Malignant Breast Lesions Research Articles

Histopathological Characteristics of Breast Lesions in a Tertiary Health Institution in Southwest Nigeria; A 9-year Review

Background: The breast is affected by several diseases, including developmental, inflammatory, and neoplastic. While benign breast neoplasms are usually more common, breast cancer is the most common nonskin cancer and the leading cause of cancer-related deaths in women after lung cancer in the world. Objective: The review aimed to assess the histopathological diagnosis of breast lesions over a period of 9years Methods: This was a retrospective review of eight hundred and fourteen histopathologic cases of breast lesions accessed through laboratory records of all breast lesions cases seen. The demographic data were obtained and the patient’s histology slides were reviewed. The tumors were classified according to the WHO International Classification of breast tumors and Malignant breast tumors were graded according to the Nottingham grading system. Data were analyzed using SPSS version 25. Results: The majority of breast cancer cases were seen in patients below 40 years, malignant cases were predominant in older age groups (40 – 59 years; 57.4%, 60 years and above; 23.3%) and malignancy was significantly associated with age group, p-value < 0.05. The prevailing neoplasm was Benign type (68.3%) while malignant neoplasm accounted for 30.2% and invasive ductal carcinoma (89.1%) was most prevalent. Moreso, the majority were in grade 2; 152 (76.8%) using the Nottigham grading system. However, the commonest benign neoplasm was fibroadenoma (53.7%) followed by fibrocystic change (20.1%). Conclusion: Breast lesions are common in this environment. Benign breast neoplasms are commoner than malignant diseases. One in three of every sub-Saharan woman diagnosed with malignant breast lesion may not survive five years post-diagnosis. The prognosis of breast malignancies in sub-Saharan is poor due to late Presentation, ignorance and poverty.

Histopathological Characteristics of Breast Lesions in a Tertiary Health Institution in Southwest Nigeria: A 9-year Review

Background: The breast is affected by several diseases, including developmental, inflammatory, and neoplastic. While benign breast neoplasms are usually more common, breast cancer is the most common nonskin cancer and the leading cause of cancer-related deaths in women after lung cancer in the world.
 Objective: The review aimed to assess the histopathological diagnosis of breast lesions over a period of 9years
 Methods: This was a retrospective review of eight hundred and fourteen histopathologic cases of breast lesions accessed through laboratory records of all breast lesions cases seen. The demographic data were obtained and the patient’s histology slides were reviewed. The tumors were classified according to the WHO International Classification of breast tumors and Malignant breast tumors were graded according to the Nottingham grading system. Data were analyzed using SPSS version 25.
 Results: The majority of breast cancer cases were seen in patients below 40 years, malignant cases were predominant in older age groups (40 – 59 years; 57.4%, 60 years and above; 23.3%) and malignancy was significantly associated with age group, p-value < 0.05. The prevailing neoplasm was Benign type (68.3%) while malignant neoplasm accounted for 30.2% and invasive ductal carcinoma (89.1%) was most prevalent. Moreso, the majority were in grade 2; 152 (76.8%) using the Nottigham grading system. However, the commonest benign neoplasm was fibroadenoma (53.7%) followed by fibrocystic change (20.1%).
 Conclusion: Breast lesions are common in this environment. Benign breast neoplasms are more commoner than malignant diseases. One in three of every sub-Saharan woman diagnosed with malignant breast lesion may not survive five years post-diagnosis. The prognosis of breast malignancies in sub-Saharan is poor due to late Presentation, ignorance, and poverty.

Implementation of Non-Invasive Quantitative Ultrasound in Clinical Cancer Imaging.

Quantitative ultrasound (QUS) is a non-invasive novel technique that allows treatment response monitoring. Studies have shown that QUS backscatter variables strongly correlate with changes observed microscopically. Increases in cell death result in significant alterations in ultrasound backscatter parameters. In particular, the parameters related to scatterer size and scatterer concentration tend to increase in relation to cell death. The use of QUS in monitoring tumor response has been discussed in several preclinical and clinical studies. Most of the preclinical studies have utilized QUS for evaluating cell death response by differentiating between viable cells and dead cells. In addition, clinical studies have incorporated QUS mostly for tissue characterization, including classifying benign versus malignant breast lesions, as well as responder versus non-responder patients. In this review, we highlight some of the important findings of previous preclinical and clinical studies and expand the applicability and therapeutic benefits of QUS in clinical settings. We summarized some recent clinical research advances in ultrasound-based radiomics analysis for monitoring and predicting treatment response and characterizing benign and malignant breast lesions. We also discuss current challenges, limitations, and future prospects of QUS-radiomics.

Differentiation of benign and malignant breast lesions using diffusion-weighted imaging with a fractional-order calculus model

PurposeTo assess the feasibility of using three diffusion parameters (D, β, and μ) derived from fractional-order calculus (FROC) diffusion model for improving the differentiation between benign and malignant breast lesions. MethodIn this prospective study, 103 patients with breast lesions were enrolled. All subjects underwent diffusion-weighted imaging (DWI) with 12b values. Inter-observer agreement with respect to quantification of parameters by two radiologists was assessed using intraclass coefficient. Conventional apparent diffusion coefficient (ADC) and three FROC model parameters D, β, and μ were compared between the benign lesion and malignant lesion groups using the Mann-Whitney U test. Then, a comprehensive prediction model was created by using binary logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the parameters using histopathological diagnosis as the reference standard. ResultsThe FROC parameters and ADC all exhibited significant differences between benign lesions and malignant lesions (P＜0.001). Among the individual parameters, the sensitivity of μ was higher than ADC (95.92% for μ vs 91.84% for ADC), and the specificity of β was higher than ADC (72.22% for β vs 70.37% for ADC). The combination of ADC and FROC parameters (D and β) generated the largest area under the ROC curve (0.841) when compared with individual parameters, indicating an improved performance for differentiating benign lesions from malignant lesions. ConclusionsThis study demonstrated the feasibility of using the FROC diffusion model to improve the accuracy of identifying malignant breast lesions.

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

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 2cm requires further investigation. 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. 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. The addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules.

Correlation of Mammography, Ultrasound and Sonoelastographic Findings With Histopathological Diagnosis in Breast Lesions.

Introduction Breast masses range from inflammatory and benign to malignant lesions, varying in different age groups and clinical presentations. Breast imaging techniques leading to prompt and specific diagnosis have been a lifesaver for millions around the globe saving them undue mental stress in inflammatory lesions and preventing early death in case of neoplasms. Here, we compare mammography, ultrasonography (US) and ultrasound elastography in the screening and diagnosis of breast lesions and the usefulness of strain ratio as a non-invasive tool in diagnosing breast malignancies. Aims and objectives Determining the characteristics ofbreast lesions on imaging by mammogram, ultrasonography, elastography, calculating strain ratio andcorrelating them with histopathology. To further estimate the advantages and limitations of one modality over the other in the evaluation of breast lesions. Methods The study was done over a duration of 18 months from November 2019 to June 2021 at JSS Medical College and Hospital, Mysuru. In this prospective study, 73 female patients with palpable breast lesions were evaluated using mammography, US and sonoelastography and were co-related with histopathological findings. Results This study has proved that the use of ultrasound elastography has higher sensitivity (91.67% with strain ratio kept at 3) in detecting malignant lesions when compared with x-ray mammography and ultrasonography having a sensitivity of 87.88% and 90.91%, respectively. Our study confirmed that there is a correlation between strain ratio and histopathological findings and that the strain ratio has high sensitivity in diagnosing and differentiating malignant breast lesions. Conclusions Mammography, US and sonoelastography when combined together are helpful in characterization and management of breast lesions. This helps to avoid unnecessary invasive interventions. The specificity of the study in detecting malignant lesions was comparable with that of histopathological analysis.

Radiomic Features Applied to Contrast Enhancement Spectral Mammography: Possibility to Predict Breast Cancer Molecular Subtypes in a Non-Invasive Manner

We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2-G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45-58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy.

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification.

To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions. In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist. The All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets. The proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.

Correlation of Shear-Wave Elastography and Apparent Diffusion Coefficient Values in Breast Cancer and Their Relationship with the Prognostic Factors.

Diffusion-weighted imaging and elastography are widely accepted methods in the evaluation of breast masses, however, there is very limited data comparing the two methods. The apparent diffusion coefficient is a measure of the diffusion of water molecules obtained by diffusion-weighted imaging as a part of breast MRI. Breast elastography is an adjunct to conventional ultrasonography, which provides a noninvasive evaluation of the stiffness of the lesion. Theoretically, increased tissue density and stiffness are related to each other. The purpose of this study is to compare MRI ADC values of the breast masses with quantitative elastography based on ultrasound shear wave measurements and to investigate their possible relation with the prognostic factors and molecular subtypes. We retrospectively evaluated histopathologically proven 147 breast lesions. The molecular classification of malignant lesions was made according to the prognostic factors. Shear wave elastography was measured in kiloPascal (kPa) units which is a quantitative measure of tissue stiffness. DWI was obtained using a 1.5-T MRI system. ADC values were strongly inversely correlated with elasticity (r = -0.662, p &lt; 0.01) according to Pearson Correlation. In our study, the cut-off value of ADC was 1.00 × 10-3 cm2/s to achieve a sensitivity of 84.6% and specificity of 75.4%, and the cut-off value of elasticity was 105.5 kPa to achieve the sensitivity of 96.3% and specificity 76.9% to discriminate between the malignant and benign breast lesions. The status of prognostic factors was not correlated with the ADC values and elasticity. Elasticity and ADC values are correlated. Both cannot predict the status of prognostic factors and differentiate between molecular subtypes.

Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions

The aim of this study was to develop a conventional ultrasound (US) features-based nomogram for the prediction of malignant nonmasslike (NML) breast lesions. Consecutive cases of adult females diagnosed with NML breast lesions via US screening in our center from June 1st, 2017, to April 17th, 2020, were retrospectively enrolled. Candidate variables included age, clinical symptoms, and the image features obtained from the conventional US. Nomograms were developed based on the results of the multiple logistic regression analysis via R language. One thousand bootstraps were used for internal validation. The area under the curve (AUC) and the bias-corrected concordance index (C-index) were calculated. Decision curve analysis (DCA) was also performed for further comparison between the nomogram and the Breast Imaging Reporting and Data System (BI-RADS). The study has not yet been registered. A total of 229 patients were included in the study after exclusion and follow-up. The overall malignant rate of NML breast lesions was 31.0%. Age, clinical symptoms, echo pattern, calcification, orientation, and Adler's classification were selected to generate the nomogram according to the results of the multivariable logistic regression analysis. The bias-corrected C-index and the AUC of our nomogram were 0.790 and 0.828, respectively. The DCA showed that our model had larger net benefits in a range from 0.2 to 0.7 when compared with the BI-RADS. We developed a prediction model using a combination of age, clinical symptoms, echo pattern, calcification, orientation, and Adler's classification for malignant NML breast lesion prediction that yielded adequate discrimination and calibration.

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

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. 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 (0.5-5mm) surrounding the lesion were measured. The sensitivity, specificity, and AUC were calculated and compared by the cutoff values recommended by WFUMB guidelines. 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 >20mm, 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 >20mm. 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. 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.

COMPARISON OF STRAIN ELASTOGRAPHY AND SHEAR WAVE ELASTOGRAPHY FOR DIFFERENTIATING MALIGNANT FROM BENIGN BREAST MASSES

To compare the diagnostic performance of shear wave and strain elastography in distinguishing benign and malignant breast lesions. A prospective study carried out from 20th March 2020 to 20th March 2021 at department of Radiology of Sahiwal, Multan, Lahore. The study included women who were lined for surgical excision or core needle biopsy and were examined using B-mode ultrasound and shear and strain wave elastography. A total of 30 female patients with 40 breast lesions were included in the study. Lesions were categorized on B-mode ultrasound as 3 BI-RADS 3, 18 BI-RADS 4A, 5 BI-RADS 4B, 7 BI-RADS 4C, and 7 BI-RADS 5. After performing a B-mode Ultrasound examination, US elastography was done. A 14-MHz transducer was used to obtain high-resolution images. For comparing the diagnostic performance of both, an area under the ROI curve (AUC) from data sets of shear and strain wave elastography was compared. The McNemar test was used for the comparison of sensitivity and specificity of both data sets. Mean age of the participants was 48.4 years (21-80years). 24 out of 40 lesions were benign while 16 were malignant. B mode US imaging showed the diameter of the lesion ranged from 0.5 to 5.2 cm. There was no significant difference between specificity and sensitivity of elastographic images. 6 breast masses showed discrepant results. After category 4a BI-RADs were selectively downgraded on the bases of cutoff values, AUCs showed improvement. Diagnostic performance of shear wave and strain elastography in distinguishing malignant and benign lesions is the same. Combining both has better diagnostic performance than using each individually.

Study of micronuclei as a potent biomarker in breast cytology aspirates

: Micronuclei scoring can be used as a bio-marker of genotoxic and chromosomal damage. Fine needle aspiration cytology (FNAC) is applied as the primary tool for diagnosis in breast masses because of its ease and rapidity. Micronucleus (MN) scoring is carried out in benign (fibroadenoma) and malignant (infiltrating ductal carcinoma) breast lesions to evaluate the role of Micronuclei as a biomarker in breast carcinomas.: To study Micronuclei (MN) scores as a biomarker on breast cytology aspirated smears. This was a prospective study done for duration of two years in the Department of Pathology, A.C.S Medical College and Hospital, Chennai. The features of micronuclei in 60 breast aspirate smears were studied and compared in benign conditions, proliferative and malignant conditions.The most common diagnosis was of fibroadenoma seen in 38 (63.3%) cases. Adenosis was seen in 10 (16.6%) cases. Usual ductal hyperplasia in 6.6% cases and Invasive ductal carcinomas in 6 (10%) cases. The Average Micronucleus score/1000 cells and the range of micronucleus score was higher in malignancy as compared to benign conditions. : Micronuclei can be used as a biomarker on fine needle aspiration cytology smears of breast lesions. An increase in micronuclei is usually seen in malignant conditions as compared to benign tumours. Attention to features of micronuclei can give a clue to the presence of malignancy.

Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201.

In order to achieve better performance, artificial intelligence is used in breast cancer diagnosis. In this study, we evaluated the efficacy of different fine-tuning strategies of deep transfer learning (DTL) based on the DenseNet201 model to differentiate malignant from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We collected 4260 images of benign lesions and 4140 images of malignant lesions of the breast pertaining to pathologically confirmed cases. The benign and malignant groups was randomly divided into a training set and a testing set at a ratio of 9:1. A DTL model based on the DenseNet201 model was established, and the effectiveness of 4 fine-tuning strategies (S0: strategy 0, S1: strategy; S2: strategy; and S3: strategy) was compared. Additionally, DCE-MRI images of 48 breast lesions were selected to verify the robustness of the model. Ten images were obtained for each lesion. The classification was considered correct if more than 5 images were correctly classified. The metrics for model performance evaluation included accuracy (Ac) in the training and testing sets, precision (Pr), recall rate (Rc), f1 score (f1), and area under the receiver operating characteristic curve (AUROC) in the validation set. The Ac of the 4 fine-tuning strategies reached 100.00% in the training set. The S2 strategy exhibited good convergence in the testing set. The Ac of S2 was 98.01% in the testing set, which was higher than those of S0 (93.10%), S1 (90.45%), and S3 (93.90%). The average classification Pr, Rc, f1, and AUROC of S2 in the validation set were (89.00%, 80.00%, 0.81, and 0.79, respectively) higher than those of S0 (76.00%, 67.00%, 0.69, and 0.65, respectively), S1 (60.00%, 60.00%, 0.60, 0.66, and respectively), and S3 (77.00%, 73.00%, 0.74, 0.72, respectively). The degree of coincidence between S2 and the histopathological method for differentiating between benign and malignant breast lesions was high (κ = 0.749). The S2 strategy can improve the robustness of the DenseNet201 model in relatively small breast DCE-MRI datasets, and this is a reliable method to increase the Ac of discriminating benign from malignant breast lesions on DCE-MRI.

Can clinical evaluation by physiotherapist forms early detection of breast carcinoma?

Introduction: Modern living associated with greater health hazards including cancers. Among which breast carcinoma ranks with higher prevalence in adult women. Physical laboratory investigations and screening. Early identification to a larger extent can influence better outcome and longevity especially for breast cancer subjects. Objectives: As physical therapist frequently involved in treating neck and low back ache as first contact healthcare expert, this research highlights importance of patients with chronic neck pain to be screened for lymph nodes, other related complaints like stiff neck, increasing atrophy, constant continuous pain, most of these could be related to malignancy while these subjects can be referred to physician or oncologist for to be detected and treated with due medical care for an enhanced post onco surgical living standard. Materials and methods: 59-year-old diabetic subject with chronic continuous neck pain with nodules over posterior cervical spine was referred to undergo mammography by physiotherapist through physician. She was diagnosed later with hyper metabolic malignant lesion of left breast and underwent modified left radical mastectomy. Later, she was getting treated for her physical functioning in improving QOL were discussed with due evidence for the period 2020 to 2022. Results: With lesser follow up post mastectomy and early identification by physiotherapists remain key component of this research. Findings of this research can form foundation of onco physiotherapy in early clinical screening and referral to medical expert. Conclusions: If adhered can become novel means of physiotherapy evaluation as red flag and could be included in every patient physical evaluation in future.

Comparison of apparent diffusion coefficient and relative apparent diffusion coefficient values for differential diagnosis of breast lesions

Objectives: The purpose the study was to evaluate the role of diffusion weighted magnetic resonance imaging (DW-MRI) in diagnosis of benign and malignant breast lesions, to calculate a cut-off apparent diffusion coefficient (ADC) value and to explore use of relative ADC (r ADC) for improving sensitivity and specificity of MRI in diagnosis of breast cancer. Methods: This retrospective study based on a cohort of patients who underwent dynamic contrast enhanced (DCE)-MRI having suspicious breast mass by ultrasonography and mammography to whom DWI sequence was added to the routine diagnostic MRI. ADC and r ADC (lesion/normal breast tissue) values of breast masses were calculated. The threshold ADC values used to differentiate benign and malignant lesions were determined using receiver operating characteristic analysis, sensitivity, specificity, positive predictive value and negative predictive value were calculated. Results: Malignant masses had significantly lower ADC (mean: 1.03 ± 0.36 × 10-3 mm2/s) and r ADC (mean: 0.66 ± 0.22 × 10-3 mm2/s) values than those of benign masses with ADC (mean: 1.50 ± 0.56 × 10−3 mm2/s) and r ADC (mean: 0.97 ± 0.31 × 10-3 mm2/s) values, respectively (p = 0.001 for both). The best cut-off value for the lesion ADC was 1.09 × 10-3 mm2/s with a sensitivity of 72.73%, and specificity of 79.17%. The best cut-off value for r ADC was 0.83 with sensitivity of 78.79% and specificity of 70.83%. Conclusions: DWI has high diagnostic value with high sensitivity and specificity differentiating benign and malignant breast lesions. ADC and r ADC values can improve the diagnostic accuracy of differentiating benign and malignant breast lesions.

EVALUATING DIAGNOSTIC VALUE OF MAGNETIC RESONANCE SPECTROSCOPY (MRS) FOR DIAGNOSIS OF MALIGNANT BREAST MASSES

To evaluate diagnostic value of magnetic resonance spectroscopy (MRS) for diagnosis of malignant breast masses. The study included 155 patients with breast lesions at Department of Radiology, CH &amp; ICH Multan from January 2021 to June 2021.1.5 Tesla MR system was used for proton magnetic resonance spectroscopy (1H MRS). Consultant radiologist interpreted MR spectroscopy for distinguishing between benign and malignant lesions using histopathology as a gold standard. SPSS 20.0 was used for data analysis. Among 81 MRS positive cases, 76 had malignant lesions (True Positive) and 05 had no malignant lesion (False Positive) on histopathology. Among 74 MRS negative cases, 7 had malignancy on histopathology (False Negative), while 67 had no malignant lesion (True Negative) on histopathology. It was concluded that MRS has high sensitivity and accuracy for diagnosis of malignant breast lesions.

Diagnostic Value of Superb Microvascular Imaging in Differentiating Benign and Malignant Breast Tumors: A Systematic Review and Meta-Analysis.

We performed a systematic review and meta-analysis of studies that investigated the diagnostic performance of Superb Microvascular Imaging (SMI) in differentiating between benign and malignant breast tumors. Studies published between January 2010 and March 2022 were retrieved by online literature search conducted in PubMed, Embase, Cochrane Library, Web of Science, China Biology Medicine Disc, China National Knowledge Infrastructure, Wanfang, and Vip databases. Pooled sensitivity, specificity, and diagnostic odd ratios were calculated using Stata software 15.0. Heterogeneity among the included studies was assessed using I2 statistic and Q test. Meta-regression and subgroup analyses were conducted to investigate potential sources of heterogeneity. Influence analysis was conducted to determine the robustness of the pooled conclusions. Deeks' funnel plot asymmetry test was performed to assess publication bias. A summary receiver operating characteristic curve (SROC) was constructed. Twenty-three studies involving 2749 breast lesions were included in our meta-analysis. The pooled sensitivity and specificity were 0.80 (95% confidence interval [CI], 0.77-0.84, inconsistency index [I2] = 28.32%) and 0.84 (95% CI, 0.79-0.88, I2 = 89.36%), respectively. The pooled diagnostic odds ratio was 19.95 (95% CI, 14.84-26.82). The area under the SROC (AUC) was 0.85 (95% CI, 0.81-0.87). SMI has a relatively high sensitivity, specificity, and accuracy for differentiating between benign and malignant breast lesions. It represents a promising supplementary technique for the diagnosis of breast neoplasms.

Virtual Biopsy by Using Artificial Intelligence-based Multimodal Modeling of Binational Mammography Data.

Background Computational models based on artificial intelligence (AI) are increasingly used to diagnose malignant breast lesions. However, assessment from radiologic images of the specific pathologic lesion subtypes, as detailed in the results of biopsy procedures, remains a challenge. Purpose To develop an AI-based model to identify breast lesion subtypes with mammograms and linked electronic health records labeled with histopathologic information. Materials and Methods In this retrospective study, 26 569 images were collected in 9234 women who underwent digital mammography to pretrain the algorithms. The training data included individuals who had at least 1 year of clinical and imaging history followed by biopsy-based histopathologic diagnosis from March 2013 to November 2018. A model that combined convolutional neural networks with supervised learning algorithms was independently trained to make breast lesion predictions with data from 2120 women in Israel and 1642 women in the United States. Results were reported using the area under the receiver operating characteristic curve (AUC) with the 95% DeLong approach to estimate CIs. Significance was tested with bootstrapping. Results The Israeli model was validated in 456 women and tested in 441 women (mean age, 51 years ± 11 [SD]). The U.S. model was validated in 350 women and tested in 344 women (mean age, 60 years ± 12). For predicting malignancy in the test sets (consisting of 220 Israeli patient examinations and 126 U.S. patient examinations with ductal carcinoma in situ or invasive cancer), the algorithms obtained an AUC of 0.88 (95% CI: 0.85, 0.91) and 0.80 (95% CI: 0.74, 0.85) for Israeli and U.S. patients, respectively (P = .006). These results may not hold for other cohorts of patients, and generalizability across populations should be further investigated. Conclusion The results offer supporting evidence that artificial intelligence applied to clinical and mammographic images can identify breast lesion subtypes when the data are sufficiently large, which may help assess diagnostic workflow and reduce biopsy sampling errors. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

The Frequency and Causes of Not-Detected Breast Malignancy in Dynamic Contrast-Enhanced MRI.

Breast MR is the most sensitive imaging modality, but there are cases of malignant tumors that are not detected in MR. This study evaluated the frequency and main causes of malignant breast lesions not detected in dynamic contrast-enhanced (DCE) MR. A total of 1707 cases of preoperative breast MR performed between 2020 and 2021 were included. Three radiologists individually reviewed the DCE MRs and found not-detected malignancy cases in the MRs. The final cases were decided through consensus. For the selected cases, images other than DCE MRIs, such as mammography, ultrasounds, diffusion-weighted MRs, and, if possible, contrast-enhanced chest CTs, were analyzed. In the final sample, 12 cases were not detected in DCE MR, and the frequency was 0.7% (12/1707). Six cases were not detected due to known non-enhancing histologic features. In four cases, tumors were located in the breast periphery and showed no enhancement in MR. In the remaining two cases, malignant lesions were not identified due to underlying marked levels of BPE. The frequency of not-detected malignancy in DCE MR is rare. Knowing the causes of each case and correlating it with other imaging modalities could be helpful in the diagnosis of breast malignancy in DCE MR.