Magnetic resonance imaging findings of radiation-induced breast angiosarcoma:A case report

BACKGROUND Breast conservation surgery(BCS)with adjuvant radiotherapy has become a gold standard in the treatment of early-stage breast cancer,significantly reducing the risk of tumor recurrence.However,this treatment is associated with adverse effects,including the rare but aggressive radiation-induced angiosarcoma(RIAS).Despite its rarity and nonspecific initial presentation,RIAS presents a challenging diagnosis,emphasizing the importance of imaging techniques for early detection and accurate diagnosis.CASE SUMMARY We present a case of a 48-year-old post-menopausal woman who developed skin ecchymosis on the right breast seven years after receiving BCS and adjuvant radiotherapy for breast cancer.Initial mammography and ultrasound were inconclusive,showing post-treatment changes but failing to identify the underlying angiosarcoma.Contrast-enhanced breast magnetic resonance imaging(MRI)revealed diffuse skin thickening and nodularity with distinctive enhan-cement kinetics,leading to the diagnosis of RIAS.This case highlights the crucial role of MRI in diagnosing and determining the extent of RIAS,facilitating timely and appropriate surgical intervention.CONCLUSION Breast MRI is crucial for detecting RIAS,especially when mammography and ultrasound are inconclusive.

Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors

Objective To investigate the difference in texture features on diffusion weighted imaging(DWI) images between breast benign and malignant tumors.Methods Patients including 56 with mass-like breast cancer, 16 with breast fibroadenoma, and 4 with intraductal papilloma of breast treated in the Hainan Hospital of Chinese PLA General Hospital were retrospectively enrolled in this study, and allocated to the benign group(20 patients) and the malignant group(56 patients) according to the post-surgically pathological results. Texture analysis was performed on axial DWI images, and five characteristic parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were calculated. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. Regression model was established by using Binary Logistic regression analysis, and receiver operating characteristic curve(ROC) analysis was carried out to evaluate the diagnostic efficiency. Results The texture features ASM, Contrast, Correlation and Entropy showed significant differences between the benign and malignant breast tumor groups(PASM= 0.014, Pcontrast= 0.019, Pcorrelation= 0.010, Pentropy= 0.007). The area under the ROC curve was 0.685, 0.681, 0.754, and 0.683 respectively for the positive texture variables mentioned above, and that for the combined variables(ASM, Contrast, and Entropy) was 0.802 in the model of Logistic regression. Binary Logistic regression analysis demonstrated that ASM, Contrast and Entropy were considered as thespecific imaging variables for the differential diagnosis of breast benign and malignant tumors.Conclusion The texture analysis of DWI may be a simple and effective tool in the differential diagnosis between breast benign and malignant tumors.

Magnetic resonance imaging in breast cancer:A literature review and future perspectives

Early detection and diagnosis of breast cancer are essential for successful treatment. Currently mammography and ultrasound are the basic imaging techniques for the detection and localization of breast tumors. The low sensitivity and specificity of these imaging tools resulted in a demand for new imaging modalities and breast magnetic resonance imaging(MRI) has become increasingly important in the detection and delineation of breast cancer in daily practice. However, the clinical benefits of the use of pre-operative MRI in women with newly diagnosed breast cancer is still a matter of debate. The main additional diagnostic value of MRI relies on specific situations such as detecting multifocal, multicentric or contralateral disease unrecognized on conventional assessment(particularly in patients diagnosed with invasive lobular carcinoma), assessing the response to neoadjuvant chemotherapy, detection of cancer in dense breast tissue, recognition of an occult primary breast cancer in patients presenting with cancer metastasis in axillary lymph nodes, among others. Nevertheless, the development of new MRI technolo-gies such as diffusion-weighted imaging, proton spectroscopy and higher field strength 7.0 T imaging offer a new perspective in providing additional information in breast abnormalities. We conducted an expert literature review on the value of breast MRI in diagnosing and staging breast cancer, as well as the future potentials of new MRI technologies.

How to use magnetic resonance imaging following neoadjuvant chemotherapy in locally advanced breast cancer

Magnetic resonance imaging(MRI) is highly sensitive in identifying residual breast cancer following neoadjuvant chemotherapy(NAC), and consequently is a commonly used imaging modality in locally advanced breast cancer patients. In these patients, tumor response is an important prognostic indicator. However, discrepancies between MRI findings and surgical pathology are well documented. Overestimation of residual disease by MRI may result in greater surgery than is actually required while underestimation may result in insufficient surgery. Thus, it is important to understand when MRI findings are reliable and when they are less accurate. MRI most accurately predicts pathology in triple negative, Her2 positive and hormone receptor negative tumors, especially if they are of a solid imaging phenotype. In these cases, post-NAC MRI is highly reliable for surgical planning. Hormone receptor positive cancers and those demonstrating non mass enhancement show lower concordance with surgical pathology, making surgical guidance more nebulous in these cases. Radiologists and surgeons must assess MRI response to NAC in the context of tumor subtype. Indiscriminate interpretations will prevent MRI from achieving its maximum potential in the pre-operative setting.

Combined molybdenum target X-ray and magnetic resonance imaging examinations improve breast cancer diagnostic efficacy

BACKGROUND Early-stage breast cancer patients often lack specific clinical manifestations,making diagnosis difficult.Molybdenum target X-ray and magnetic resonance imaging(MRI)examinations both have their own advantages.Thus,a combined examination methodology may improve early breast cancer diagnoses.AIM To explore the combined diagnostic efficacy of molybdenum target X-ray and MRI examinations in breast cancer.METHODS Patients diagnosed with breast cancer at our hospital from March 2019 to April 2021 were recruited,as were the same number of patients during the same period with benign breast tumors.Both groups underwent molybdenum target X-ray and MRI examinations,and diagnoses were given based on each exam.The single(i.e.,X-ray or MRI)and combined(i.e.,using both methods)diagnoses were counted,and the MRI-related examination parameters(e.g.,T-wave peak,peak and early enhancement rates,and apparent diffusion coefficient)were compared between the groups.RESULTS In total,63 breast cancer patients and 63 benign breast tumor patients were recruited.MRI detected 53 breast cancer cases and 61 benign breast tumor cases.Molybdenum target X-ray detected 50 breast cancer cases and 60 benign breast tumor cases.The combined methodology detected 61 breast cancer cases and 61 benign breast tumor cases.The sensitivity(96.83%)and accuracy(96.83%)of the combined methodology were higher than single-method MRI(84.13%and 90.48%,respectively)and molybdenum target X-ray(79.37%and 87.30%,respectively)(P<0.05).The combined methodology specificity(96.83%)did not differ from singlemethod MRI(96.83%)or molybdenum target X-ray(95.24%)(P>0.05).The Twave peak(169.43±32.05)and apparent diffusion coefficient(1.01±0.23)were lower in the breast cancer group than in the benign tumor group(228.86±46.51 and 1.41±0.35,respectively).However,the peak enhancement rate(1.08±0.24)and early enhancement rate(1.07±0.26)were significantly higher in the breast cancer group than in the benign tumor group(0.83±0.19 and 0.75±0.19,respectively)(P<0.05).CONCLUSION Combined molybdenum target X-ray and MRI examinations for diagnosing breast cancer improved the diagnostic sensitivity and accuracy,minimizing the missedand misdiagnoses risks and promoting timely treatment intervention.

Breast Tumor Computer-Aided Detection System Based on Magnetic Resonance Imaging Using Convolutional Neural Network

Background:The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue.Early diagnosis of tumors has become the most effective way to prevent breast cancer.Method:For distinguishing between tumor and non-tumor in MRI,a new type of computer-aided detection CAD system for breast tumors is designed in this paper.The CAD system was constructed using three networks,namely,the VGG16,Inception V3,and ResNet50.Then,the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system.Result:CAD system built based on VGG16,Inception V3,and ResNet50 has higher performance than mainstream CAD systems.Among them,the system built based on VGG16 and ResNet50 has outstanding performance.We further explore the impact of the secondary migration on the experimental results in the VGG16 system,and these results show that the migration can improve system performance of the proposed framework.Conclusion:The accuracy of CNN represented by VGG16 is as high as 91.25%,which is more accurate than traditional machine learningmodels.The F1 score of the three basic networks that join the secondary migration is close to 1.0,and the performance of the VGG16-based breast tumor CAD system is higher than Inception V3,and ResNet50.

3.0 Tesla vs 1.5 Tesla breast magnetic resonance imaging in newly diagnosed breast cancer patients

AIM:To compare 3.0 Tesla(T) vs 1.5T magnetic resonance(MR) imaging systems in newly diagnosed breast cancer patients.METHODS:Upon Institutional Review Board approval,a Health Insurance Portability and Accountability Actcompliant retrospective review of 147 consecutive 3.0T MR examinations and 98 consecutive 1.5T MR examinations in patients with newly diagnosed breast cancer between 7/2009 and 5/2010 was performed.Eleven patients who underwent neoadjuvant chemotherapy in the 3.0T group were excluded.Mammographically occult suspicious lesions(BIRADS Code 4 and 5) additional to the index cancer in the ipsilateral and contralateral breast were identified.Lesion characteristics and pathologic diagnoses were recorded,and results achieved with both systems compared.Statistical significance was analyzed using Fisher’s exact test.RESULTS:In the 3.0T group,206 suspicious lesions were identified in 55%(75/136) of patients and 96%(198/206) of these lesions were biopsied.In the 1.5T group,98 suspicious lesions were identified in 53%(52/98) of patients and 90%(88/98) of these lesions were biopsied.Biopsy results yielded additional malignancies in 24% of patients in the 3.0T group vs 14% of patients in the 1.5T group(33/136 vs 14/98,P = 0.07).Average size and histology of the additional cancers was comparable.Of patients who had a suspicious MR imaging study,additional cancers were found in 44% of patients in the 3.0T group vs 27% in the 1.5T group(33/75 vs 14/52,P = 0.06),yielding a higher positive predictive value(PPV) for biopsies performed with the 3.0T system.CONCLUSION:3.0T MR imaging detected more additional malignancies in patients with newly diagnosed breast cancer and yielded a higher PPV for biopsies performed with the 3.0T system.

Value of magnetic resonance diffusion combined with perfusion imaging techniques for diagnosing potentially malignant breast lesions

BACKGROUND Lesions of breast imaging reporting and data system(BI-RADS)4 at mammography vary from benign to malignant,leading to difficulties for clinicians to distinguish between them.The specificity of magnetic resonance imaging(MRI)in detecting breast is relatively low,leading to many false-positive results and high rates of re-examination or biopsy.Diffusion-weighted imaging(DWI),combined with perfusion-weighted imaging(PWI),might help to distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.AIM To evaluate the value of DWI and PWI in diagnosing BI-RADS 4 breast lesions.METHODS This is a retrospective study which included patients who underwent breast MRI between May 2017 and May 2019 in the hospital.The lesions were divided into benign and malignant groups according to the classification of histopathological results.The diagnostic efficacy of DWI and PWI were analyzed respectively and combinedly.The 95 lesions were divided according to histopathological diagnosis,with 46 benign and 49 malignant.The main statistical methods used included the Student t-test,the Mann-Whitney U-test,the chi-square test or Fisher’s exact test.RESULTS The mean apparent diffusion coefficient(ADC)values in the parenchyma and lesion area of the normal mammary gland were 1.82±0.22×10^(-3)mm^(2)/s and 1.24±0.16×10^(-3)mm^(2)/s,respectively(P=0.021).The mean ADC value of the malignant group was 1.09±0.23×10^(-3)mm^(2)/s,which was lower than that of the benign group(1.42±0.68×10^(-3)mm^(2)/s)(P=0.016).The volume transfer constant(Ktrans)and rate constant(Kep)values were higher in malignant lesions than in benign ones(all P<0.001),but there were no significant statistical differences regarding volume fraction(V_(e))(P=0.866).The sensitivity and specificity of PWI combined with DWI(91.7%and 89.3%,respectively)were higher than that of PWI or DWI alone.The accuracy of PWI combined with DWI in predicting pathological results was significantly higher than that predicted by PWI or DWI alone.CONCLUSION DWI,combined with PWI,might possibly distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.

Progresses of Functional Magnetic Resonance Imaging Diagnosis in Breast Cancer

Breast cancer is the most common malignant tumor that threatens women’s health. Breast magnetic resonance imaging (MRI) is a commonly used method recommended for the diagnosis of breast cancer. Diffusion weighted imaging (DWI) and dynamic enhanced magnetic resonance imaging (DCE-MRI) are now widely used. At present, with the continuous advancement of magnetic resonance technology, Magnetic resonance spectroscopy (MRS), Perfusion weighted imaging (PWI), Positron emission tomography-magnetic resonance imaging (PET-MRI) and so on are gradually being used in clinical practice. Mammography imaging and imaging genomics are hot topics. This article will briefly introduce several functional magnetic resonance techniques and their latest applications.

The accuracy of magnetic resonance imaging and ultrasound in evaluating the size of early-stage breast neoplasms

Objective Breast cancer is the most frequently diagnosed cancer in women. Accurate evaluation of the size and extent of the tumor is crucial in selecting a suitable surgical method for patients with breast cancer. Both overestimation and underestimation have important adverse effects on patient care. This study aimed to evaluate the accuracy of breast magnetic resonance imaging(MRI) and ultrasound(US) examination for measuring the size and extent of early-stage breast neoplasms.Methods The longest diameter of breast tumors in patients with T_(1–2)N_(0–1)M_0 invasive breast cancer preparing for breast-conserving surgery(BCS) was measured preoperatively by using both MRI and US and their accuracy was compared with that of postoperative pathologic examination. If the diameter difference was within 2 mm, it was considered to be consistent with pathologic examination.Results A total of 36 patients were imaged using both MRI and US. The mean longest diameter of the tumors on MRI, US, and postoperative pathologic examination was 20.86 mm ± 4.09 mm(range: 11–27 mm), 16.14 mm ± 4.91 mm(range: 6–26 mm), and 18.36 mm ± 3.88 mm(range: 9–24 mm). US examination underestimated the size of the tumor compared to that determined using pathologic examination(t = 3.49, P < 0.01), while MRI overestimated it(t =-6.35, P < 0.01). The linear correlation coefficients between the image measurements and pathologic tumor size were r = 0.826(P < 0.01) for MRI and r = 0.645(P < 0.01) for US. The rate of consistency of MRI and US compared to that with pathologic examination was 88.89% and 80.65%, respectively, and there was no statistically significant difference between them(χ~2 = 0.80, P > 0.05).Conclusion MRI and US are both effective methods to assess the size of breast tumors, and they maintain good consistency with pathologic examination. MRI has a better correlation with pathology. However, we should be careful about the risk of inaccurate size estimation.

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics:State of art

Breast cancer represents the most common malignancy in women,being one of the most frequent cause of cancer-related mortality.Ultrasound,mammography,and magnetic resonance imaging(MRI)play a pivotal role in the diagnosis of breast lesions,with different levels of accuracy.Particularly,dynamic contrastenhanced MRI has shown high diagnostic value in detecting multifocal,multicentric,or contralateral breast cancers.Radiomics is emerging as a promising tool for quantitative tumor evaluation,allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities.Radiomics analysis may provide novel information through the quantification of lesions heterogeneity,that may be relevant in clinical practice for the characterization of breast lesions,prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers.Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions.Particularly,the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management.The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.

Magnetic resonance image-guided versus ultrasound-guided high-intensity focused ultrasound in the treatment of breast cancer

Image-guided high-intensity focused ultrasound (HIFU) has been used for more than ten years, primarily in the treatment of liver and prostate cancers. HIFU has the advantages of precise cancer ablation and excellent protection of healthy tissue. Breast cancer is a common cancer in women. HIFU therapy, in combination with other therapies, has the potential to improve both oncologic and cosmetic outcomes for breast cancer patients by providing a curative therapy that conserves mammary shape. Currently, HIFU therapy is not commonly used in breast cancer treatment, and efforts to promote the application of HIFU is expected. In this article, we compare different image-guided models for HIFU and reviewed the status, drawbacks, and potential of HIFU therapy for breast cancer.

Efficacy of Magnetic Resonance Imaging in BIRADS 3, 4 and 5 Patients Detected on Full Field Digital Mammography: Our Experience

Aim of the study: To perform Dynamic contrast enhanced MRI of breast in patients with positive findings (BIRADS 3, 4 and 5) detected on screening mammography;to correlate the findings of digital mammography and contrast enhanced MRI of breast with histopathological examinations. Settings and Design: A prospective observation study was conducted at a single centre, i.e. HCG Manavata Cancer Centre. Materials and Methods: Screening mammography was performed on patients with age > 40 years and on patients with age 35 - 40 years having positive family history. The positive mammography was reported and the lesions classified according to BIRADS criteria for mammography. Results: Mammographic examination of the breast lesions yielded an overall sensitivity of 97.67% and a specificity of 85.71%. In our study we combined both morphologic and dynamic parameters and its modification into BIRADS category for lesion classification. The sensitivity of MRI examinations was 97.67% while the specificity was 71.43%. Spiculated margins were encountered only in malignant lesions (p = 0.0006). Statistical correlation was obtained between the pathologically proven benign and malignant lesions regarding their enhancement pattern with p value of Conclusion: As per the results, dynamic contrast MRI had high sensitivity but limited specificity. We did not find any significant difference between FFDM and MRI in terms of diagnostic accuracy. The use of DWI showed high specificity at cut off point of ADC value—0.85 mm2/s. Thus, DWI can be used in addition of morphological and dynamic kinetic characteristics to increase specificity of MRI.

Diagnostic performance of contrast-enhanced ultrasound and enhanced magnetic resonance for breast nodules

In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound（US）, contrastenhanced US（CEUS）, combined US and CEUS and magnetic resonance imaging（MRI） in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve.The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them（kappa 〈0.20）. In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.

Diffusion-tensor imaging as an adjunct to dynamic contrastenhanced MRI for improved accuracy of differential diagnosis between breast ductal carcinoma in situ and invasive breast carcinoma

Objective： To determine the value of diffusion-tensor imaging （DTI） as an adjunct to dynamic contrastenhanced magnetic resonance imaging （DCE-MRI） for improved accuracy of differential diagnosis between breast ductal carcinoma in situ （DCIS） and invasive breast carcinoma （IBC）. Methods： The MRI data of 63 patients pathologically confirmed as breast cancer were analyzed. The conventional MRI analysis metrics included enhancement style, initial enhancement characteristic, maximum slope of increase, time to peak, time signal intensity curve （TIC） pattern, and signal intensity on FS- T2WI. The values of apparent diffusion coefficient （ADC）, directionally-averaged mean diffusivity （D^vg）, exponential attenuation （EA）, fractional anisotropy （FA）, volume ratio （VR） and relative anisotropy （RA） were calculated and compared between DCIS and IBC. Multivariate logistic regression was used to identify independent factors for distinguishing IBC and DCIS. The diagnostic performance of the diagnosis equation was evaluated using the receiver operating characteristic （ROC） curve. The diagnostic efficacies of DCE- MRI, DWI and DTI were compared independently or combined. Results： EA value, lesion enhancement style and TIC pattern were identified as independent factor for differential diagnosis of IBC and DCIS. The combination diagnosis showed higher diagnostic efficacy than a single use of DCE-MRI （P=0.02）, and the area of the curve was improved from 0.84 （95% CI, 0.67-0.99） to 0.94 （95% CI, 0.85-1.00）. Conclusions： Quantitative DTI measurement as an adjunct to DCE-MRI could improve the diagnostic performance of differential diagnosis between DCIS and IBC compared to a single use of DCE-MRI.

Application of T2~*-weighted first-pass perfusion imaging in the diagnosis of breast tumors

Objective:To study the diagnostic value of T2*-weighted first-pass perfusion imaging in breast tumors.Methods: We analyzed the magnetic resonance imaging(MRI)information along with the pathological and immunohistochemistry re- sults.Magnetic resonance imaging was performed in 28 patients with breast tumor.The time to signal intensity curves were generated according to the T2*-weighted first-pass perfusion imaging.The curve’s maximal signal intensity drop rate and maximal signal intensity decrease time were analyzed and compared with the pathological diagnoses after surgery.Results: Malignant breast lesions showed higher maximal signal intensity drop rate(44.69%±17.07 vs.17.22%±7.49,P<0.001) than benign lesions,but there was no significant difference of maximal signal decrease time between those two lesions(23.94 s±4.92 vs.20.02 s±6.83,P>0.05).Conclusion:The T2*-weighted first-pass perfusion imaging has enough sensitivity and specificity in breast tumor diagnosis.

Breast imaging:A survey

Breast cancer is the second leading cause of death in women.It occurs when cells in the breast start to grow out of proportion and invade neighboring tissues or spread throughout the body.Mammography is one of the most effective and popular modalities presently used for breast cancer screening and detection.Efforts have been made to improve the accuracy of breast cancer diagnosis using different imaging modalities.Ultrasound and magnetic resonance imaging have been used to detect breast cancers in high risk patients.Recently,electrical impedance imaging and nuclear medicine techniques are also being widely used for breast cancer screening and diagnosis.In this paper,we discuss the capabilities of various breast imaging modalities.

Differentiation between Benign and Malignant Breast Lesions Using ADC on Diffusion-Weighted Imaging at 3.0 T

Aim: To investigate the diagnostic efficiency of apparent diffusion coefficient value (ADC) in differentiating benign from malignant breast lesions at 3.0 T diffusion-weighted imaging with different pair of b value. Methods: Total 110 patients with 107 lesions (44 benign and 63 malignant) were selected for our study with five different b-values 0, 400, 800, 1200 and 1600 s/mm2. ADC values were calculated using different pairs of b values. The cut-off ADC values and diagnostic efficiency were evaluated by receiver operating characteristic analysis. Comparison of Mean ADC value for breast lesions was determined by using independent sample t test. ROC curves were used for diagnostic efficiency of ADC using different pairs of b values. Results: With increase of b value, mean ADC value decreases. The mean ADC values for benign were 1.73 × 10-3 mm2/s for b 0 and 400, 1.57 × 10-3 mm2/s for b 0 and 800, 1.43 × 10-3 mm2/s for b 0 and 1200 and 1.30 × 10-3 mm2/s for b 0 and 1600 s/mm2. The mean ADC values for the malignant breast lesion were 1.21 × 10-3 mm2/s for b 0 and 400, 1.06 × 10-3 mm2/s for b 0 and 800, 0.94 × 10-3 mm2/s for b 0 and 1200 and 0.86 × 10-3 mm2/s for b 0 and 1600 s/mm2. ADC diagnostic efficiency for benign and malignant lesion for all the pair of b value combination was significant (p > 0.05). The sensitivity, specificity, PPV, NPV and accuracy were 80.95%, 90.9%, 92.72%, 76.92%, 85.04% for b 0 and 400;84.12%, 90.9%, 92.98%, 80%, 86.91% for b 0 and 800;84.12%, 90.9%, 92.98%, 80%, 86.91% for b 0 and 1200;84.12%, 90.9%, 92.98%, 80%, 86.91% for b 0 and 1600 s/mm2 respectively. Conclusion: DWI is effective in differentiating benign and malignant breast lesion at 3.0 Tesla using ADC with higher b value combination.

Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

MR imaging features of secondary breast lymphoma in a male patient:a case report and literature review

We reported the rare case of an elderly man with secondary breast lymphoma （SBL） associated with magnetic resonance imaging findings. MR images demonstrated multiple well-defined masses in the left breast, with heterogeneous enhancement on dynamic contrast-enhanced sequences. The time signal-intensity curve rapidly increased during the initial rise phase and washed out during the delayed phase. The apparent diffusion coefficient （ADC） value was 0.649 x 104 mm2/s. Maximum intensity projection （MIP） showed that the masses were distributed in the upper outer quadrant, in the axillary region and in the lower outer region of the left chest wall. The pathology confirmed the diagnosis of non-Flodgkin＇s lymphoma. The combination of morphological and kinetic features, as well as a significantly lower ADC value, are helpful in the diagnosis of breast lymphoma and its differentiation from breast cancer.