Comparison of malignant calcification identification between breast cone-beam computed tomography and digital mammography

BACKGROUND

Calcifications are important abnormal findings in breast imaging and help in the diagnosis of breast cancer.

PURPOSE

To compare breast cone-beam computed tomography (CBCT) with digital mammography (DM) in terms of the ability to identify malignant calcifications.

MATERIAL AND METHODS

In total, 115 paired examinations were performed utilizing breast CBCT and DM; 86 pathology-proven malignant lesions with calcifications detected on DM and 29 randomly selected breasts without calcifications were reviewed by three radiologists. The ability to detect calcifications was assessed on CBCT images. The characterization agreement of two imaging modalities was evaluated by the kappa coefficient. For breast CBCT images, the parameters for the display of calcifications were recorded. The Kruskal-Wallis test was used to compare the preferred slice thickness chosen by each of the three radiologists. The degree of calcification clarity was compared between two modalities using the Mann-Whitney U-test.

RESULTS

The combined sensitivity and specificity of three radiologists in 85 DM-detected calcifications detection on breast CBCT images were 98.43% (251/255) and 98.85% (86/87), respectively. CBCT images showed substantial agreement with mammograms in terms of the characterization of calcifications morphology (k = 0.703; P < 0.05) and distribution (k = 0.629; P < 0.05). CBCT images with a slice thickness of 0.273 mm and three-dimensional maximum-intensity projection (3D-MIP) were more beneficial for calcifications identification. No statistically significant difference was found between standard DM views and CBCT images for three radiologists on calcification display clarity.

CONCLUSION

CBCT images were comparable to mammograms in calcification identification and may be sufficient for malignant calcifications detection and characterization.

How to optimize MRI breast protocol? The value of combined analysis of ultrafast and diffusion-weighted MRI sequences

PURPOSE

The purpose of this retrospective study was to demonstrate the validity of early enhancement criteria on ultrafast magnetic resonance imaging (MRI) sequence to predict malignancy in a large population, and the benefit of diffusion-weighted imaging (DWI) to improve the performance of breast MRI.

MATERIAL AND METHODS

Women who underwent breast MRI examination between April 2018 and September 2020 and further breast biopsy were retrospectively included. Two readers quoted the different conventional features and classified the lesion according to the BI-RADS classification based on the conventional protocol. Then, the readers checked for the presence of early enhancement (≤ 30 s) on ultrafast sequence and the presence of an apparent diffusion coefficient (ADC) ≥ 1.5 × 10^-3 mm²/s to classify the lesions based on morphology and these two functional criteria only.

RESULTS

Two hundred fifty-seven women (median age: 51 years; range: 16-92 years) with 436 lesions (157 benign, 11 borderline and 268 malignant) were included. A MRI protocol plus two simple functional features, early enhancement (≤ 30 s) and an ADC value ≥ 1.5 × 10^-3 mm²/s, had a greater accuracy than the conventional protocol to distinguish benign from malignant breast lesions with or without ADC value (P = 0.01 and P = 0.001, respectively) on MRI, mainly due to better classification of benign lesions (increased specificity) with increasing diagnostic confidence of 3.7% and 7.8% respectively.

CONCLUSION

BI-RADS analysis based on a simple short MRI protocol plus early enhancement on ultrafast sequence and ADC value has a greaterr diagnostic accuracy than a conventional protocol and may avoid unnecessary biopsy.

AI-assisted Method for Efficiently Generating Breast Ultrasound Screening Reports

BACKGROUND

Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report, which is time-consuming and laborious, and it is easy to miss and miswrite.

AIM

We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing.

METHODS

AI efficiently generated personalized breast ultrasound screening preliminary reports, especially for benign and normal cases, which account for the majority. Doctors then make simple adjustments or corrections based on the preliminary AI report to generate the final report quickly. The approach has been trained and tested using a database of 4809 breast tumor instances.

RESULTS

Experimental results indicate that this pipeline improves doctors' work efficiency by up to 90%, greatly reducing repetitive work.

CONCLUSION

Personalized report generation is more widely recognized by doctors in clinical practice than non-intelligent reports based on fixed templates or options to fill in the blanks.

Evaluating breast ultrasound S-detect image analysis for small focal breast lesions

Background

S-Detect is a computer-assisted, artificial intelligence-based system of image analysis that has been integrated into the software of ultrasound (US) equipment and has the capacity to independently differentiate between benign and malignant focal breast lesions. Since the revision and upgrade in both the breast imaging-reporting and data system (BI-RADS) US lexicon and the S-Detect software in 2013, evidence that supports improved accuracy and specificity of radiologists' assessment of breast lesions has accumulated. However, such assessment using S-Detect technology to distinguish malignant from breast lesions with a diameter no greater than 2 cm requires further investigation.

Methods

The US images of focal breast lesions from 295 patients in our hospital from January 2019 to June 2022 were collected. The BI-RADS data were evaluated by the embedded program and as manually modified prior to the determination of a pathological diagnosis. The receiver operator characteristic (ROC) curves were constructed to compare the diagnostic accuracy between the assessments of the conventional US images, the S-Detect classification, and the combination of the two.

Results

There were 326 lesions identified in 295 patients, of which pathological confirmation demonstrated that 239 were benign and 87 were malignant. The sensitivity, specificity, and accuracy of the conventional imaging group were 75.86%, 93.31%, and 88.65%. The sensitivity, specificity, and accuracy of the S-Detect classification group were 87.36%, 88.28%, and 88.04%, respectively. The assessment of the amended combination of S-Detect with US image analysis (Co-Detect group) was improved with a sensitivity, specificity, and accuracy of 90.80%, 94.56%, and 93.56%, respectively. The diagnostic accuracy of the conventional US group, the S-Detect group, and the Co-Detect group using area under curves was 0.85, 0.88 and 0.93, respectively. The Co-Detect group had a better diagnostic efficiency compared with the conventional US group (Z = 3.882, p = 0.0001) and the S-Detect group (Z = 3.861, p = 0.0001). There was no significant difference in distinguishing benign from malignant small breast lesions when comparing conventional US and S-Detect techniques.

Conclusions

The addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules.

Clinical use and adjustment of ultrasound elastography for breast lesions followed WFUMB guidelines and recommendations in the real world

Objective

This study aimed to explore the value of strain elastography (SE) and shear wave elastography (SWE) following the World Federation of Ultrasound in Medicine and Biology (WFUMB) guidelines and recommendations in the real world in distinguishing benign and malignant breast lesions and reducing biopsy of BI-RADS (Breast Imaging Reporting and Data System) 4a lesions.

Methods

This prospective study included 274 breast lesions. The elastography score (ES) by the Tsukuba score, the strain ratio (SR) for SE, and Emax for SWE of the lesion(A) and the regions(A') included the lesion and the margin (0.5-5 mm) surrounding the lesion were measured. The sensitivity, specificity, and AUC were calculated and compared by the cutoff values recommended by WFUMB guidelines.

Results

When scores of 1 to 3 were classified as probably benign by WFUMB recommendation, the ES was significantly higher in malignant lesions compared to benign lesions (p < 0.05) in all lesions. For the cohort by size >20 mm, the sensitivity was 100%, and the specificity was 45.5%. ES had the highest AUC: 0.79(95% CI 0.72-0.86) with a sensitivity of 96.2%, and a specificity of 61.8% for the cohort by size ≤20 mm. For the Emax-A'-S2.5mm, when the high stiffness would be considered with Emax above 80 kPa in SWE, the malignant lesions were diagnosed with a sensitivity of 95.8%, a specificity of 43.3% for all lesions, a sensitivity of 88.5% for lesions with size ≤20 mm, and sensitivity of 100.0% for lesions with size >20 mm. In 84 lesions of BI-RADS category 4a, if category 4a lesions with ES of 1-3 points or Emax-A'-S2.5 less than 80 kPa could be downgraded to category 3, 52 (61.9%) lesions could be no biopsy, including two malignancies. If category 4a lesions with ES of 1-3 points and Emax-A'-S2.5 less than 80kPa could be downgraded to category 3, 23 (27.4%) lesions could be no biopsy, with no malignancy.

Conclusions

The elastography score for SE and Emax-A' for SWE after our modification were beneficial in the diagnosis of breast cancer. The combination of SWE and SE could effectively reduce the biopsy rate of BI-RADS category 4a lesions.

Accuracy and Upgrading of CNB and BI-RADS Diagnoses Compared to Excision: A Clinicopathological-Radiological Correlation of Papillary Breast Lesions and Neoplasms

BACKGROUND

Papillary breast lesions and neoplasms (PBLs/Ns) are diagnostically challenging lesions in both core needle biopsy (CNB) and radiology.

AIM

To determine the accuracy and upgrade rate of CNB and BI-RADS diagnosis of PBLs/Ns compared to final excision diagnosis and the factors linked to upgrade.

METHODS

The favored CNB diagnosis and BI-RADS category for 82 PBLs/Ns were assessed based on histopathology, myoepithelial marker immunohistochemistry, mammographic/ultrasonographic findings. The radiological findings were compared to the pathological diagnoses. The accuracies of CNB and BI-RADS were compared to the excision diagnosis of the corresponding PBLs/Ns. The upgrade rates to malignancy were evaluated for both CNB and BI-RADS.

RESULTS

The presence of solid, irregular masses in breasts with composition A/B with calcification in radiology was significantly associated with the diagnosis of suspicious/malignant CNB, and malignant excision specimens (p<0.05). CNB was more accurate (90%), sensitive and specific with high positive and negative predictive values than BI-RADS. Combined CNB/BI-RADS accuracy was 90.2%. Overall upgrade rate came up to 9.8%. Upgrade rates to carcinoma were 7.3% for CNB and 8.5% for BI-RADS. Factors linked to upgrade were the age, lesion-size, BI-RADS category 4A and C, and histopathological/radiological discordance. All the upgraded PBLs/Ns were diagnosed as benign lesions in CNB with present/focally present myoepithelial diagnosis reflecting a sampling error.

CONCLUSION

Up to 9.8% of PBLs/Ns diagnosed on CNB and BI-RADS undergo upgrading upon final excision, despite the high diagnostic accuracy. These evidences should be considered for final decision on whether to excise the lesion or not.

Identification of the Benignity and Malignancy of BI-RADS 4 Breast Lesions Based on a Combined Quantitative Model of Dynamic Contrast-Enhanced MRI and Intravoxel Incoherent Motion

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann-Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_{_ADC}, model_{_IVIM}, model_{_DCE}, model_{_DCE+ADC}, and model_{_DCE+IVIM}). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_{_DCE+IVIM} showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828-0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_{_ADC} (p = 0.014) and model_{_IVIM} (p = 0.033). The model_{_ADC} had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672-0.846) but was not significantly different from model_{_IVIM} (p = 0.168). The united quantitative model with DCE-MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated.

Opinions on the Assessment of Breast Density Among Members of the Society of Breast Imaging

OBJECTIVE

Dense breast decreases the sensitivity and specificity of mammography and is associated with an increased risk of breast cancer. We conducted a survey to assess the opinions of Society of Breast Imaging (SBI) members regarding density assessment.

METHODS

An online survey was sent to SBI members twice in September 2020. The survey included active members who were practicing radiologists, residents, and fellows. Mammograms from three patients were presented for density assessment based on routine clinical practice and BI-RADS fourth and fifth editions. Dense breasts were defined as heterogeneously or extremely dense. Frequencies were calculated for each survey response. Pearson's correlation coefficient was used to evaluate the correlation of density assessments by different definitions.

RESULTS

The survey response rate was 12.4% (357/2875). For density assessments, the Pearson correlation coefficients between routine clinical practice and BI-RADS fourth edition were 0.05, 0.43, and 0.12 for patients 1, 2, and 3, respectively; these increased to 0.65, 0.65, and 0.66 between routine clinical practice and BI-RADS fifth edition for patients 1, 2, and 3, respectively. For future density grading, 79.0% (282/357) of respondents thought it should reflect both potential for masking and overall dense tissue for risk assessment. Additionally, 47.1% (168/357) of respondents thought quantitative methods were of use.

CONCLUSION

Density assessment varied based on routine clinical practice and BI-RADS fourth and fifth editions. Most breast radiologists agreed that density assessment should capture both masking and overall density. Moreover, almost half of respondents believed computer or artificial intelligence-assisted quantitative methods may help refine density assessment.

Mammographic breast density and the risk of breast cancer: A systematic review and meta-analysis

OBJECTIVES

Mammographic density is a well-defined risk factor for breast cancer and having extremely dense breast tissue is associated with a one-to six-fold increased risk of breast cancer. However, it is questioned whether this increased risk estimate is applicable to current breast density classification methods. Therefore, the aim of this study was to further investigate and clarify the association between mammographic density and breast cancer risk based on current literature.

METHODS

Medline, Embase and Web of Science were systematically searched for articles published since 2013, that used BI-RADS lexicon 5th edition and incorporated data on digital mammography. Crude and maximally confounder-adjusted data were pooled in odds ratios (ORs) using random-effects models. Heterogeneity regarding breast cancer risks were investigated using I² statistic, stratified and sensitivity analyses.

RESULTS

Nine observational studies were included. Having extremely dense breast tissue (BI-RADS density D) resulted in a 2.11-fold (95% CI 1.84-2.42) increased breast cancer risk compared to having scattered dense breast tissue (BI-RADS density B). Sensitivity analysis showed that when only using data that had adjusted for age and BMI, the breast cancer risk was 1.83-fold (95% CI 1.52-2.21) increased. Both results were statistically significant and homogenous.

CONCLUSIONS

Mammographic breast density BI-RADS D is associated with an approximately two-fold increased risk of breast cancer compared to having BI-RADS density B in general population women. This is a novel and lower risk estimate compared to previously reported and might be explained due to the use of digital mammography and BI-RADS lexicon 5th edition.

Solitary Dilated Ducts Revisited: Malignancy Rate and Implications for Management

RATIONALE AND OBJECTIVES

A solitary dilated duct (SDD) is a single asymmetrically dilated breast duct with diameter more than 2 mm. The Breast Imaging Reporting and Data System (BI-RADS) fifth edition recommends additional imaging and biopsy for SDDs without demonstrated benign etiology, however management of this rare entity remains controversial. This study describes practice patterns, malignancy rate, and features associated with high-risk/malignant SDDs to better stratify patients requiring biopsy versus follow-up.

MATERIALS AND METHODS

This IRB-approved retrospective study identified mammographic, sonographic and MRI exams utilizing the term "solitary dilated duct" at a multisite academic institution between 1/1/2010 and 12/31/2020. Clinical and imaging features, BI-RADS assessments, and outcomes were analyzed. Univariate and multivariate analyses identified predictors of high-risk/malignant histology.

RESULTS

SDDs identified in 49 women (mean age 56.1 years) were assessed as BI-RADS 4/5 (31/49, 63%), BI-RADS 3 (9/49, 18%), or BI-RADS 2 (9/49, 18%). Most sampled lesions were benign (16/31, 52%) and the remaining were high-risk (15/31, 48%, all papillary lesions). The only papilloma with atypia on core biopsy upgraded to grade 2 DCIS on excision (malignancy rate 1/49, 2%). All anechoic SDDs were benign (n=13), and all benign SDDs lacked internal vascularity. SDDs with associated masses were associated with malignant/high-risk outcomes on multivariate analysis (p < .001).

CONCLUSION

The BI-RADS fifth edition recommends biopsy for SDDs without demonstrated benign etiology. In our 11-year study period, practice patterns were variable with a low malignancy rate of 2%. Our findings suggest that anechoic SDDs may be followed, and SDDs with associated masses or internal vascularity require biopsy.

Sonographic features of primary breast lymphoma: An analysis of 10 cases

BACKGROUND

Primary breast lymphoma (PBL) is a rare malignant breast tumor. The literature concerning PBL ultrasound is based primarily on case reports, with only a few case series reported to date.

PURPOSE

This study aimed to elucidate the sonographic characteristics of PBL and explore the value of ultrasonography in the preoperative diagnosis of PBL using the Breast Imaging Reporting and Data System (BI-RADS).

METHODS

A retrospective review of files involving a diagnosis of PBL (2013-2020) was conducted in the Department of Pathology, Zhejiang Provincial People's Hospital, Hangzhou, and the First Affiliated Hospital of Wenzhou Medical University, Wenzhou. The clinical characteristics and sonographic features of 12 lesions in 10 patients were analyzed and discussed in light of the literature.

RESULTS

All patients, aged 50.40 ± 14.31 years (range 30-66 years), had clinically palpable lumps. Most cases were on the right breast and were unilateral. Only one patient had mucosa-associated lymphoma. The histological type of the other patients was diffuse large B-cell lymphoma (DLBCL). Ultrasonography revealed nodular and diffuse PBL lesions without internal calcification. The nodular PBL was hypoechoic or mixed hypo- to hyperechoic, with a differential lobulated shape and horizontal growth. Although color Doppler flow imaging (CDFI) showed no significant features, the ultrasound findings were categorized as BI-RADS 4 in 10 of the 12 lesions and BI-RADS 5 in two lesions. All patients were suspected of having malignancies (BI-RADS 4 or 5).

CONCLUSION

PBL was mostly found in middle-aged and elderly women, and the right breast was more prone to the development of malignancies. PBL lesions were classified as either nodular or diffuse, based on the boundaries of the tumors in the ultrasound images. Typical PBL was characterized by hypoechoic or heterogeneous lesions with circumscribed or microlobulated margins and horizontal growth. The sonographic features of the PBL lesions and the BI-RADS categorizations of the lesions analyzed suggested malignancy.

Establishment of a deep-learning system to diagnose BI-RADS4a or higher using breast ultrasound for clinical application

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI‐RADS) has become widespread worldwide, the problem of inter‐observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a system in which artificial intelligence (AI) can distinguish whether a static image obtained using a breast ultrasound represents BI‐RADS3 or lower or BI‐RADS4a or higher to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4028 images containing 5014 lesions and a test dataset consisting of 3166 images containing 3656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 91.2%, and 90.7%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Although deep‐learning methods to categorize benign and malignant tumors using breast ultrasound have been extensively reported, our work represents the first attempt to establish an AI system to classify BI‐RADS3 or lower and BI‐RADS4a or higher successfully, providing important implications for clinical actions. These results suggest that the AI diagnostic system is sufficient to proceed to the next stage of clinical application.

Ultrasound features of pregnancy-associated breast cancer: A retrospective observational analysis

Pregnancy-associated breast cancer (PABC) is a poor prognosis in women, and the mortality rate is higher in this subgroup of patients than in non-PABC. This study aims to assess clinicopathological and ultrasound features of patients with PABC. Of 75 patients with breast cancer, 31 cases were in lactating, or pregnancy phase and 44 patients had no recent history of pregnancy/lactation at the time of cancer detection. The available pathological characteristics and ultrasound findings of the PABC and non-PABC groups were compared. The analysis of ultrasound findings demonstrated that the percentages of antiparallel orientation (p = 0.04) and heterogeneous internal echo pattern (p = 0.002) were higher in the PABC group. The final Breast Imaging Reporting and Data System (BI-RADS) assessment in the two groups was significantly different (p = 0.008). In this study, most PABCs were BI-RADS 4c or 5; compared with age-matched non-PABC cases. There were significant differences in ER (p = 0.03), receptor groups (p = 0.007), and tumor grade (p = 0.02) in PABC compared to non-PABC group. To conclude, radiologists should be careful about ultrasound findings of PABC and recommend core needle biopsy in suspected cases.

Comparative study of breast core needle biopsy (CNB) findings with ultrasound BI-RADS subtyping

&lt;b&gt; Introduction:&lt;/b&gt; Given the high prevalence of breast cancer, developing quick and accessible diagnostics solutions is critical. The BIRADS classification is a reliable method for assessing and estimating the risk of malignancy in breast lesions. &lt;/br&gt;&lt;/br&gt; &lt;b&gt;Aim:&lt;/b&gt; The aim of this study was to compare the results of core needle biopsy of breast lesions and sonographic findings based on the BIRADS category in Yazd. &lt;/br&gt;&lt;/br&gt; &lt;b&gt;Materials and methods:&lt;/b&gt; This retrospective analytical study was done on all core needle biopsy specimens referred to Mortaz hospital, Yazd, Iran from 2010 to 2019. Demographic data such as age, laterality of the lesion, BIRADS category, and pathology reports were extracted from patients&apos; hospital folders. Data were analyzed by SPSS version 21. P &lt; 0.05 was considered statistically significant. &lt;/br&gt;&lt;/br&gt; &lt;b&gt;Results:&lt;/b&gt; In total, 514 cases with a mean age of 43.9 9.4 years were studied. Among them, 104 cases (20.2%) were malignant and 410 cases (79.8%) were benign. The most common benign and malignant lesions were fibroadenoma (24.9%), and infiltrative ductal carcinoma (83.7%) respectively. The most common BIRADS was class 4A (54.9%). Patients with benign lesions were mostly in the 3rd and 4th decade of life, while malignant lesions were more in the 4th and 5th decades, and this difference was statistically significant (P = 0.001). The correlation between ultrasound diagnoses (BIRADS) and pathology findings was statistically significant (P &lt; 0.001). &lt;/br&gt;&lt;/br&gt; &lt;b&gt;Conclusion&lt;/b&gt;: Based on the results, there is a significant correlation between ultrasound outcomes according to BIRADS and pathology results, and the radiology-pathology accordance, owing to its high accuracy, can be very helpful in correctly diagnosing, monitoring, and managing the lesion.

Correlation of Sonographic Findings and Histopathological Diagnoses in Women Presenting With Breast Masses

BACKGROUND

Breast lumps have been reported as the most common breast symptom among adult females in Western Nigeria and are benign in 60% of cases. In South-Eastern Nigeria, fibroadenoma has been reported as the most common breast disease (47.5%), followed by carcinoma (30.4%) and fibrocystic disease. The aim of this study was to determine the correlation between sonographic and histopathologic findings in women who presented with breast masses.

MATERIALS AND METHODS

This was a cross-sectional study conducted among 160 consecutive female patients who presented with breast masses. A breast ultrasound scan was carried out to categorize the masses using the American College of Radiology Breast Imaging Reporting and Data System classification, and the histopathological diagnoses of the masses were obtained. The correlation of the sonographic findings and histopathological diagnoses was determined using the Statistical Package for Social Sciences (SPSS) IBM version 23.0.

RESULTS

Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were found to be 79.5%, 98.3%, 93.9%, 93.7%, and 93.8%, respectively. There was a positive correlation between the sonographic findings and histopathological diagnoses of the breast masses, which was statistically significant (P = 0.000, r = 0.846).

CONCLUSION

This study found a statistically significant positive correlation between sonographic findings and histopathological diagnoses of breast masses.

Comparison of Diagnostic Accuracies of USG, MG and MRI Modalities Defined with BI-RADS Classification System

BACKGROUND

BI-RADS classification provides facilitating information in diagnosis for radiologists. It allows radiologists to interpret mammograms accurately Objective: We aimed to compare the diagnostic accuracy of the modalities with the BI-RADS classification system made with imaging findings accompanied by USG, MG and MRI, which are a total of 3 modalities.

METHODS

This study included 82 patients who underwent Tru-Cut biopsy under the guidance of USG, MG, and MRI. Mammography, sonography and MRI were performed in the prone position.

RESULTS

Of the patients, 46.3%, 14.6%, and 39.0% were assessed in 4A, 4B, and 5 MRI BI-RADS categories, respectively. Based on the variable surgical/pathological diagnosis, 50%, 28.0%, and 22.0% of the patients were categorized as malignant findings, benign findings, and infection-inflammation-mastitis, respectively. The determination of the endpoints for the parameter of long-axis diameter (mm) was found to be statistically significant according to ROC analysis as a gold standard performed based on specificity levels of benign and malignant findings (p<0.05). A significant correlation was detected between the gold standard and the categorical variable MRI BI-RADS (χ^2=46.380, p<0.01).

CONCLUSION

When specificity and sensitivity of all three modalities in surgical/pathological diagnosis were compared, it was concluded that MRI was superior to the other modalities, and a valuable method in prediction of lesion malignancy and determination of biopsy prediction and priority.

The diagnostic value of contrast-enhanced ultrasound and superb microvascular imaging in differentiating benign from malignant solid breast lesions: A systematic review and meta-analysis

OBJECTIVE

To investigate the added value of contrast-enhanced ultrasound (CEUS) and superb microvascular imaging (SMI) to the conventional ultrasound (US) in the diagnosis of breast lesions.

METHODS

PubMed, EMBASE, Web of Science, Chinese national knowledge infrastructure databases, Chinese biomedical literature databases, and Wanfang were searched for relevant studies from November 2015 to November 2021. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Studies (QUADAS) tool. Meta-Disc version 1.4 was used to calculate sensitivity (SEN), specificity (SPE), positive likelihood ratio (LR +), negative likelihood ratio (LR-), area under curve (AUC), and diagnostic odds ratio (DOR). Meta-regression analysis was performed using STATA 16.0 software to compare the diagnostic accuracy of the two techniques.

RESULTS

In the five studies included, 530 patients were eligible for this meta-analysis. For SMI, the pooled SEN and SPE were 0.75 (95% confidence interval [CI]: 0.69-0.91) and 0.88 (95% CI: 0.83-0.91), respectively, LR + was 5.75 (95% CI: 4.26-7.78), LR- was 0.29 (95% CI: 0.23-0.36), DOR was 21.42 (95% CI, 13.61-33.73), and AUC was 0.8871. For CEUS, the pooled SEN and SPE were 0.87 (95% CI: 0.82-0.91) and 0.86 (95% CI: 0.82-0.89), respectively, LR + was 5.92 (95% CI: 4.21-8.33), LR- was 0.16 (95% CI: 0.11-0.25), DOR was 38.27 (95% CI: 18.73-78.17), and AUC was 0.9210.

CONCLUSIONS

Adding CEUS and (or) SMI to conventional US could improve its diagnostic performance in differentiating benign from malignant solid breast lesions.

Can shear wave elastography be utilized as an additional tool for the assessment of non-mass breast lesions?

INTRODUCTION

We aimed to describe shear wave elastography parameters of non-mass lesions of the breast and to assess the measures of diagnostic accuracy of shear wave elastography in the differentiation of non-mass lesions compared with conventional ultrasound, using histopathologic results as the reference standard.

METHODS

This retrospective study included breast ultrasound-detected non-mass lesions with a confirmed pathologic diagnosis during a two-year study period. B-mode ultrasound and shear wave elastography were performed for all lesions before biopsy. Ultrasound features, shear wave elastography parameters (mean elasticity and maximum stiffness color), as well as Breast Imaging-Reporting and Data System categories were recorded for each lesion. Measures of diagnostic accuracy of ultrasound and ultrasound + shear wave elastography were also assessed.

RESULTS

From a total of 567 breast lesions requiring core-needle biopsy, 49 (8.6%) were considered as non-mass lesions. Based on histopathologic reports, 32 patients (65.3%) had non-high-risk benign lesions, five (10.2%) had high-risk benign lesions, five (10.2%) had ductal carcinoma in situ, and seven (14.3%) had invasive carcinoma. There was no significant difference in patients' age and palpability between benign and malignant lesions (p = 0.16 and p = 0.12, respectively). Mean elasticity values and Breast Imaging-Reporting and Data System categories were significantly higher among malignant lesions compared with benign non-mass lesions (both p < 0.001). Furthermore, the addition of shear wave elastography to grayscale ultrasound increased the specificity, positive predictive value, and diagnostic accuracy.

CONCLUSION

The complementary use of shear wave elastography with conventional ultrasound might help in the differentiation of non-mass breast lesions and has the potential to decrease the frequency of unnecessary biopsies performed for benign non-mass lesions.

MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review

BACKGROUND AND OBJECTIVE

This paper aims to overview multidimensional mining algorithms in relation to Magnetic Resonance Imaging (MRI) radiogenomics for computer aided detection and diagnosis of breast tumours. The work also aims to address a new problem in radiogenomics mining: how to combine structural radiomics information with non-structural genomics information for improving the accuracy and efficacy of Neoadjuvant Chemotherapy (NAC).

METHODS

This requires the automated extraction of parameters from non-structural breast radiomics data, and finding feature vectors with diagnostic value, which then are combined with genomics data. In order to address the problem of weakly labelled tumour images, a Generative Adiversarial Networks (GAN) based deep learning strategy is proposed for the classification of tumour types; this has significant potential for providing accurate real-time identification of tumorous regions from MRI scans. In order to efficiently integrate in a deep learning framework different features from radiogenomics datasets at multiple spatio-temporal resolutions, pyramid structured and multi-scale densely connected U-Nets are proposed. A bidirectional gated recurrent unit (BiGRU) combined with an attention based deep learning approach is also proposed.

RESULTS

The aim is to accurately predict NAC responses by combining imaging and genomic datasets. The approaches discussed incorporate some of the latest developments in of current signal processing and artificial intelligence and have significant potential in advancing and provide a development platform for future cutting-edge biomedical radiogenomics analysis.

CONCLUSIONS

The association of genotypic and phenotypic features is at the core of the emergent field of Precision Medicine. It makes use of advances in biomedical big data analysis, which enables the correlation between disease-associated phenotypic characteristics, genetics polymorphism and gene activation to be revealed.

A New Practical Decision Rule to Better Differentiate BI-RADS 3 or 4 Breast Masses on Breast Ultrasound

OBJECTIVES

The BI-RADS classification provides a standardized way to describe ultrasound findings in breast cancer diagnostics. However, there is little information regarding which BI-RADS descriptors are most strongly associated with malignancy, to better distinguish BI-RADS 3 (follow-up imaging) and 4 (diagnostic biopsy) breast masses.

METHODS

Patients were recruited as part of an international, multicenter trial (NCT02638935). The trial enrolled 1294 women (6 excluded) categorized as BI-RADS 3 or 4 upon routine B-mode ultrasound examination. Ultrasound images were evaluated by three expert physicians according to BI-RADS. All patients underwent histopathological confirmation (reference standard). We performed univariate and multivariate analyses (chi-square test, logistic regression, and Krippendorff's alpha).

RESULTS

Histopathologic evaluation showed malignancy in 368 of 1288 masses (28.6%). Upon performing multivariate analysis, the following descriptors were significantly associated with malignancy (P < .05): age ≥50 years (OR 8.99), non-circumscribed indistinct (OR 4.05) and microlobulated margin (OR 2.95), nonparallel orientation (OR 2.69), and calcification (OR 2.64). A clinical decision rule informed by these results demonstrated a 97% sensitivity and missed fewer cancers compared to three physician experts (range of sensitivity 79-95%) and a previous decision rule (sensitivity 59%). Specificity was 44% versus 22-83%, respectively. The inter-reader reliability of the BI-RADS descriptors and of the final BI-RADS score was fair-moderate.

CONCLUSIONS

A patient should undergo a diagnostic biopsy (BI-RADS 4) instead of follow-up imaging (BI-RADS 3) if the patient is 50 years or older or exhibits at least one of the following features: calcification, nonparallel orientation of mass, non-circumscribed margin, or posterior shadowing.

Imaging and pathological discordance amongst the plethora of breast lesions in breast biopsies

INTRODUCTION

Imaging-guided breast tissue biopsy has become an acceptable alternative to open surgical biopsy for nonpalpable breast lesions. Discussion of abnormal results of the correlation between imaging and pathological findings can be very challenging as it can assist in decision-making with regard to the further treatment options by arriving at a comprehensive diagnosis.

MATERIALS AND METHODS

This was a retrospective study. Radiological data from imaging-guided breast biopsies of 500 patients during a 6-year period was collected and classified by a specialist radiologist as per the BI-RADS format. Histopathology reports were studied and discordance analyzed.

RESULTS

A total of 500 cases were reviewed. Approximately 33% (168) cases fell into the BI-RADS 3 category, 24.4% (122) into the BI-RADS 4, and 37% (187) into BI-RADS 5 categories. Approximately 50% (n = 250) cases were benign, 2.6% (13) belonged to the high-risk category, and 47.4% (237) were malignant. The number of discordant cases was 12 (2.4%), mostly due to technical factors. Sensitivity of biopsies to detect malignancy was 85%, specificity was 96%, and accuracy of biopsy in diagnosing cancer was 90%.

DISCUSSION

The "triple assessment" is the most sensitive method for detecting early breast cancer. An effective communication pathway must be established between a clinician, radiologist, and pathologist for surgical excision in discordance as it carries a high prevalence of carcinoma in these lesions.

CONCLUSION

In discordant cases, either due to abnormal results of imaging or of abnormal pathological findings, the final decision is based on two concordant findings, out of the three parameters. This involves a multidisciplinary breast conference and an active participation by the pathologist.

Color-map virtual touch tissue imaging (CMV) combined with BI-RADS for the diagnosis of breast lesions

OBJECTIVE

To investigate the importance of color-map virtual touch tissue imaging (CMV) in assisting Breast Imaging Reporting and Data Systems (BI-RADS) in diagnosing malignant breast lesions.

METHODS

A dataset included 134 patients and 146 breast lesions was assembled. All patients underwent biopsy or surgical excision of breast lesions, and pathological results were obtained. All patients with breast lesions also underwent conventional ultrasound (US) and CMV. Each lesion was assigned a CMV score based on the color pattern of the lesion and surrounding breast tissue and a BI-RADS classification rating based on US characteristics. We compared the diagnostic performance of using BI-RADS and CMV separately and their combination.

RESULTS

BI-RADS (odds ratio [OR]: 3.665; 95% confidence interval [CI]: 2.147, 6.258) and CMV (OR: 6.616; 95% CI: 2.272, 19.270) were independent predictors of breast malignancy (all P < 0.05). The area under the receiver operating characteristic curves (AUC) for either CMV or BI-RADS alone was inferior to that of the combination (0.877 vs. 0.962; 0.938 vs. 0.962; all P < 0.05).

CONCLUSIONS

The performance of BI-RADS in diagnosing breast lesions is significantly improved by combining CMV. Therefore, we recommend CMV as an adjunct to BI-RADS.

A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value

Objectives

To investigate the diagnostic performance of the Kaiser score and apparent diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions at dynamic contrast-enhanced (DCE) MRI.

Methods

This was a single-institution retrospective study of patients who underwent breast MRI from March 2020 to June 2021. All image data were acquired with a 3-T MRI system. Kaiser score of each lesion was assigned by an experienced breast radiologist. Kaiser score+ was determined by combining ADC and Kaiser score. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of Kaiser score+, Kaiser score, and ADC. The area under the curve (AUC) values were calculated and compared by using the Delong test. The differences in sensitivity and specificity between different indicators were determined by the McNemar test.

Results

The study involved 243 women (mean age, 43.1 years; age range, 18-67 years) with 268 MR BI-RADS 4 lesions. Overall diagnostic performance for Kaiser score (AUC, 0.902) was significantly higher than for ADC (AUC, 0.81; p = 0.004). There were no significant differences in AUCs between Kaiser score and Kaiser score+ (p = 0.134). The Kaiser score was superior to ADC in avoiding unnecessary biopsies (p < 0.001). Compared with the Kaiser score alone, the specificity of Kaiser score+ increased by 7.82%, however, at the price of a lower sensitivity.

Conclusion

For MR BI-RADS category 4 breast lesions, the Kaiser score was superior to ADC mapping regarding the potential to avoid unnecessary biopsies. However, the combination of both indicators did not significantly contribute to breast cancer diagnosis of this subgroup.