse optical tomography (US-guided DOT) on BI-RADS assessment in women undergoing US-guided breast biopsy.

METHOD

This prospective study enrolled women referred for US-guided breast biopsy between 3/5/2019 and 3/19/2020. Participants underwent US-guided DOT immediately before biopsy. The US-guided DOT acquisition generated average maximum total hemoglobin (HbT) spatial maps and quantitative HbT values. Four radiologists blinded to histopathology assessed conventional imaging (CI) to assign a CI BI-RADS assessment and then integrated DOT information in assigning a CI&DOT BI-RADS assessment. HbT was compared between benign and malignant lesions using an ANOVA test and Tukey's test. Benign biopsies were tabulated, deeming BI-RADS ≥ 4A as positive. Reader agreement was assessed.

RESULTS

Among 61 included women (mean age 48 years), biopsy demonstrated 15 (24.6%) malignant and 46 (75.4%) benign lesions. Mean HbT was 55.3 ± 22.6 µM in benign lesions versus 85.4 ± 15.6 µM in cancers (p < .001). HbT threshold of 78.5 µM achieved sensitivity 80% (12/15) and specificity 89% (41/46) for malignancy. Across readers and patients, 197 pairs of CI BI-RADS and CI&DOT BI-RADS assessments were assigned. Adjunctive US-guided DOT achieved a net decrease in 23.5% (31/132) of suspicious (CI BI-RADS ≥ 4A) assessments of benign lesions (34 correct downgrades and 3 incorrect upgrades). 38.3% (31/81) of 4A assessments were appropriately downgraded. No cancer was downgraded to a non-actionable assessment. Interreader agreement analysis demonstrated kappa = 0.48-0.53 for CI BI-RADS and kappa = 0.28-0.44 for CI&DOT BI-RADS.

CONCLUSIONS

Integration of US-guided DOT information achieved a 23.5% reduction in suspicious BI-RADS assessments for benign lesions. Larger studies are warranted, with attention to improved reader agreement.

Superb Microvascular Imaging Technology Can Improve the Diagnostic Efficiency of the BI-RADS System

BACKGROUND

To explore whether superb microvascular imaging (SMI)SMI can improve the diagnostic efficiency by evaluating the vascular index (VI) and vascular architecture (VA) in breast lesions.

METHODS

This is a retrospective study of data collected prospectively for research use. Taking 225 consecutive cases of breast lesions from November 2016 to December 2017 as a training set, the VI values and VA types of benign and malignant lesions were calculated based on the pathological results. Taking 238 consecutive cases of breast lesions from January 2018 to October 2018 as the verification set, the diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were calculated to compare the diagnostic efficacy.

RESULTS

The training set included 225 breast lesions and the validation set 238 breast lesions. The VI value in the malignant group (10.3 ± 8.0) was significantly higher than that in the benign group (4.3 ± 5.0)(P<0.001). A VI value of 4.05 was used as the diagnostic threshold for differentiating benign from malignant lesions, with a sensitivity of 80.5%, a specificity of 61.9%, an accuracy of 71.1%, a PPV of 62.9%, a NPV of 76.9%, and an area under the curve of 0.758 (0.696-0.819). There was a significant difference in the types of benign and malignant VA (P < 0.001), and the PPV of the root hair-like and crab claw-like VAs were 93.9% and 100.0%, respectively. The diagnostic sensitivity, specificity, accuracy, PPV, NPV and area under the AUC curve were 58.0%, 98.2%, 97.0%, 70.3% and 0.781, respectively (95%CI: 0.719-0.844). SMI combined with conventional ultrasound improved the diagnostic specificity (70.0% vs. 90.0%), accuracy (87.4% vs. 96.6%), and PPV (82.5% vs. 93.2%) without decreasing the diagnostic sensitivity (99.3%), yielded higher diagnostic performance with the area under the ROC curve was 0.941 (95%CI: 0904-0.979) compared with conventional US alone (P < 0.001).

CONCLUSION

A VI value 4.05 is a cut-off value with good diagnostic efficacy. The residual root-like and crab claw-like VAs are the characteristic VAs of malignant lesions. Conventional ultrasound combined with the VI and VA can improve the diagnostic specificity, accuracy and PPV without reducing the diagnostic sensitivity.

Association Between the Presence of Female-Specific Tumors and Aggressive Clinicopathological Features in Papillary Thyroid Cancer: A Retrospective Analysis of 9,822 Cases

Objective

To investigate the association between the presence of female-specific tumors and aggressive clinicopathological features in papillary thyroid cancer (PTC).

Methods

This study retrospectively analyzed 9,822 female cases between June 2008 and December 2017. Odds ratios and corresponding 95% confidence intervals were calculated. Findings were stratified by age and body mass index (BMI) in different models.

Results

1443/9822 (14.7%) patients with PTC had a female-specific tumor. Presence of a benign breast mass was an independent risk factor for a primary PTC lesion > 1 cm in diameter (adjusted OR = 1.446, 95% CI 1.136–1.840, P = 0.003), but a protective factor against extrathyroidal extension of PTC (adjusted OR = 0.650, 95%CI 0.500–0.845, P = 0.001). Presence of a benign uterine mass was an independent risk factor for multifocal PTC (adjusted OR = 1.305, 95%CI 1.113–1.531, P = 0.001). Analyses stratified by age and BMI revealed the presence of a benign breast mass was an independent risk factor for a primary PTC lesion > 1 cm in diameter in patients aged <36 years (adjusted OR = 1.711, 95% CI 1.063–2.754, P = 0.027), and a protective factor against extrathyroidal extension of PTC in patients aged ≥36 - <42 years (OR adjusted = 0.533, 95% CI 0.302–0.941, P = 0.030) or with a BMI ≥ 23.4 kg/m² (BMI ≥ 23.4 to < 25.7 kg/m², adjusted OR = 0.441, 95% CI 0.246–0.792, P = 0.006; BMI ≥25.7 kg/m², adjusted OR = 0.558, 95% CI 0.315–0.998, P₂ = 0.045). Presence of a benign uterine mass was an independent risk factor for multifocal PTC in patients aged ≥49 years (adjusted OR = 1.397, 95% CI 1.088–1.793, P = 0.009) or with a BMI <21.5 kg/m² (OR adjusted = 1.745, 95% CI 1.214–2.509, P = 0.003).

Conclusion

The presence of a benign breast mass was an independent risk factor for a primary PTC lesion > 1 cm in diameter and a protective factor against extrathyroidal extension of PTC, while the presence of a benign uterine mass was an independent risk factor for multifocal PTC. Data from this study may help surgeons propose more personalized treatment plans when encountering patients with PTC and female-specific benign tumors.

Wireless ultrasound-guided vacuum-assisted breast biopsy: Experience in clinical practice at European Institute of Oncology

In the last few years, ultrasound-guided vacuum-assisted breast biopsy (US-VABB) has replaced surgical biopsy due to higher diagnostic accuracy and lower patient discomfort, and, at present, an even greater possibility is represented by the new wireless ultrasound-guided VAB device (Wi-UVAB). The purpose of our study is to determine the diagnostic accuracy of this new device in a sizeable representative number of patients. From January 2014 to June 2018, 168 biopsies were performed in our institution using the new Wi-UVAB device. We analyzed sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of biopsies obtained with the new device using surgical results as reference point, following patients for at least one year. In our cohort, we obtained a complete sensitivity of 97.5%, an absolute sensitivity of 94.3%, a complete specificity of 98%, and an absolute specificity of 98%. The positive predictive value of the procedure was 97.5% while the negative predictive value was 98%. The diagnostic accuracy was 98%. The Wi-UVAB is a safe procedure with high diagnostic accuracy, comparable to that of the traditional vacuum-assisted breast biopsy and even higher than that of core needle biopsy (CNB). Moreover, the Wi-UVAB is easy to use and shows low costs as core needle biopsy (CNB).

Prediction for Breast Cancer in BI-RADS Category 4 Lesion Categorized by Age and Breast Composition of Women in Songklanagarind Hospital

Background:

Older age and dense breast are the important risk factors for breast cancer. The ACR BI-RADS lexicon 5^th edition does not mention how patient age and breast density may affect the category assessment. The aim of this study was to investigate whether patient age and breast density influence the positive predictive value (PPV) of mammographic and ultrasonographic findings categorized as BI-RADS category 4 and subcategories 4a, 4b, and 4c among female patients.

Materials and Methods:

A retrospective study was conducted in Songklanagarind Hospital between January 1, 2016 and December 31, 2017 in female patients older than 18 years who had breast lesions categorized as BI-RADS category 4 and subcategories 4a, 4b, 4c. A total of 961 breast lesions consisted of 772 (80.33%) benign lesions and 189 (19.67%) malignant lesions. Categorization was done in each lesion based on age ranges of ≤35 years, >35 to 60 years, and >60 years and breast density according to mammographic breast composition. The PPV for each BI-RADS category was calculated based on the pathological diagnoses and were compared using the chi-square test.

Results:

The overall PPV in each subcategory was in the reference range. The PPV increased with increasing age: 4% vs. 22.63% vs. 36.67% for category 4 (p-value=0.01); 0% vs. 5.81% vs. 6.88% for subcategory 4a (p-value=0.002); 6.67% vs. 26.62% vs. 51.35% for subcategory 4b (p-value=0.001); and 33.33% vs. 76.92% vs. 81.82% for subcategory 4c (p-value=0.02). An association was not found between PPV and breast density.

Conclusion:

A significantly positive association was found between PPV and age in patients in BI-RADS subcategories 4a, 4b, and 4c. This study could not determine that mammographic breast composition according to the ACR BI-RADS 5th edition was associated with PPV due to improper sample distribution.

Ultrasound as a Valuable Imaging Modality in Sclerosing Lymphocytic Lobulitis: Imaging Features Based on a Retrospective Cohort Analysis of 51 Cases

PURPOSE

Sclerosing lymphocytic lobulitis (SLL) is a rare benign breast condition usually associated with diabetes mellitus and whose imaging features have been assessed in few studies, limiting the adoption of diagnostic guidelines. We aimed to identify the main morphological features associated with SLL on imaging examinations (mainly ultrasound and mammography) and to retrospectively evaluate the role that each method played in the diagnostic workup (detection and indication for biopsy).

METHODS

A retrospective study was conducted in a high-volume single center, encompassing 51 consecutive patients (100% female; 26-78 y; 43.7 ± 15.5 y) with histopathologically proven SLL (59 lesions; 0.5-6.1 cm).

RESULTS

Most lesions (31/59; 53%) were found in asymptomatic individuals. Ultrasound detected 91.1% (51 out of 56 lesions assessed by this modality), of which 94.1% were non-circumscribed masses (BI-RADS® 4). Mammography detected 41.6% (15 out of 36 lesions assessed by this modality), with a predominance (80%) of non-calcified ones (masses, asymmetries and distortion). Two-year follow-up was achieved in 29 lesions (49%), showing complete remission (45%) or stability (41%) in most cases.

CONCLUSIONS

Most lesions in this retrospective sample have been detected by means of ultrasound and had their need for biopsy indicated by this modality. Female diabetic patients younger than 40 years presenting with a palpable lesion and a non-circumscribed mass on ultrasound could be submitted to core biopsy; histopathologic findings suggestive of SLL should be considered concordant in this scenario, with subsequent conservative treatment.

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches

Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification’s performance. We introduce a machine-learning method and have designed an analysis procedure of benign and malignant breast tumour classification in ultrasound (US) images without a need for a priori tumour region-selection processing, thereby decreasing clinical diagnosis efforts while maintaining high classification performance. Our dataset constituted 677 US images (benign: 312, malignant: 365). Regarding two-dimensional US images, the oriented gradient descriptors’ histogram pyramid was extracted and utilised to obtain feature vectors. The correlation-based feature selection method was used to evaluate and select significant feature sets for further classification. Sequential minimal optimisation—combining local weight learning—was utilised for classification and performance enhancement. The image dataset’s classification performance showed an 81.64% sensitivity and 87.76% specificity for malignant images (area under the curve = 0.847). The positive and negative predictive values were 84.1 and 85.8%, respectively. Here, a new workflow, utilising machine learning to recognise malignant US images was proposed. Comparison of physician diagnoses and the automatic classifications made using machine learning yielded similar outcomes. This indicates the potential applicability of machine learning in clinical diagnoses.

The Positive Predictive Values of the Breast Imaging Reporting and Data System (BI-RADS) 4 Lesions and its Mammographic Morphological Features

The Breast Imaging Reporting and Data System (BI-RADS) is a comprehensive guideline to systematize breast imaging reporting, and as per its recommendations, any lesion with likelihoods of malignancy greater than 2% is deemed as suspicious and tissue diagnosis is recommended. The aim of the study is to determine the positive predictive value (PPV) of BI-RADS categories 4a, 4b, and 4c for malignancy and association of mammographic morphological features of BI-RADS 4 subgroups with malignant outcomes. We retrospectively reviewed all the patients undergoing mammography with BI-RADS score of 4 followed by biopsy from May 2019 to April 2020. The predictive values of BI-RADS 4 subcategories and morphological features with malignancy are performed taking histopathology report as the gold standard. The PPV of BI-RADS subcategories 4a, 4b, and 4c for malignancies were 34, 89, and 97%, respectively. BI-RADS 4c patients tend to be older (50.2 ± 12.2 vs. 44.6 ± 10.3 years) with larger mass (44 ± 16 vs. 32.9 ± 16.8 mm) at presentation than 4a. Postmenopausal state (P = 0.03) and older age (P = 0.019) were significantly associated with malignancy. There is no meaningful difference observed in the predictability of BI-RADS category 4c lesions among different breast density patterns. The overall higher PPV for BI-RADS 4a and 4b reflects subjectivity in subcategory assignments of BI-RADS 4. In patients, less than 40 years with the BI-RADS 4a category on mammograms may undergo supplementary imaging with MRI which may downscale the lesion classification in turn reducing unnecessary biopsy and surgery.

Contrast-enhanced mammography: past, present, and future

Contrast-enhanced mammography (CEM) combines conventional mammography with iodinated contrast material to improve cancer detection. CEM has comparable performance to breast MRI without the added cost or time of conventional MRI protocols. Thus, this technique may be useful for indications previously reserved for MRI, such as problem-solving, determining disease extent in patients with newly diagnosed cancer, monitoring response to neoadjuvant therapy, evaluating the posttreatment breast for residual or recurrent disease, and potentially screening in women at intermediate- or high-risk for breast cancer. This article will provide a comprehensive overview on the past, present, and future of CEM, including its evolving role in the diagnostic and screening settings.

Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine

In this study, a transfer learning method was utilized to recognize and classify benign and malignant breast tumors, using two-dimensional breast ultrasound (US) images, to decrease the effort expended by physicians and improve the quality of clinical diagnosis. The pretrained deep residual network model was utilized for image feature extraction from the convolutional layer of the trained network; whereas, the linear support vector machine (SVM), with a sequential minimal optimization solver, was used to classify the extracted feature. We used an image dataset with 2099 unlabeled two-dimensional breast US images, collected from 543 patients (benign: 302, malignant: 241). The classification performance yielded a sensitivity of 94.34 % and a specificity of 93.22 % for malignant images (Area under curve = 0.938). The positive and negative predictive values were 92.6 and 94.8, respectively. A comparison between the diagnosis made by the physician and the automated classification by a trained classifier, showed that the latter had significantly better outcomes. This indicates the potential applicability of the proposed approach that incorporates both the pretrained deep learning network and a well-trained classifier, to improve the quality and efficacy of clinical diagnosis.

Current Status and Future of BI-RADS in Multimodality Imaging, From the AJR Special Series on Radiology Reporting and Data Systems

BI-RADS is a communication and data tracking system that has evolved since its inception as a brief mammography lexicon and reporting guide into a robust structured reporting platform and comprehensive quality assurance tool for mammography, ultrasound, and MRI. Consistent and appropriate use of the BI-RADS lexicon terminology and assessment categories effectively communicates findings, estimates the risk of malignancy, and provides management recommendations to patients and referring clinicians. The impact of BI-RADS currently extends internationally through six language translations. A condensed version has been proposed to facilitate a phased implementation of BI-RADS in resource-constrained regions. The primary advance of the 5th edition of BI-RADS is harmonization of the lexicon terms across mammography, ultrasound, and MRI. Harmonization has also been achieved across these modalities for the reporting structure, assessment categories, management recommendations, and data tracking system. Areas for improvement relate to certain common findings that lack lexicon descriptors and a need for further clarification of proper use of category 3. BI-RADS is anticipated to continue to evolve for application to a range of emerging breast imaging modalities.

Subclassification of BI-RADS 4 Magnetic Resonance Lesions: A Systematic Review and Meta-Analysis

OBJECTIVE

This research aims to investigate and evaluate the diagnostic efficacy of magnetic resonance imaging (MRI) in classifying Breast Imaging Reporting and Data System (BI-RADS) 4 lesions into subcategories: 4a, 4b, and 4c, so as to limit biopsies of suspected lesions in the breast.

METHODS

The PubMed, Web of Science, Embase, and Cochrane Library foreign language databases were searched for literature published between January 2000 and July 2018. After analyzing the selection, data extraction, and quality assessment, a meta-analysis was performed, including data pooling, heterogeneity testing, and meta-regression.

RESULTS

Fourteen articles, including 18 studies, met the inclusion criteria. The diagnostic efficacy of MRI for BI-RADS 4-weighted summary assay sensitivity and specificity were estimated at 0.95 [95% confidence interval (CI), 0.89-0.98] and 0.87 (95% CI, 0.81-0.91), respectively. The positive and negative likelihood ratios were 7.1 (95% CI, 4.7-10.7) and 0.06 (95% CI, 0.02-0.14), respectively. The diagnostic odds ratio was 126 (95% CI, 37-426), and the area under the receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). The malignancy ratio of BI-RADS 4a, 4b, and 4c and malignancy range were 2.5% to 18.3%, 23.5% to 57.1%, and 58.0% to 95.2%, respectively.

CONCLUSION

Risk stratification of suspected lesions (BI-RADS categories 4a, 4b, and 4c) can be achieved by MRI. The MRI is an effective auxiliary tool to further subclassify BI-RADS 4 lesions and prevent unnecessary biopsy of BI-RADS 4a lesions.

The Clinical Utility of a Negative Result at Molecular Breast Imaging: Initial Proof of Concept

Purpose

To calculate the negative predictive value (NPV) and false-negative rate (FNR) of molecular breast imaging (MBI) performed in patients who had low-suspicion index findings on mammograms and US images.

Materials and Methods

This retrospective study included patients who had undergone MBI between January 2015 and July 2017, who had index findings on screening mammograms and/or US images, and for whom either histopathologic results or a minimum of 1-year imaging follow-up results were available. A drawn dose of 8 mCi (296 MBq) of technetium 99m sestamibi was administered to all patients for MBI. The NPV and FNR of MBI was calculated for the cohort of 381 findings among 338 women (median age, 56 years; age range, 28-89 years) included in this study.

Results

Overall, 292 of the 381 (76.6%) MBI results were interpreted as negative. Of the 292, 27 patients underwent subsequent biopsies, results of which were negative for cancer; one patient underwent biopsy, and the result was positive for cancer; and 264 patients had true-negative findings based on follow-up imaging for a minimum of 1 year. Of the 89 MBI acquisitions interpreted as positive, there were 36 cancers. The NPV was calculated to be 99.7% (291 of 292, 95% confidence interval [CI]: 99.1%, 100%), and the FNR was 2.7% (one of 37, 95% CI: 0%, 7.9%). Interposing MBI reduced the number of biopsies by 67.5%.

Conclusion

The concept of the clinical utility of a negative MBI result may be valid but requires further testing.Keywords: Breast, Molecular Imaging-Cancer© RSNA, 2020.

Scoring System to Stratify Malignancy Risks for Mammographic Microcalcifications Based on Breast Imaging Reporting and Data System 5th Edition Descriptors

Objective

To develop a scoring system stratifying the malignancy risk of mammographic microcalcifications using the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS).

Materials and Methods

One hundred ninety-four lesions with microcalcifications for which surgical excision was performed were independently reviewed by two radiologists according to the 5th edition of BI-RADS. Each category's positive predictive value (PPV) was calculated and a scoring system was developed using multivariate logistic regression. The scores for benign and malignant lesions or BI-RADS categories were compared using an independent t test or by ANOVA. The area under the receiver operating characteristic curve (AUROC) was assessed to determine the discriminatory ability of the scoring system. Our scoring system was validated using an external dataset.

Results

After excision, 69 lesions were malignant (36%). The PPV of BI-RADS descriptors and categories for calcification showed significant differences. Using the developed scoring system, mean scores for benign and malignant lesions or BI-RADS categories were significantly different (p < 0.001). The AUROC of our scoring system was 0.874 (95% confidence interval, 0.840–0.909) and the PPV of each BI-RADS category determined by the scoring system was as follows: category 3 (0%), 4A (6.8%), 4B (19.0%), 4C (68.2%), and 5 (100%). The validation set showed an AUROC of 0.905 and PPVs of 0%, 8.3%, 11.9%, 68.3%, and 94.7% for categories 3, 4A, 4B, 4C, and 5, respectively.

Conclusion

A scoring system based on BI-RADS morphology and distribution descriptors could be used to stratify the malignancy risk of mammographic microcalcifications.

Contrast Enhanced Digital Mammography (CEDM) Helps to Safely Reduce Benign Breast Biopsies for Low to Moderately Suspicious Soft Tissue Lesions

RATIONALE AND OBJECTIVES

To preliminarily asses if Contrast Enhanced Digital Mammography (CEDM) can accurately reduce biopsy rates for soft tissue BI-RADS 4A or 4B lesions.

MATERIALS AND METHODS

Eight radiologists retrospectively and independently reviewed 60 lesions in 54 consenting patients who underwent CEDM under Health Insurance Portability and Accountability Act compliant institutional review board-approved protocols. Readers provided Breast Imaging Reporting & Data System ratings sequentially for digital mammography/digital breast tomosynthesis (DM/DBT), then with ultrasound, then with CEDM for each lesion. Area under the curve (AUC), true positive rates and false positive rates, positive predictive values and negative predictive values were calculated. Statistical analysis accounting for correlation between lesion-examinations and between-reader variability was performed using OR/DBM (for SAS v.3.0), generalized linear mixed model for binary data (proc glimmix, SAS v.9.4, SAS Institute, Cary North Carolina), and bootstrap.

RESULTS

The cohort included 49 benign, two high-risk and nine cancerous lesions in 54 women aged 34-74 (average 50) years. Reader-averaged AUC for CEDM was significantly higher than DM/DBT alone (0.85 versus 0.66, p < 0.001) or with US (0.85 versus 0.75, p = 0.001). CEDM increased true positive rates from 0.74 under DB/DBT, and 0.89 with US, to 0.90 with CEDM, (p = 0.019 DM/DBT versus CEDM, p = 0.78 DM/DBT + US versus CEDM) and decreased false positive rates from 0.47 using DM/DBT and 0.61 with US to 0.39 with CEDM (p = 0.017 DM/DBT versus CEDM, p = 0.001 DM/DBT+ US versus CEDM). For an expected cancer rate of 10%, CEDM positive predictive values was 20.5% (95% CI: 16%-27%) and negative predictive values 98.3% (95% CI: 96%-100%).

CONCLUSION

Addition of CEDM for evaluation of low-moderate suspicion soft tissue breast lesions can substantially reduce biopsy of benign lesions without compromising cancer detection.

Cost-effectiveness of stereotactic vacuum-assisted biopsy for nonpalpable breast lesions

PURPOSE

To examine the potential cost-savings of stereotactic vacuum-assisted biopsy (SVAB) over open surgical biopsy (OSB) in diagnosis of nonpalpable lesions on mammography and to estimate the cost-saving effect on lesions at different levels of malignant probability.

METHODS

This retrospective study was approved by our Institutional Review Board. We retrospectively reviewed 276 (33.8 %) SVAB and 541 (66.2 %) OSB medical records at a medical center. Direct costs included patients' self-paid and national health insurance claim charges. Indirect costs were calculated using sick days, average salary, and age-adjusted employment rate. One-way and two-way sensitivity analyses were conducted. Lesion classification was determined by the assessment categories of Breast Imaging Reporting and Data System (BI-RADS), 4th or 5th editions.

RESULTS

SVAB decreased the direct cost by $90.3 (10.1 %) per diagnosis. The indirect cost was decreased by $560.2 (96.0 %). Overall, SVAB saved 43.9 % of resource utilization for each biopsy. Taking the cost of the subsequent malignant surgery into account, from the healthcare providers' perspective, SVAB was cost-effective if a lesion had less than 19 % likelihood of malignancy. From the societal perspective, SVAB reduced productivity loss for all the lesions. Based on the positive predictive value of the BI-RADS categories, SVAB was more suitable for the lesions of category 4A and category 3, resulting in greater savings in both medical and societal resources.

CONCLUSIONS

SVAB is a cost-effective diagnostic option for nonpalpable breast lesions. The cost-saving effect is greater for the lesions of category 4A and category 3.

The Vascular Index of Superb Microvascular Imaging Can Improve the Diagnostic Accuracy for Breast Imaging Reporting and Data System Category 4 Breast Lesions

PURPOSE

To investigate whether the vascular index (VI) of superb microvascular imaging (SMI) could improve the diagnostic efficiency for BI-RADS 4 breast lesions and reduce the number of unnecessary biopsies.

PATIENTS AND METHODS

For this study, we selected 222 consecutive BI-RADS 4 breast lesions detected by ultrasound and confirmed by pathology from January 2016 to October 2018. A VI of 4.0 was set as the cutoff value to degrade BI-RADS classification. We calculated the accuracy, sensitivity and PPV of a BI-RADS diagnosis alone and the combination of BI-RADS and the VI.

RESULTS

Pathologically, of the 222 lesions, 129 were confirmed to be benign, and 93 were found to be malignant. A VI of 4.0 was set as the cutoff value; when the VI≤4.0, those BI-RADS 4 masses were downgraded one level (4C-4B, 4B-4A, 4A-3) to an integral BI-RADS grade, while the others maintained the conventional grade. A total of 54 BI-RADS 4 lesions were degraded to BI-RADS 3, including 53 benign lesions and 1 malignant lesion. The diagnostic accuracy (65.3% vs 41.9%) and PPV (54.8% vs 41.9%) were significantly improved. The sensitivity decreased slightly (98.9% vs 100%) because 1 of the 54 downgraded BI-RADS 4 lesions, which had a pathological type of invasive ductal carcinoma, was incorrectly downgraded.

CONCLUSION

SMI is a noninvasive tool for visualizing the vascular structure with high-resolution microvascular images. As a quantitative index, the VI can be used to appropriately downgrade benign lesions classified as BI-RADS 4, which can improve the diagnostic accuracy and PPV and reduce unnecessary biopsies.

Contrast-enhanced spectral mammography: A potential exclusion diagnosis modality in dense breast patients

BACKGROUND

China has an increasing burden of breast cancer. However, with a large population of dense breast patients, the diagnostic efficiency of conventional digital mammography is attenuated.

METHODS

From July 2017 to October 2018, we retrospectively reviewed 397 dense breast patients who underwent contrast-enhanced spectral mammography (CESM) in West China Hospital. Among them, 53 patients who had both CESM and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) results and 114 patients who had pathological diagnoses were finally enrolled. All images were reviewed by two independent radiologists according to the 2013 Breast Imaging Reporting and Data System (BI-RADS) with all disagreements handed to an associate professor for final decisions. Correlation analyses between CESM and DCE-MRI were conducted. The diagnostic performance of CESM were investigated.

RESULTS

The kappa value of the BI-RADS scores between CESM and DCE-MRI was 0.607 (P < .001), indicating high correspondence between CESM and DCE-MRI. As for lesion size measurement, moderate correlation (Kendall's tau coefficient: 0.556, P < .001) was detected between CESM and DCE-MRI. Using pathological diagnoses as the reference standard, the sensitivity, specificity, and area under the curve (AUC) of CESM were 82.4%, 96.4%, and 0.894, respectively.

CONCLUSION

CESM demonstrated excellent overall diagnostic accuracy and a moderate correlation in lesion size estimation against DCE-MRI in dense breast patients, supporting it to be an alternative to DCE-MRI in breast cancer detection and diagnosis, especially for exclusion diagnosis.

Evaluation of the positive predictive value (PPV3) of ACR BI-RADS category 4 and 5 based on the outcomes of Invasive Diagnostic Office in an outpatient clinic

PURPOSE

The American College of Radiology (ACR) BI-RADS classification is the applicable for breast lesion assessment. BI-RADS categories 4 and 5 need to be followed by biopsy. The aim of our study was to evaluate the tissue biopsy-proven positive predictive value (PPV3) for BI-RADS 4 (and its subcategories) and for BI-RADS 5, and BI-RADS distribution, in comparison to ACR assumptions and literature.

MATERIAL AND METHODS

We retrospectively analysed biopsies performed in our outpatient clinic in 2017. Our target group of patients consisted of 797 patients at the average age of 52 years.

RESULTS

BI-RADS 5 constituted 12% of cases (95 cases), and BI-RADS 4 88% (698 cases). Within BI-RADS 4 subdivisions there were 359 cases in 4a (45.3%), 215 in 4b (27%), and 124 in 4c (15.6%). Overall PPV3 was 34.8%. BI-RADS 5 category PPV3 number was 97.89%. In category BI-RADS 4 the values of PPV3 equalled 26.22% without subdivision and 3.6%, 27.9%, and 88.7% for subcategories BI-RADS 4a, 4b, and 4c, respectively.

CONCLUSIONS

BI-RADS categorisation by radiologists in the studied group matches the literature data according to achieved PPV and BI-RADS percentage distribution. The stratification of cancer risk among categories was proven with Mann-Whitney U test p value < 0.005. There was a statistically important unaccepted difference of PPV3 between core biopsy and vacuum-assisted biopsy, which needs further investigation.