Characterization Of Breast Lesions Research Articles

Diagnostic Performance of Kaiser Score for Characterization of Breast Lesions on Modified Abbreviated Breast MRI and Comparison with Full-Protocol Breast MRI.

Background: This study aimed to evaluate the diagnostic performance of the Kaiser score (KS) on the modified abbreviated breast magnetic resonance imaging (AB-MRI) protocol for characterizing breast lesions by comparing it with full-protocol MRI (FP-MRI), using the histological data as the reference standard. Methods: Breast MRIs detecting histologically verified contrast-enhancing breast lesions were evaluated retrospectively. A modified AB-MRI protocol was created from the standard FP-MRI, which comprised axial fat-suppressed T2-weighted imaging (T2WI), pre-contrast T1-weighted imaging (T1WI), and first, second, and fourth post-contrast phases. Two radiologists reviewed both protocols, recording the KS for each detected lesion. Sensitivity, specificity, and positive and negative predictive values, as well as accuracy, were calculated for each protocol. Receiver operating characteristic (ROC) analysis was performed to determine the diagnostic performance of the modified AB-MRI compared to the FP-MRI. Results: In total, 154 patients with 158 histopathologically proven lesions (107 malignant, 51 benign) were included. For the diagnostic performance of the KS for modified AB-MRI and FP-MRI, the sensitivity was 96.3% vs. 98.1%, the specificity was 78.4% vs. 74.5%, PPV was 90.4% vs. 89%, NPV was 90.9% vs. 95%, and the diagnostic accuracy was 90.5% vs. 90.5%. The area under the curve (AUC) obtained from the ROC curve analysis was 0.873 and 0.863 for modified AB-MRI and FP-MRI for reader 1, respectively, and 0.859 and 0.878 for modified AB-MRI and FP-MRI for reader 2, respectively, (p < 0.001). Conclusions: Our modified AB-MRI protocol revealed comparable results in terms of the diagnostic value of the KS in characterizing breast lesions compared to FP-MRI and reduced both scanning and interpretation time.

External validation of multiparametric magnetic resonance imaging-based decision rules for characterizing breast lesions and comparison to Kaiser score and breast imaging reporting and data system (BI-RADS) category.

Breast imaging reporting and data system (BI-RADS) provides standard descriptors but not detailed decision rules for characterizing breast lesions. Diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) are also not incorporated in the BI-RADS. Several multiparametric magnetic resonance imaging (mpMRI)-based decision rules have been developed to differentiate breast lesions, but lack external validation. This study aims to externally validate several mpMRI-based decision rules for characterizing breast lesions and compare them with Kaiser score and BI-RADS category. There were 206 patients with 218 pathology-proven breast lesions (99 malignancies) included in this retrospective study from January 2018 to May 2018. Two radiologists blinded to pathology evaluated breast lesions according to the three mpMRI-based decision rules (Kim, Istomin, Zhong) and Kaiser score. BI-RADS category was extracted from radiology reports and also analysed. The diagnostic performances of the four decision rules and BI-RADS category were calculated and compared for different lesion types [mass and non-mass enhancement (NME)] and size (≤10 and >10 mm). The unnecessary biopsy rates for BI-RADS 4 lesions were calculated by the four decision rules. The three mpMRI-based decision rules showed area under the curve (AUC) of 0.81-0.87 for all lesions, 0.86-0.92 for mass lesions, 0.68-0.82 for NME, and 0.68-0.87 for lesion size ≤10 mm, 0.82-0.87 for lesion size >10 mm. Kaiser score showed the highest diagnostic performance for all subgroups except for lesion size ≤10 mm. No significant differences were found in AUC between Kaiser score and BI-RADS category. The mpMRI-based decision rules showed high sensitivity of 100% in all subgroups at the expense of low specificity (range, 2.9-41.2%). In contrast, Kaiser score demonstrated a significantly higher specificity of 73.5-92.9% than the three mpMRI-based decision rules at the cost of a decreased sensitivity (range, 60.0-93.6%) in different subgroups. The unnecessary biopsy rates for BI-RADS 4 lesions were 9.8% (Istomin), 12.2% (Zhong), 14.6% (Kim) and 70.7% (Kaiser score), respectively. The mpMRI-based decision rules showed high sensitivity but low specificity for characterizing breast lesions, and their diagnostic efficiencies were inferior to Kaiser score and BI-RADS category.

The Role of Diffusion-Weighted Magnetic Resonance Imaging in Differentiating Benign and Malignant Breast Lesions: A Comprehensive Review

Background: Breast magnetic resonance imaging (MRI) has emerged as a vital tool for detecting and characterizing breast lesions, particularly in differentiating benign from malignant tumors. This review explores the role of diffusion-weighted imaging (DWI) in breast MRI, highlighting its advantages over conventional imaging techniques. Methods: A systematic literature search was conducted across multiple databases, including PubMed and Scopus, to gather studies assessing DWI's sensitivity and specificity in breast cancer detection. Results: The findings indicate that DWI provides high diagnostic accuracy, with a pooled sensitivity of 86.5% and specificity of 83.5%, comparable to dynamic contrast-enhanced MRI (DCE-MRI). Notably, utilizing apparent diffusion coefficient (ADC) thresholds can minimize unnecessary biopsies without compromising sensitivity. However, challenges remain in achieving standardization in DWI protocols, particularly regarding b-value selection and imaging techniques, which impact ADC accuracy. Advanced methodologies such as intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) have shown promise in enhancing diagnostic capabilities by providing deeper insights into tissue microstructure. Conclusions: This review underscores the importance of ongoing standardization efforts and collaboration among institutions to improve the reliability and applicability of DWI in clinical settings. In conclusion, DWI represents a transformative approach to breast cancer management, allowing for enhanced differentiation of lesions while reducing the need for invasive procedures. Further research and standardization are essential to optimize its use in routine clinical practice.

Diagnostic accuracy of ultrasonography and mammography of breast lesions

Introduction: Breast cancer is the most common malignant tumors in women, with over one million cases diagnosed globally each year, resulting in approximately 410,000 deaths. Early detection and treatment significantly reduce the risk of mortality. Ultrasound and mammography are widely used non-invasive, cost-effective imaging techniques for early breast cancer diagnosis. This study aims to identify the characteristics of breast lesions on ultrasound and mammography and correlate them with histological findings. Method: This was a retrospective study conducted in Patan Hospital, with the data set from January 2019 to December 2022. All patient’s data were collected regarding findings of ultrasound, mammography, BIRADS from radiological department and correlated with histopathological diagnosis. All findings were compared for the sensitivity and specificity of mammography with ultrasonography in diagnosing breast lesions. Result: A total of 137 cases were found to have histopathology diagnosed benign and malignant breast lesions. Ultrasonography showed 91.58% sensitivity, 57.14% specificity, 82.86% positive predictive value, 75.00% negative predictive value, and 81.02% diagnostic accuracy. Mammography exhibited 92.63% sensitivity, 61.90% specificity, 84.62% positive predictive value, 78.79% negative predictive value, and 83.62% diagnostic accuracy. When combining mammography and ultrasonography, the results showed 93.63% sensitivity, 42.86% specificity, 78.76% positive predictive value, 75% negative predictive value, and 78.10% diagnostic accuracy. Conclusion: Ultrasound and mammography are both highly sensitive in detecting benign and malignant breast lesions. While mammography has a slightly higher overall accuracy in diagnosis, it is recommended to complement ultrasonography with mammography for a more comprehensive detection of breast lesions.

The Value of Shear Wave Sono Elastography in Characterization of Breast Lesions in Correlation with Histopathology

The Value of Shear Wave Sono Elastography in Characterization of Breast Lesions in Correlation with Histopathology

Enhancing detection of high-level axillary lymph node metastasis after neoadjuvant therapy in breast cancer patients with nodal involvement: a combined approach of axilla ultrasound and breast elastography.

To develop a combined approach using shear wave elastography (SWE) and conventional ultrasound (US) to determine the extent of positive axillary lymph nodes (LNs) following neoadjuvant therapy (NAT) in breast cancer patients with nodal involvement. This prospective, multicenter study was registered on the Chinese Clinical Trial Registry (ChiCTR2400085035). From October 2018 to February 2024, a total of 303 breast cancer patients with biopsy-proven positive LN were enrolled. The conventional US features of axillary LNs and SWE characteristics of breast lesions after NAT were analyzed. The diagnostic performances of axilla US, breast SWE, and their combination in detecting residual metastasis in axillary level III after NAT were assessed. Pathologically positive LN(s) in axilla level III were detected in 13.75% of cases following NAT. The kappa value for the axilla level with positive LN confirmed by surgical pathology and detected by US is 0.39 (p < 0.001). The AUC of conventional axilla US to determine the status of axilla level III LNs after NAT was 0.67, with a sensitivity of 51.52%, a specificity of 74.36%. The breast SWE displayed moderate performance for detecting residual metastasis in axilla level III following NAT, with an AUC of 0.79, sensitivity of 84.85%, and specificity of 74.36%. Compared to axilla US and breast SWE alone, the combination of axilla US with breast SWE achieved a stronger discriminatory ability (AUC, 0.86 vs 0.67 vs 0.79, p < 0.05, Delong's test) and precise calibration (X2 = 13.90, p = 0.085, HL test), with an improved sensitivity of 93.94% and a comparable specificity of 75.64%%. SWE outperformed conventional US in identifying the axilla levels with nodal metastasis following NAT in patients with initially diagnosed positive axilla. Furthermore, combining breast SWE with axilla US showed good diagnostic performance for detecting residual metastasis in axilla level III after NAT.

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.

Comparison between Digital Breast Tomosynthesis and Digital Mammography in the Assessment of Breast Cancer across Dense and Nondense Breast

Abstract Background Women's breast cancer is considered a serious public health issue. throughout the world. For asymptomatic women, digital mammography is the primary imaging modality used for breast cancer screening. It also has been shown to reduce breast cancer mortality. Digital breast tomosynthesis provides better detection of the abnormalities especially in females with dense breasts Aim to evaluate the impact of digital breast tomosynthesis (DBT) in comparison to digital mammography (DM) in improving the detection and characterization of breast cancer across the dense and non-dense breasts (according to BI-RADS Atlas) in correlation with ultrasound and histopathology. Material &amp; Method This study included 100 female patients according to our inclusion criteria. All patients performed full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) were classified according to breast density. To establish a correlation between mammographic findings and cancer diagnosis, high-resolution ultrasound (HHUS) and DBT were used following FFDM as the primary imaging modality. The cancer diagnosis was subsequently confirmed by histopathology. Results The current study was based on histopathology as a reference standard, This was correlated with the final diagnosis; the diagnostic indices of mammography at the whole group were a sensitivity of 73.9%, a specificity of 74.3%, a positive predictive value (PPV) 84.2%, a negative predictive value (NPV) of 60.5%, and a diagnostic accuracy of 74%. Diagnostic indices of DBT were a sensitivity of 100%, a specificity of 88.6%, a PPV of 94.2%, an NPV of 100%, and a diagnostic accuracy of 96%. In patients with dense breasts Radiology ACR (c and d), 78% of cases had changed their BI-RADS scoring with the addition of tomosynthesis. However, in 54% of individuals with non-dense breast ACR (a and b), the addition of DBT to FFDM changed the BIRADS score. Our results showed that 100% of lesions detected by tomosynthesis were malignant tumors. Conclusion We conclude that DBT exceeded Mammography in terms of sensitivity, specificity, and diagnostic accuracy at all breast densities, particularly dense breasts. This is because DBT enables more accurate identification and characterization of breast lesions, which reduces the number of recalled cases and negative biopsies.

Role of Diffusion Weighted Imaging with Background Body Signal Suppression (DWIBS) in Evaluation of Breast Masses and Correlation with Histopathological Findings

Abstract Background Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is an important tool in breast imaging. Yet, it has two major limitations which are represented by its specificity and the use of contrast material. Diffusion weighted imaging (DWI) provides important functional information without the need for contrast material. A newly introduced Diffusion weighted imaging with back-ground 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. Objective To assess the role of DWIBS sequence for evaluating breast masses and to compare DWIBS results with DCE-MRI in correlation with histopathological findings for intermediate, suspicious and malignant imaging results. Methods The study was conducted during the period from December 2020 t&amp; January 2023. Thirty five patients were included in the study, referred from Ain Shams university hospitals sonomammography clinic to Ain Shams university hospitals MRI unit for further MRI assessment of breast masses (BIRADS 3, BIRADS 4&amp; BIRADS 5), probably benign, suspicious and malignant- lesions on sonomammography 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 &amp;lt;1.2 x 10-3 mm2/s were considered suspicious for malignancy. The results were then compared with the histological findings. Results 35 female patients had 39 breast masses included in our study. By DCE-MRI, 8 (20.5%) of masses were categorized as benign and 31(79.5%) of masses were categorized as malignant. By DWIBS sequence, 7 (17.9%) of masses were categorized as benign and 32 (82.1%) of masses were categorized as malignant. By histopathology, 14 (35.9%) of masses were benign and 25 (64.1%) were malignant. DCE-MRI obtained accuracy, sensitivity, specificity, PPV and NPV values of 84.6, 100, 57.1, 80.6, 100 %, respectively. DWIBS sequences obtained accuracy, sensitivity, specificity, PPV and NPV values of 82.1, 100, 50, 78.1, 100 %, respectively. Conclusions DWIBS can be added to DCE breast MRI, to improve DCE-MRI sensitivity and specificity. It can as well be used instead of DCE -MRI sequences in certain circumstances as in cases of renal impairment, but with some limitations as DWIBS cannot assess the extent of the malignant process.

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.