Imaging Modalities in Inflammatory Breast Cancer (IBC) Diagnosis: A Computer-Aided Diagnosis System Using Bilateral Mammography Images.

Inflammatory breast cancer (IBC) is an aggressive type of breast cancer. It leads to a significantly shorter survival than other types of breast cancer in the U.S. The American Joint Committee on Cancer (AJCC) defines the diagnosis based on specific criteria. However, the clinical presentation of IBC in North Africa (Egypt, Morocco, and Tunisia) does not agree, in many cases, with the AJCC criteria. Healthcare providers with expertise in IBC diagnosis are limited because of the rare nature of the disease. This paper reviewed current imaging modalities for IBC diagnosis and proposed a computer-aided diagnosis system using bilateral mammograms for early and improved diagnosis. The National Institute of Cancer in Egypt provided the image dataset consisting of IBC and non-IBC cancer cases. Type 1 and Type 2 fuzzy logic classifiers use the IBC markers that the expert team identified and extracted carefully. As this research is a pioneering work in its field, we focused on breast skin thickening, its percentage, the level of nipple retraction, bilateral breast density asymmetry, and the ratio of the breast density of both breasts in bilateral digital mammogram images. Granulomatous mastitis cases are not included in the dataset. The system's performance is evaluated according to the accuracy, recall, precision, F1 score, and area under the curve. The system achieved accuracy in the range of 92.3-100%.

The role of automated breast ultrasound in the assessment of the local extent of breast cancer.

Automated breast ultrasound (ABUS) is a non-invasive advanced ultrasound modality. The degree of extension of the cancer within the breast is very important to choose the appropriate kind of surgery/therapy. In the current work, the aim was to evaluate the role of the ABUS in the assessment of the local extent of the breast cancer before management. This is a prospective analysis that studied 562 female patients with proved breast cancers. Evaluation was in regard of the size, multiplicity, and the stromal invasion (ie, the presence of tumor emboli or tumor masses within the stroma of the breast tissue) around the tumor. Cases were subjected to automated breast ultrasound performed in the axial and coronal planes. ABUS showed high accuracy of assessment of the tumor multiplicity (82.2%) and the stromal involvement (93.5%). There was a statistical significance (P < .001) between the ABUS and the pathology regarding the measurement of the size of the index cancer. In conclusion, ABUS could be used for determination of the intramammary extend of the breast cancer. ABUS provided accurate assessment of the peritumor stromal involvement and multiplicity of the cancer which is required to choose the proper choice of surgery.

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

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

Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research.

Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM). New deep learning (DL) models were developed that have accuracies equal to that of an average radiologist. However, most studies trained the DL models on DM images as no datasets exist for CESM images. We aim to resolve this limitation by releasing a Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images (CDD-CESM) to evaluate decision support systems. The dataset includes 2006 images, with an average resolution of 2355 × 1315, consisting of 310 mass images, 48 architectural distortion images, 222 asymmetry images, 238 calcifications images, 334 mass enhancement images, 184 non-mass enhancement images, 159 postoperative images, 8 post neoadjuvant chemotherapy images, and 751 normal images, with 248 images having more than one finding. This is the first dataset to incorporate data selection, segmentation annotation, medical reports, and pathological diagnosis for all cases. Moreover, we propose and evaluate a DL-based technique to automatically segment abnormal findings in images.

Biological and molecular studies on specific immune cells treated with checkpoint inhibitors for the thera-personal approach of breast cancer patients (ex-vivo study).

Immunotherapy becomes a promising line of treatment for breast cancer (BC) however, its success rate is still limited. Methods: The study was designed to optimize the condition for producing an effective dendritic cell (DCs) based immunotherapy by using DCs and T lymphocytes together with tumor-infiltrating lymphocytes (TILs) and tumor-infiltrating DCs (TIDCs), treated with anti-PD1 and anti-CTLA4 monoclonal antibodies. This mixture of immune cells was co-cultured with autologous breast cancer cells (BCCs) isolated from 26 BC females. Results: There was a significant upregulation of CD86 and CD83 on DCs (p = 0.001 and 0.017, respectively), similarly upregulation of CD8, CD4 and CD103 on T cells (p = 0.031, 0.027, and 0.011, respectively). While there was a significant downregulation of FOXP3 and combined CD25.CD8 expression on regulatory T cells (p = 0.014 for both). Increased CD8/Foxp3 ratio (p < 0.001) was also observed. CD133, CD34 and CD44 were downregulated on BCCs (p = 0.01, 0.021, and 0.015, respectively). There was a significant increase in interferon-γ (IFN-γ, p < 0.001), lactate dehydrogenase (LDH, p = 0.02), and a significant decrease in vascular endothelial growth factor (VEGF, p < 0.001) protein levels. Gene expression of FOXP3 and Programmed cell death ligand 1 (PDL-1) were downregulated in BCCs (p < 0.001, for both), similarly cytotoxic T lymphocyte antigen-4 (CTLA4, p = 0.02), Programmed cell death 1 (PD-1, p < 0.001) and FOXP3 (p < 0.001) were significantly downregulated in T cells. Conclusion: Ex-vivo activation of immune cells (DCs, T cells, TIDCs, and TILs) with immune checkpoint inhibitors could produce a potent and effective BC immunotherapy. However, these data should be validated on an experimental animal model to be transferred to the clinical setting.

The impact of contrast-enhanced spectral mammogram (CESM) and three-dimensional breast ultrasound (3DUS) on the characterization of the disease extend in cancer patients.

OBJECTIVE: The main importance of imaging breast cancer is to guide conservative surgeries. In this study, we evaluated the role of contrast-enhanced spectral mammogram (CESM) in correlation with three-dimensional (3D) breast ultrasound in characterizing the extension of the intramammary cancer in view of the: (i) the size of the main tumor, (ii) the multiplicity of the breast cancer, and (iii) the peri-tumoral stromal involvement (i.e. free or intraductal extension of the cancer). METHODS: The study is a prospective analysis that included 300 breast masses proved to be malignant. The masses were evaluated for their size, multiplicity and surrounding stromal involvement. Contrast-based mammography performed with low (22-33 kVp) and high (44-49 kVp) energy exposures that were taken after i.v. injection of contrast agent and followed by bilateral 3D breast ultrasound. Operative data were the gold standard reference. RESULTS: There was no significant difference between the sizes of the included cancers as measured by CESM and 3D ultrasound and that measured at the pathological analysis. CESM showed higher accuracy (32.7%, n = 98) than 3D ultrasound (24.7%, n = 74) in the size agreement within 5% range. CESM was the most accurate modality (94%, n = 282) in detecting tumor multiplicity, followed by traditional sonomammogram (88%, n = 264), then 3D breast ultrasound (84%, n = 252). Intraductal extension of the breast cancer was best evaluated by the 3D ultrasound with an accuracy value of 98% (n = 294) compared to only 60% (n = 180) by CESM. CONCLUSION: CESM is a recommended investigation in breast cancer to increase the accuracy of size measurement and the detection of multiple tumors. The addition of 3D ultrasound can enhance the detection of intraductal extension. Advances in knowledge: Choice of conservative breast surgery vs mastectomy is still a debate. We used an advanced, contrast-based, application of the mammogram: CESM and a non-invasive 3D breast ultrasound in the assessment of the local extension of the breast cancer regarding size, perifocal stromal infiltration and multiplicity to guide the selection of proper management in proved cases of breast cancer.

Staging of breast cancer and the advanced applications of digital mammogram: what the physician needs to know?

OBJECTIVE: To study the role of advanced applications of digital mammogram, whether contrast-enhanced spectral mammography (CESM) or digital breast tomosynthesis (DBT), in the "T" staging of histologically proven breast cancer before planning for treatment management. METHODS: In this prospective analysis, we evaluated 98 proved malignant breast masses regarding their size, multiplicity and the presence of associated clusters of microcalcifications. Evaluation methods included digital mammography (DM), 3D tomosynthesis and CESM. Traditional DM was first performed then in a period of 10-14-day interval; breast tomosynthesis and contrast-based mammography were performed for the involved breast only. Views at tomosynthesis were acquired in a "step-and-shoot" tube motion mode to produce multiple (11-15), low-dose images and in contrast-enhanced study, low-energy (22-33 kVp) and high-energy (44-49 kVp) exposures were taken after the i.v. injection of the contrast agent. Operative data were the gold standard reference. RESULTS: Breast tomosynthesis showed the highest accuracy in size assessment (n = 69, 70.4%) than contrast-enhanced (n = 49, 50%) and regular mammography (n = 59, 60.2%). Contrast-enhanced mammography presented the least performance in assessing calcifications, yet it was most sensitive in the detection of multiplicity (92.3%), followed by tomosynthesis (77%) and regular mammography (53.8%). The combined analysis of the three modalities provided an accuracy of 74% in the "T" staging of breast cancer. CONCLUSION: The combined application of tomosynthesis and contrast-enhanced digital mammogram enhanced the performance of the traditional DM and presented an informative method in the staging of breast cancer. Advances in knowledge: Staging and management planning of breast cancer can divert according to tumour size, multiplicity and the presence of microcalcifications. DBT shows sharp outlines of the tumour with no overlap tissue and spots microcalcifications. Contrast-enhanced spectral mammogram shows the extent of abnormal contrast uptake and detects multiplicity. Integrated analysis provides optimal findings for proper "T" staging of breast cancer.

Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

OBJECTIVE:: To assess the feasibility of using the MRI breast imaging reporting and data system (BI-RADS) lexicon morphology descriptors to characterize enhancing breast lesions identified on contrast-enhanced spectral mammography (CESM). METHODS:: The study is a retrospective analysis of the morphology descriptors of 261 enhancing breast lesions identified on CESM in 239 patients. We presented the morphological categorization of the included lesions into focus, mass and non-mass. Further classifications included (1) the multiplicity for "focus" category, (2) the shape, margin and internal enhancement for "mass" category and (3) the distribution and internal enhancement for "non-mass" category. Each morphology descriptor was evaluated individually (irrespective of all other descriptors) by calculating its sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) and likelihood ratios (LRs). RESULTS:: The study included 68/261 (26.1%) benign lesions and 193/261 (73.9%) malignant lesions. Intensely enhancing foci, whether single (7/12, 58.3%) or multiple (2/12, 16.7%), were malignant. Descriptors of "irregular"-shape (PPV: 92.4%) and "non-circumscribed" margin (odds ratio: 55.2, LR positive: 4.77; p-value: <0.001) were more compatible with malignancy. Internal mass enhancement patterns showed a very low specificity (58.0%) and NPV (40.0%). Non-mass enhancement (NME) was detected in 81/261 lesions. Asymmetrical NME in 81% (n = 52/81) lesions was malignant lesions and internal enhancement patterns indicative of malignancy were the heterogeneous and clumped ones. CONCLUSION:: We can apply the MRI morphology descriptors to characterize lesions on CESM, but with few expectations. In many situations, irregular-shaped, non-circumscribed masses and NME with focal, ductal or segmental distribution and heterogeneous or clumped enhancement are the most suggestive descriptors of malignant pathologies. ADVANCES IN KNOWLEDGE:: (1) The MRI BI-RADS lexicon morphology descriptors can be applied in the characterization of enhancing lesions on CESM with a few exceptions. (2) Multiple bilateral intensely enhancing foci should not be included under the normal background parenchymal enhancement unless they are proved to be benign by biopsy. (3) Mass lesion features that indicated malignancy were irregular-shaped, spiculated and irregular margins and heterogeneous internal enhancement patterns. The rim enhancement pattern should not be considered as a descriptor of malignant lesions unless CESM is coupled with an ultrasound examination.

Contrast-enhanced spectral mammography: Impact of the qualitative morphology descriptors on the diagnosis of breast lesions.

OBJECTIVE: To analyze the morphology and enhancement characteristics of breast lesions on contrast-enhanced spectral mammography (CESM) and to assess their impact on the differentiation between benign and malignant lesions. MATERIALS AND METHOD: This ethics committee approved study included 168 consecutive patients with 211 breast lesions over 18 months. Lesions classified as non-enhancing and enhancing and then the latter group was subdivided into mass and non-mass. Mass lesions descriptors included: shape, margins, pattern and degree of internal enhancement. Non-mass lesions descriptors included: distribution, pattern and degree of internal enhancement. The impact of each descriptor on diagnosis individually assessed using Chi test and the validity compared in both benign and malignant lesions. The overall performance of CESM were also calculated. RESULTS: The study included 102 benign (48.3%) and 109 malignant (51.7%) lesions. Enhancement was encountered in 145/211 (68.7%) lesions. They further classified into enhancing mass (99/145, 68.3%) and non-mass lesions (46/145, 31.7%). Contrast uptake was significantly more frequent in malignant breast lesions (p value ≤ 0.001). Irregular mass lesions with intense and heterogeneous enhancement patterns correlated with a malignant pathology (p value ≤ 0.001). CESM showed an overall sensitivity of 88.99% and specificity of 83.33%. The positive and negative likelihood ratios were 5.34 and 0.13 respectively. CONCLUSION: The assessment of the morphology and enhancement characteristics of breast lesions on CESM enhances the performance of digital mammography in the differentiation between benign and malignant breast lesions.

Evaluation of BreastLight as a tool for early detection of breast lesions among females attending National Cancer Institute, Cairo University.

BACKGROUND: Breast illumination was suggested as a simple method for breast cancer screening. BreastLight is a simple apparatus for this purpose. OBJECTIVE: To evaluate the diagnostic performance of BreastLight as a screening tool of breast cancer in comparison to mammography and histopathology. MATERIALS AND METHODS: This hospital-based cross sectional study was conducted in the mammography unit of the radiodiagnosis department at National Cancer Institute, Cairo University. All participants were subjected to breast examination with the BreastLight tool, mammography and ultrasonography. Suspicious cases were biopsied for histopathological examination which is considered as a gold standard. RESULTS: The mean age of the participants was 46.3±12.4 years. Breast illumination method had sensitivity, specificity, positive predictive value, negative predictive value and total accuracy of 93.0%, 73.7%, 91.4%, 77.8% and 88.2%, respectively in detection of breast cancer. CONCLUSIONS: Breast illumination method with BreastLight apparatus is a promising easy-to-use tool to screen for breast cancer suitable for primary health care physician or at-home use. It needs further evaluation especially in asymptomatic women.

Role of Sonoelastography in the Differentiation between Benign and Malignant Breast Lesions.

PURPOSE: To prospectively evaluate the accuracy of real time elastography (ultrasound strain imaging) for distinguishing between benign and malignant solid breast lesions with the pathologic results as the reference standard. We also evaluated if the fat÷lesion ratio could semiquantitatively evaluate the stiffness of breast lesions. PATIENTS AND METHODS: Conventional ultrasonography (US) and real time elastography were performed in 100 women with breast masses with the mean age is 50 years. Elasticity images were given an elasticity score according to the degree and distribution of the strain induced by light compression with 1-3 is benign and 4-5 is malignant. We also calculated the ratio of the normal breast tissue to that of the lesion (fat÷lesion ratio) of the different breast lesions with the fat as the reference. The cutoff point was 4.8 with ratio below this level is considered benign and above this level is considered malignant. RESULTS: For elasticity score, the mean standard deviation was 4.1 for malignant lesions and 2.1 for benign lesions (p<0.001). When a cutoff point between 3 and 4 was used, elastography had 87.2% sensitivity, 90.6% specificity and 90% accuracy. The fat÷lesion ratio (F÷L ratio) of the benign lesions was different from that of the malignant ones. CONCLUSION: US strain imaging can facilitate improved classification of benign and malignant breast masses and has the potential to aid their diagnosis. By using the F÷L ratio, the stiffness of breast lesions could be semiquantitated and this method may provide another diagnostic method in addition to the scoring system. KEY WORDS: Breast - Sonoelastography - Strain imaging - Fat-lesion ratio.

Women Health Outreach Program; a New Experience for all Egyptian Women.

UNLABELLED: The global health community faces a challenge with breast cancer being the most common cause of cancer related death among women around the globe. Since breast cancer's pathogenesis is poorly understood, primary prevention is still a distant goal. Thus secondary prevention through early detection is the only feasible approach at present. With this strong conviction, the launching of the first Egyptian national screening program "Women Health Outreach Program" (WHOP), was announced on October 30th 2007. This project is a government- funded program that offers free breast screening for all Egyptian women above the age of 45 years. In addition to free mammograms, the program gives the participants a chance to be screened for diabetes, hypertension and obesity as well. Positively detected cases are also offered the option of free management. During the period from October 30th, 2007, up to February 9th, 2009, 20, 098 women in Cairo, Alexandria and Suez governorates were screened for breast cancer, diabetes, hypertension and obesity through the program. In this article we will represent the achievements, challenges and services delivered by WHOP. KEY WORDS: Breast cancer - Breast screening - Early detection - Breast biopsy.