Characterization Of Breast Lesions Research Articles

Machine learning-based discrimination of benign and malignant breast lesions on US: The contribution of shear-wave elastography

PurposeTo build and validate a combined radiomics and machine learning (ML) approach using B-mode US and SWE images to differentiate benign from malignant solid breast lesions (BLs) and compare its performance with that of an expert radiologist. MethodsPatients with at least one BI-RADS 2–6 BL who performed breast US integrated with SWE were retrospectively included. B-mode US and SWE images were manually segmented to extract radiomics features. A multi-step feature selection process was performed and a predictive model built using the Logistic Regression algorithm. The diagnostic accuracy was evaluated with the AUC and Matthews Correlation Coefficient (MCC) metrics. The performance of the ML classifier was compared to that of an expert radiologist. Results427 Bls were included and divided into a training (286 BLs, of which 127 benign and 159 malignant) and a test set (141 BLs, of which 59 benign and 82 malignant). Of 1098 features extracted from B-mode US and SWE images, 13 were finally selected. The ML classifier showed an AUC of 0.768 and 0.746, and an MCC of 0.403 and 0.423 in the training and test sets, respectively. The performance was higher than that of the expert radiologist assessing only B-mode US images, but significantly lower when SWE images were also provided. ConclusionA ML approach based on B-mode US and SWE images may represent a potential tool in the characterization of BLs. SWE still gives its most relevant contribution in the clinical setting rather than included in a radiomics pipeline.

Does Contrast-Enhanced Mammography Outperform Digital Breast Tomosynthesis for Detection and Characterization of Breast Lesions or Vice Versa?

Does Contrast-Enhanced Mammography Outperform Digital Breast Tomosynthesis for Detection and Characterization of Breast Lesions or Vice Versa?

EVALUATION OF FOCAL BREAST LESIONS USING ULTRASOUND ELASTOGRAPHY

Breast lesions pose a significant diagnostic challenge, necessitating advanced imaging techniques for accurate characterization. Ultrasound elastography, including strain and shear wave methods, offers promising diagnostic capabilities for distinguishing between benign and malignant breast lesions. Objective: This study aimed to evaluate the effectiveness of ultrasound elastography in characterizing focal breast lesions and to compare the diagnostic performance of strain and shear wave elastography techniques. Methods: A prospective observational study was conducted, enrolling 200 women aged 18 and older with focal breast lesions detected on conventional ultrasound. Participants underwent both strain and shear wave elastography. Lesion characteristics, including stiffness scores and echogenicity, were documented. Histopathological analysis was performed on biopsied lesions to confirm diagnoses. Diagnostic accuracy metrics were calculated for both elastography techniques, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The average age of participants was 52 ± 10 years. Most lesions were solid (70%), with an average size of 22 mm. Shear wave elastography exhibited higher stiffness scores (average 4.5) than strain elastography (average 3.2). Diagnostic accuracy for benign lesions showed a sensitivity of 85% and specificity of 90% with elastography. For malignant lesions, sensitivity was 90% and specificity was 85%. The ROC curve analysis indicated the superior performance of shear wave elastography with an AUC of 0.85 compared to 0.80 for strain elastography. Conclusion: Ultrasound elastography, particularly shear wave elastography, demonstrates high diagnostic accuracy in characterizing breast lesions, providing valuable support in clinical decision-making.

Breast multiparametric ultrasound: a single-center experience.

To evaluate the role of multiparametric ultrasound (mpUS) in the characterization of focal breast lesions (FBLs). This prospective study enrolled patients undergoing multiparametric breast ultrasound for FBLs. An experienced breast radiologist evaluated the following ultrasound features: US BI-RADS category, vascularization pattern (internal, vessels in rim and combined) and presence of penetrating vessels with each Doppler method (Color-Doppler, Power-Doppler, Microvascular imaging), strain ratio (SR) and Tsukuba score (TS) with Strain Elastography (SE), Emax, Emean, Emin and Eratio with 2D-shear wave elastography (2D-SWE). Core biopsy for all BI-RADS 4-5 FBLs and 24-month follow-up for all BI-RADS 2-3 FBLs were considered for standard of reference. The diagnostic performance was assessed with the area under curve (AUCs) and cut-off values were determined according to the Youden's index. A total of 139 FBLs were included with 75/139 (53.9%) benign and 64/139 (46.1%) malignant FBLs. Internal vascularization patterns (p < 0.001), penetrating vessels (p < 0.001), TS 4-5 (p < 0.001) and all 2D-SWE parameters (p < 0.001) were significantly different between benign and malignant FBLs. The BI-RADS score provided an AUC of 0.876 (95% CI 0.810-0.926) for the diagnosis of malignant FBLs. Among the 2D-SWE measurements, an excellent diagnostic performance was observed for Emax with an AUC of 0.915 (95% CI 0.856-0.956) and Emean of 0.908 (95% CI 0.847-0.951). Optimal cutoff for the diagnosis of malignant FBLs were US BI-RADS > 3, Strain Ratio > 2.52, Tsukuba Score > 3, Emax > 82.6kPa, Emean > 66.0kPa, Emin > 54.4kPa and Eratio > 330.8. Multiparametric ultrasound, particularly SWE, can improve specificity in the characterization of FBLs.

Application of the Kaiser score on contrast-enhanced mammography in the differential diagnosis of breast lesions: comparison with breast magnetic resonance imaging.

The Kaiser score (KS) as a clinical decision rule has been proven capable of enhancing the diagnostic efficiency for suspicious breast lesions and obviating unnecessary benign biopsies. However, the consistency of KS in contrast-enhanced mammography (CEM-KS) and KS on magnetic resonance imaging (MRI-KS) is still unclear. This study aimed to evaluate and compare the diagnostic efficacy and agreement of CEM-KS and MRI-KS for suspicious breast lesions. This retrospective study included 207 patients from April 2019 to June 2022. The radiologists assigned a diagnostic category to all lesions using the Breast Imaging Reporting and Data System (BI-RADS). Subsequently, they were asked to assign a final diagnostic category for each lesion according to the KS. The diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC). The agreement in terms of the kinetic curve and the KS categories for CEM and MRI were evaluated via the Cohen kappa coefficient. The AUC was higher for the CEM-KS category assignment than for the CEM-BI-RADS category assignment (0.856 vs. 0.776; P=0.047). The AUC was higher for MRI-KS than for MRI-BI-RADS (0.841 vs. 0.752; P =0.015). The AUC of CEM-KS was not significantly different from that of MRI-KS (0.856 vs. 0.841; P=0.538). The difference between the AUCs for CEM-BI-RADS and MRI-BI-RADS was not statistically significant (0.776 vs. 0.752; P=0.400). The kappa agreement for the characterization of suspicious breast lesions using CEM-KS and MRI-KS was 0.885. The KS substantially improved the diagnostic performance of suspicious breast lesions, not only in MRI but also in CEM. CEM-KS and MRI-KS showed similar diagnostic performance and almost perfect agreement for the characterization of suspicious breast lesions. Therefore, CEM holds promise as an alternative when breast MRI is not available or contraindicated.

The Role of Diffusion-Weighted Imaging in Characterizing Benign and Malignant Breast Lesions: A Retrospective Study.

Introduction Diffusion-weighted imaging (DWI) is a promising magnetic resonance imaging (MRI) technique for differentiating between benign and malignant breast lesions. This study set out to assess the diagnostic utility of DWI and apparent diffusion coefficient (ADC) values in the characterization of breast lesions. Materials and methods A retrospective analysis comprised 30 patients with breast lesions who had breast MRI with DWI. The histopathological findings, ADC readings, and conventional MRI features were all analyzed. The receiver operating characteristic (ROC) curve analysis method was utilized to assess the diagnostic accuracy of DWI. Results Out of the 30 lesions, 22 (73.3%) were benign and eight (26.7%) were malignant. Malignant lesions exhibited significantly lower ADC values (p < 0.001) compared to benign lesions. An ADC cutoff value of 1.1 × 10-3mm2/s was optimal for differentiating benign from malignant lesions, yielding 90.81% sensitivity, 91.51% specificity, and 91.5% accuracy. Conclusion Combining DWI with quantitative ADC analysis is a helpful, non-invasive method for the characterization of breast lesions. It shows excellent diagnostic accuracy in identifying benign and malignant lesions, which may cut down on pointless biopsies and help with patient management.

Value of digital breast tomosynthesis in characterization of breast lesions in dense breast

BackgroundPatients with dense breasts have a higher risk factor for developing breast cancer. Digital mammography (DM) exhibits a reduced sensitivity in dense breast tissue as a result of overlapping fibro-glandular tissue. Digital breast tomosynthesis (DBT) solves this problem, so allows better characterization of masses, asymmetries, and parenchymal distortion. It is useful in screening studies for early detection of breast lesions by accurate detection and description of their margins, shape, and extent as well as surrounding structures.ObjectiveWe aimed to evaluate the added value of DBT in characterizing of different breast lesions in dense breasts compared to DM.MethodsA Prospective Cohort study involved ninety female cases having dense breast parenchyma (ACR C & ACR D) having breast complaint lesions detected by DM or ultrasound or recalled from screening units, during the period from June 2021 to Jan 2023.All patient underwent DM and DBT with correlation with the pathological finding or follow up.ResultsAs regards the characterization of breast mass DBT had higher sensitivity, higher specificity (98%, 87% respectively). However, DM showed less sensitivity, less specificity (58%, 80%,) with (p < 0.0001, p = 0.0005 respectively).ConclusionsDBT effectively visualizes overlapping tissue in dense breasts. DBT is important due to its ability to detect breast cancer earlier, characterize lesions accurately, and inspect the margins of masses precisely, along with decreasing recall rates for false-positive results.

Role of diffusion weighted imaging with background body signal suppression (DWIBS) in diagnosis of breast masses and correlation with histopathological findings

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the important tool in breast imaging. However, two major limitations are represented by its specificity and by the injection of contrast material. Diffusion weighted imaging (DWI) provides important functional information without the need for contrast material. A newly introduced diffusion weighted imaging with background suppression (DWIBS) sequence is an accurate and rapid tool for the identification and characterization of breast lesions, with its short examination time, high lesion-to-background contrast and lack of need for intravenous contrast agents.ObjectiveTo assess the role of DWIBS sequence in the evaluation of indeterminate and suspicious breast masses and to compare its accuracy with DCE-MRI in correlation with histopathological findings.MethodsThirty-five patients were included in the study, referred from sono-mammography clinic to MRI unit for further MRI assessment of probably benign, suspicious and malignant looking breast masses (BIRADS 3, BIRADS 4 & BIRADS 5) on sono-mammography imaging results. MRI breast protocol which included DCE-MRI and DWIBS sequences were obtained for characterization and were verified by core needle biopsy or excisional biopsy. The results were statistically analyzed. Sensitivity, specificity, diagnostic accuracy (DA), positive predictive value (PPV) and negative predictive value (NPV) were calculated for DCE-MRI and DWIBS. Apparent diffusion co-efficient (ADC) values were calculated with ADC ≤ 1.2 × 10−3 mm2/s was considered suspicious for malignancy. The results were then compared with the histological findings.ResultsThirty-five female patients had 39 breast masses included in our study. By DCE-MRI, 8 (20.5%) masses were categorized as benign and 31(79.5%) masses were categorized as malignant. By DWIBS sequence, 7 (17.9%) masses were categorized as benign and 32 (82.1%) masses were categorized as malignant. By histopathology, 14 (35.9%) masses were benign and 25 (64.1%) masses were malignant. DCE-MRI obtained accuracy, sensitivity, specificity, PPV and NPV values of 84.6, 100, 57.1, 80.6 and 100%, respectively. DWIBS sequences obtained accuracy, sensitivity, specificity, PPV and NPV values of 82.1, 100, 50, 78.1and 100%, respectively.ConclusionsDWIBS can be added to DCE-MRI, as complementary tool to make radiologist more confident about the diagnosis. It can also be used instead of DCE-MRI sequences in certain circumstances such as in cases of renal impairment.

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study

PurposeTo investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs).Material and methodsTwo expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B).ResultsA total of 202 of 352 (57.4%) FBLs were benign, and 150 of 352 (42.6%) were malignant. The AI-based semi-automated segmentation achieved a success rate of 95.7% for one reviewer and 96% for the other, without significant difference (p = 0.839). A total of 15 (4.3%) and 14 (4%) of 352 semi-automated segmentations were not accepted due to posterior acoustic shadowing at B-Mode US and 13 and 10 of them corresponded to malignant lesions, respectively. In the validation cohort, the characterization made by the expert radiologist yielded values of sensitivity, specificity, PPV and NPV of 0.933, 0.9, 0.857, 0.955, respectively. The ML model obtained values of sensitivity, specificity, PPV and NPV of 0.544, 0.6, 0.416, 0.628, respectively. The combined assessment of radiologists and ML model yielded values of sensitivity, specificity, PPV and NPV of 0.756, 0.928, 0.872, 0.855, respectively.ConclusionAI-based semi-automated segmentation is feasible, allowing an instantaneous and reproducible extraction of US-derived radiomics features of FBLs. The combination of radiomics and US BI-RADS classification led to a potential decrease of unnecessary biopsy but at the expense of a not negligible increase of potentially missed cancers.

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PurposeTo evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. MethodsThis prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. Resultskep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). ConclusionThe inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging

BackgroundTo compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue.MethodsThis prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model.ResultsAlmost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively).ConclusionsQuantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

Comparison of Performance in Diagnosis and Characterization of Breast Lesions: Contrast-Enhanced Mammography Versus Breast Magnetic Resonance Imaging

IntroductionIn contemporary medical practice, magnetic resonance imaging (MRI) is the most sensitive modality for detecting breast cancer. Contrast-enhanced mammography (CEM), a relatively recent technology, represents another contrast-enhanced imaging technique that has the potential to serve as an alternative to breast MRI. Our main goal is to compare the diagnostic accuracy including assessment of sensitivity and specificity of these 2 contrast-enhanced breast imaging methods, CEM and MRI, in the diagnosis and characterization of breast lesions. Material and methodsOur prospective study included patients who were clinically suspected of malignancy and/or had suspicious findings detected by mammography or ultrasound. A total of 116 patients were included, and both CEM and MRI examinations were performed on all patients. All CEM examinations were conducted at our institution, while 56.89% of all MRI examinations were carried out at external centers. While histopathological results were accessible for all malignant lesions, the final diagnosis for 80.5% of benign lesions was established through typical imaging findings and adequate follow-up. ResultsThis study encompassed a total of 219 lesions, with 125 out of 219 (57.07%) malignant lesions and 94 out of 219 (42.92%) benign lesions. The sensitivity and specificity values were 98.40% and 81.91%, respectively, for CEM, and 100% and 75.33%, respectively, for MRI. Moreover, CEM showcased comparable performance to MRI in evaluating women with dense breasts. ConclusionCEM and MRI were compared for breast lesion diagnosis, with MRI showing higher sensitivity and CEM higher specificity; however, the differences were not statistically significant.

Breast masses with rim enhancement on contrast-enhanced mammography: morphological and enhancement features for diagnosis and differentiation of benign and malignant.

To investigate the imaging characteristics and clinicopathological features of rim enhancement of breast masses demonstrated on contrast-enhanced mammography (CEM). 67 cases of breast lesions confirmed by pathology and showing rim enhancement on CEM examinations were analyzed. The lesions were divided into benign and malignant groups, and the morphological and enhanced features were described. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated separately for each morphology descriptor to evaluate the diagnostic ability of each indicator. There were 35 (52.2%) malignant and 32 (47.8%) benign lesions. There are significant differences in the morphological and enhanced features between benign and malignant lesions. 29/35 (82.9%) malignant lesions exhibited irregular shapes, and 31/35 (88.6%) showed indistinct margins. 28/35 (80%) malignant lesions displayed strong enhancement on CEM, while 12/32 (37.5%) benign lesions exhibited weak enhancement (P = 0.001). Malignant lesions showed a higher incidence of unsmooth inner walls than benign lesions (28/35 vs 7/32; P <.001). Lesion margins showed high sensitivity of 88.57% and NPV of 81.8%. The presence of suspicious calcifications had the highest specificity of 100% and PPV of 100%. The diagnostic sensitivity, specificity, PPV, and NPV of the combined parameters were 97.14%, 93.15%, 94.44%, and 96.77%, respectively. The assessment of morphological and enhanced features of breast lesions exhibiting rim enhancement on CEM can improve the differentiation between benign and malignant breast lesions. This article provides a reference for the differential diagnosis of ring enhanced lesions on CEM.

Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results

IntroductionContrast-enhanced mammography (CEM) is used for characterization of breast lesions with increased diagnostic accuracy compared to digital mammography (DM). Artificial intelligence (AI) approaches are emerging with accuracies equal to an average radiologist. However, most studies trained deep learning (DL) models on DM images and there is a paucity in literature for discovering the application of AI using CEM. ObjectivesTo develop and test a DL model that classifies CEM images and produces corresponding highlights of lesions detected. MethodsFully annotated 2006 images of 326 females available from the previously published Categorized Digital Database for Contrast Enhanced Mammography images (CDD-CESM) were used for training. We developed a DL multiview contrast mammography model (MVCM) for classification of CEM low energy and recombined images. An external test set of 288 images of 37 females not included in the training was used for validation. Correlation with histopathological results and follow-up was considered the standard reference. The study protocol was approved by the Institutional Review Board and patient informed consent was obtained. ResultsAssessment was done on an external test set of 37 females (mean age, 51.31 years ± 11.07 [SD]) with AUC-ROC for AI performance 0.936; (95 % CI: 0.898, 0.973; p < 0.001) and the best cut off value for prediction of malignancy using AI score = 0.28. Findings were then correlated with histopathological results and follow up which revealed a sensitivity of 75 %, specificity 96.3 %, total accuracy of 90.1 %, positive predictive value (PPV) 87.1 %, and negative predictive value (NPV) 92 %, p-value (<0.001). Diagnostic indices of radiologists were sensitivity 88.9 %, specificity 92.6 %, total accuracy 91.7 %, PPV 80 %, and NPV 96.2 %, p-value (<0.001). ConclusionA deep learning multiview CEM model was developed and evaluated in a cohort of female participants and showed promising results in detecting breast cancer. This warrants further studies, external training, and validation.

PhiΦBreast New Diagnostic Techniques for Breast Cancer Detection

Background: Today, breast cancer is one of the most aggressive cancers in women and new cases continue to increase worldwide. In order to reduce mortality, we need to detect this life-threatening disease at an earlier stage. For two years, we have conducted a study for the identification and characterization of suspicious breast lesions using a new diagnostic technique applied to ultrasonography and mammography called “PhiΦBreast.” Methods: We have conducted a study for the identification and characterization of suspicious breast lesions using a new diagnostic technique applied to mammography and ultrasonography. Ultrasound systems with high frequency probes was used, 10-15 MHz, and Digital Breast Tomosynthesis (DBT). Identification and characterization of category C4-C5 lesions of the breast with high Predictive Positive PPV value, with a new innovative method called “PhiΦBreast” using the Golden Ratio (Phi, or Φ 1.618...) Fibonacci sequence and a Predictive Algorithm, applied to the ultrasonography and mammography with subsequent deepening with cytological examination using Fine Needle Aspiration Cytology (FNAC), according to evaluation criteria of the Breast Imaging Report Data System (BI-RADS) and the American College of Radiology (ACR). Usefulness of this research and the use of this new diagnostic tecnique is to detect the breast cancer in early stage. In addition to develop a classification model of the histological type identified in the section areas and the percentage of probability in relation between the Golden Ratio (Φ) and Fibonacci sequence. Results: With the use of Golden Ratio and Fibonacci sequence, applied to ultrasonography and mammography, we have experimented and developed a diagnostic map with characteristics of high probability of identifying suspicious lesions at an early stage. We examined 987 women, 55 lesions detected with PhiΦBreast pattern were classified according to BI-RADS descriptors for US-imaging, including morphologic features that had a high predictive value for the malignancy (p &lt;0.001) This innovative diagnostic technique has shown a sensitivity of 95%, a specificity of 97%, a positive predictive value (PPV) of 97%, and negative predictive value (NPV) of 96%. Furthermore, with a Predictive Algorithm associated with malignant cytology after FNAC, we have classified different types of potentially life-threatening cancers for patients. Conclusion: PhiΦBreast could be an important new model diagnostic technique to be applied mammography and ultrasound for detection of malignant lesions of category C4-C5. In diagnostic imaging is fundamental to recognize predictively the characteristics of a potentially aggressive tumor. This new model could represent the cornerstone as an important contribution for early diagnosis of breast cancer.

Carney complex predisposes to breast cancer: prospective study of 50 women.

Carney complex (CNC) is a rare genetic syndrome, mostly due to germline loss-of-function pathogenic variants in PRKAR1A. Carney complex includes pigmented skin lesions, cardiac myxomas, primary pigmented nodular adrenocortical dysplasia, and various breast benign tumors. The present study was designed to describe the characteristics of breast lesions in CNC patients and their association with other manifestations of CNC and PRKAR1A genotype. A 3-year follow-up multicenter French prospective study of CNC patients included 50 women who were analyzed for CNC manifestations and particularly breast lesions, with breast imaging, genotyping, and hormonal settings. Among the 38 women with breast imaging, 14 (39%) had breast lesions, half of them bilateral. Ten women (26%) presented with benign lesions and six with breast carcinomas (16%): one had ductal carcinoma in situ at 54, and five had invasive cancer before 50 years old, whom one with contralateral breast cancer during follow-up. The occurrence of breast cancer was more frequent in women with PRKAR1A pathogenic variant odds ratio = 6.34 (1.63-17.91) than in general population of same age. The mean age at breast cancer diagnosis was 44.7 years old: 17 years younger than in the general population. Breast cancer patients had good prognosis factors. All breast carcinomas occurred in individuals with familial CNC and PRKAR1A pathogenic variants. Loss of heterozygosity at the PRKAR1A locus in the 2 invasive breast carcinomas analyzed suggested a driver role of this tumor suppressor gene. As CNC could predispose to breast carcinoma, an adequate screening strategy and follow-up should be discussed in affected women. ClinicalTrial.gov NCT00668291.

Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography.

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.

Evaluating the features of breast lesions identified by bimodal breast examination: a real-world study.

Several image-based diagnostic methods have been developed to examine the features of breast lesions among women, while the value of combining palpation imaging and ultrasound by a bimodal breast examination system is still unknown. A real-world study was conducted among 424 patients who visited Fujian Maternal and Child Health Hospital and Fujian Obstetrics and Gynecology Hospital, and used the Bimodal Breast Exam (BBE) systems which combines palpation imaging and ultrasound imaging. Among them, 97 patients had additional ultrasound, mammogram, or pathological examination. These patients were used to evaluate the consistency and efficacy of the BBE in interpreting the features of breast lesions as compared to results of ultrasound, mammogram, and pathological examinations. The BBE system detected 1517 lesions with palpation imaging, 1126 lesions with ultrasound examination (950 solid lesions and 176 cysts), and 391 non mass lesions. Among them, 404 patients were diagnosed as benign and 20 were diagnosed as malignant tumor. However, 12, 9 and 4 cases were diagnosed as malignant tumors by ultrasound, mammogram and pathological examination, respectively. Compared with the integrative results of ultrasound, mammogram and pathology, the sensitivity of BBE is 55.6%, and the specificity is 90.9%, with a kappa coefficient of 0.387 (0.110, 0.665), indicating moderate consistency. In clinical practice, BBE can be used to evaluate features of breast lesions with a high specificity. The diagnostic efficacy is comparable to the integrative results of ultrasound, mammography, and pathological examination.

Breast imaging reporting and data system (BI RADS): Why don’t we use it correctly in the region?

Breast Imaging Reporting and Data System (BI RADS) is used for the standardization of radiological reports in breast imaging, created by the American college of radiology (ACR). Standardized reports allow breast lesion characterization, malignancy risk assessment, and facilitate communication between doctors. The system is made for everyday clinical practice with the aim of improving the quality of diagnostics and anticipate the comparisons of results with colleagues from other health institutions, while in Serbia to assist in communication with colleagues from the region. The BI RADS system is composed of lexicon recommendations for structured radiological reporting and further management. Most health institutions in Serbia and surrounding countries use it, but in everyday clinical practice the system is not correctly implemented in all institutions. During daily imaging procedures we see radiological reports coming from different institutions in the region. Some of them clearly show that the system is misunderstood and not implemented correctly. Our aim is to evaluate the use of the BI RADS system in everyday clinical practice in Serbia and the region, to explain why it is not used correctly, and to point out the most common mistakes with the desire to make the system work adequately.

A Force-Matched Approach to Large-Strain Nonlinearity in Elasticity Imaging for Breast Lesion Characterization.

Ultrasound elasticity imaging is a class of ultrasound techniques with applications that include the detection of malignancy in breast lesions. Although elasticity imaging traditionally assumes linear elasticity, the large strain elastic response of soft tissue is known to be nonlinear. This study evaluates the nonlinear response of breast lesions for the characterization of malignancy using force measurement and force-controlled compression during ultrasound imaging. 54 patients were recruited for this study. A custom force-instrumented compression device was used to apply a controlled force during ultrasound imaging. Motion tracking derived strain was averaged over lesion or background ROIs and matched with compression force. The resulting force-matched strain was used for subsequent analysis and curve fitting. Greater median differences between malignant and benign lesions were observed at higher compressional forces (p-value < 0.05 for compressional forces of 2-6N). Of three candidate functions, a power law function produced the best fit to the force-matched strain. A statistically significant difference in the scaling parameter of the power function between malignant and benign lesions was observed (p-value = 0.025). We observed a greater separation in average lesion strain between malignant and benign lesions at large compression forces and demonstrated the characterization of this nonlinear effect using a power law model. Using this model, we were able to differentiate between malignant and benign breast lesions. With further development, the proposed method to utilize the nonlinear elastic response of breast tissue has the potential for improving non-invasive lesion characterization for potential malignancy.