Grade Invasive Ductal Carcinoma Research Articles

IMAML-IDCG: Optimization-based meta-learning with ImageNet feature reusing for few-shot invasive ductal carcinoma grading

Model-Agnostic Meta-Learning (MAML) is a widely used few-shot learning (FSL) technique that reduces reliance on large, labeled datasets in deep learning for medical imaging analysis. However, MAML requires backpropagating through all feature layers for task adaptation, leading to suboptimal computational efficiency. We propose IMAML-IDCG (ImageNet Model-Agnostic Meta-Learning in Invasive Ductal Carcinoma Grading), which enhances computational efficiency for few-shot grading of Invasive Ductal Carcinoma (IDC) through three key techniques: (1) ImageNet feature reusing, (2) ImageNet partial freezing strategy, and (3) adaptive inner learning rate. IMAML-IDCG is initialized with ImageNet pre-trained weights. During the inner optimization loop, only the model’s classifier head layer is optimized, leveraging prior ImageNet knowledge (ImageNet feature reusing) and employing an adaptive learning rate for improved task adaptation. In the outer optimization loop, IMAML-IDCG selectively fine-tunes the last few model layers to enhance efficiency and reduce overfitting (ImageNet partial freezing strategy). We evaluated IMAML-IDCG using the BreaKHis dataset (7,909 images) as the base dataset, and the BCHI (282 images) and PathoIDCG (3,744 images) datasets as the novel datasets. Our empirical results demonstrate that IMAML-IDCG outperforms MAML and other FSL methods in few-shot IDC grading tasks across various cross-magnification domain settings. Notably, IMAML-IDCG achieves a 14.64% improvement over MAML on the BCHI dataset and a 6.04% improvement on the PathoIDCG 40X dataset when meta-trained with the BreaKHis 40X dataset in the 3-way 5-shot scenario.

Value of magnetic resonance imaging radiomics features in predicting histologic grade of invasive ductal carcinoma of the breast.

Breast cancer has the second highest mortality rate of all cancers and occurs mainly in women. To investigate the relationship between magnetic resonance imaging (MRI) radiomics features and histological grade of invasive ductal carcinoma (IDC) of the breast and to evaluate its diagnostic efficacy. The two conventional MRI quantitative indicators, i.e. the apparent diffusion coefficient (ADC) and the initial enhancement rate, were collected from 112 patients with breast cancer. The breast cancer lesions were manually segmented in dynamic contrast-enhanced MRI (DCE-MRI) and ADC images, the differences in radiomics features between Grades I, II and III IDCs were compared and the diagnostic efficacy was evaluated. The ADC values (0.77 ± 0.22 vs 0.91 ± 0.22 vs 0.92 ± 0.20, F= 4.204, p< 0.01), as well as the B_sum_variance (188.51 ± 67.803 vs 265.37 ± 77.86 vs 263.74 ± 82.58, F= 6.040, p< 0.01), L_energy (0.03 ± 0.02 vs 0.13 ± 0.11 vs 0.12 ± 0.14, F= 7.118, p< 0.01) and L_sum_average (0.78 ± 0.32 vs 16.34 ± 4.23 vs 015.45 ± 3.74, F= 21.860, p< 0.001) values of patients with Grade III IDC were significantly lower than those of patients with Grades I and II IDC. The B_uniform (0.15 ± 0.12 vs 0.11 ± 0.04 vs 0.12 ± 0.03, F= 3.797, p< 0.01) and L_SRE (0.85 ± 0.07 vs 0.78 ± 0.03 vs 0.79 ± 0.32, F= 3.024, p< 0.01) values of patients with Grade III IDC were significantly higher than those of patients with Grades I and II IDC. All differences were statistically significant (p< 0.05). The ADC radiomics signature model had a higher area-under-the-curve value in identifying different grades of IDC than the ADC value model and the DCE radiomics signature model (0.869 vs 0.711 vs 0.682). The accuracy (0.812 vs 0.647 vs 0.710), specificity (0.731 vs 0.435 vs 0.342), positive predictive value (0.815 vs 0.663 vs 0.669) and negative predictive value (0.753 vs 0.570 vs 0.718) of the ADC radiomics signature model were all significantly better than the ADC value model and the DCE radiomics signature model. ADC values and breast MRI radiomics signatures are significant in identifying the histological grades of IDC, with the ADC radiomics signatures having greater value.

Comparison between the Young and Elderly Diagnosed Patients of Carcinoma of the Breast

Breast cancer presents a significant health challenge in Pakistan, marked by high incidence rates and specific cultural and societal barriers to early detection and treatment. Objective: To compare diagnosed cases of CA breast in younger and older patients in terms of mode of presentation, TNM stage at presentation, histological variety and hormonal status. Methods: In our prospective comparative study conducted at the Department of Surgery, Bahawal Victoria Hospital, Bahawalpur, we included a total of 220 breast cancer patients aged 20 years and above. Patients were stratified into two distinct age groups for comparison: younger patients (aged less than or equal to 35 years) and older patients (aged more than 35 years). Modes of presentation, histological types, and hormonal receptor statuses were compared between the both groups. Results: In a study of 220 breast cancer patients with a mean age of 42.97 years, younger patients (≤35 years) constituted 24%, while older patients (&gt;35 years) made up 76%. The most common presentation was lump formation (78.18%), mainly in older patients. Ulceration was evenly distributed across age groups. Histologically, invasive ductal carcinoma Grade III was more frequent in older patients, whereas Ductal Carcinoma In Situ (DCIS) was exclusive to younger patients, underscoring distinct age-related disease patterns. Conclusions: Our study revealed significant age-related differences in breast cancer presentation among Pakistani patients. Older patients (&gt;35 years) predominantly presented with lump formation, suggesting diagnostic delays, while all younger patients (≤35 years) had DCIS, indicating possible early detection or unique tumor biology. Additionally, older patients exhibited higher ER and PR positivity.

Assessment of breast cytoarchitecture and its associated axillary lymph node status under normal and pathological conditions in Egyptian women

ObjectiveHerein, we compare the features of neoplastic cancer cells in invasive ductal carcinoma (IDC) grade II and III patients to their corresponding normal cells both in breast and axillary lymph node (ALN) tissues. MethodsA retrospective cohort of 70 female breast cancer patients enrolled between 2018 and 2020 at Medical Research Institute, Alexandria University, Egypt, was analyzed for clinicopathological features presentation. Fresh tiny pieces of breast tissue and its associated ALN tissues were then processed to investigate the morphological appearance by scanning electron microscopy. Moreover, the histological architecture of tissue sections stained with hematoxylin and eosin was studied by light microscope, while the characterization of the ultrastructure features of breast and ALN tissues was analyzed by transmission electron microscopy. ResultsClinicopathological presentation of patients revealed that the Egyptian female breast cancer population adhered to the global trends of breast cancer disease with elevated incidence rate among postmenopausal women (61.3%), high frequency of IDC (95.7%), and increased ALN metastasis (65.7%). The percentage of estrogen receptor alpha (ERα) and human epidermal growth factor receptor 2 (HER2) expression, as key indicators for carcinogenesis and disease progression was 87.1% and 55.8%, respectively. The present study points to the observed discrepancies among the investigated variables in the diagnostic separation between IDC grade II and grade III. Ductal epithelial cells organization, nuclei size and irregularity, chromatin amount and uniformity, mitochondrial abundance and dysfunction were differentially manifested in IDC grades. Moreover, aberrations in the cellular organelles like lysosomes, endoplasmic reticulum, and lipid droplets vary according to the grade of IDC and the aggressiveness of the invasive breast cancer. ConclusionsTo sum up, this study emphasizes the importance of accurate specimen evaluation for treatment choice and decision.

Data mining techniques in breast cancer diagnosis at the cellular-molecular level.

Studies in the field of better diagnosis of breast cancer using machine learning and data mining techniques have always been promising. A new diagnostic method can detect the characteristics of breast cancer in the early stages and help in better treatment. The aim of this study is to provide a method for early detection of breast cancer by reducing human errors based on data mining techniques in medicine using accurate and rapid screening. The proposed method includes data pre-processing and image quality improvement in the first step. The second step consists of separating cancer cells from healthy breast tissue and removing outliers using image segmentation. Finally, a classification model is configured by combining deep neural networks in the third phase. The proposed ensemble classification model uses several effective features extracted from images and is based on majority vote. This model can be used as a screening system to diagnose the grade of invasive ductal carcinoma of the breast. Evaluations have been done using two histopathological microscopic datasets including patients with invasive ductal carcinoma of the breast. With extracting high-level features with average accuracies of 92.65% and 93.34% in these two datasets, the proposed method has succeeded in quickly diagnosing and classifying breast cancer with high performance. By combining deep neural networks and extracting features affecting breast cancer, the ability to diagnose with the highest accuracy is provided, and this is a step toward helping specialists and increasing the chances of patients' survival.

The use of mammography-based radiomics nomograms for the preoperative prediction of the histological grade of invasive ductal carcinoma.

Accurate prediction of the grade of invasive ductal carcinoma (IDC) before treatment is vital for individualized therapy and improving patient outcomes. This study aimed to develop and validate a mammography-based radiomics nomogram that would incorporate the radiomics signature and clinical risk factors in the preoperative prediction of the histological grade of IDC. The data of 534 patients from our hospital with pathologically confirmed IDC (374 in the training cohort and 160 in the validation cohort) were retrospectively analyzed. A total of 792 radiomics features were extracted from the patients' craniocaudal and mediolateral oblique view images. A radiomics signature was generated using the least absolute shrinkage and selection operator method. Multivariate logistic regression was adopted to establish a radiomics nomogram, the utility of which was evaluated using a receiver-operating characteristic curve, calibration curve, and decision curve analysis (DCA). The radiomics signature was found to have a significant correlation with histological grade (P < 0.01), but the efficacy of the model is limited. The radiomics nomogram, which incorporated the radiomics signature and spicule sign into mammography, showed good consistency and discrimination in both the training cohort [area under the curve (AUC) = 0.75] and the validation cohort (AUC = 0.75). The calibration curves and DCA demonstrated the clinical usefulness of the proposed radiomics nomogram model. A radiomics nomogram based on the radiomics signature and spicule sign can be used to predict the histological grade of IDC and assist in clinical decision-making for patients with IDC.

Clinicopathological characteristics and prognosis of triple-negative breast cancer invasive ductal carcinoma with ductal carcinoma in situ.

The purpose of this study is to compare and analyze the clinicopathological characteristics and prognosis of patients with invasive ductal carcinoma coexisting with ductal carcinoma in situ (IDC-DCIS) and invasive ductal carcinoma (IDC) in triple-negative breast cancer (TNBC), and to explore the factors affecting the prognosis, so as to provide new ideas for clinical diagnosis and treatment of these patients. The patients with TNBC underwent surgery in the Department of Breast Surgery of Harbin Medical University Cancer Hospital from October 2012 to December 2018 were retrospectively analyzed and divided into IDC-DCIS group and IDC group. The clinicopathological characteristics and prognosis of the two groups were compared. P < 0.05 was considered statistically significant. A total of 358 patients were enrolled. There were significant differences in age (P = 0.002), family history (P = 0.016), menopausal status (P = 0.003), KI-67% (P < 0.001), lymphovascular invasion (P = 0.010), histologic grade of IDC (P < 0.001) and multifocal (P < 0.001) between the two groups. The disease-free survival (DFS) of the IDC-DCIS group was better than that of the IDC group (the 5-year DFS was 87.9% vs. 82.6%, P = 0.045), but the overall survival (OS) of the two groups was not statistically significant (the 5-year OS was 96.2% vs. 96.0%, P = 0.573). In addition, the coexistence of DCIS (P = 0.030), lymph node pathologic stage (P = 0.001), tumor location (P = 0.011), and adjuvant chemotherapy (P < 0.001) were independent prognostic factors for DFS. In TNBC, the IDC-DCIS group had less invasive biological characteristics. The DFS of the IDC-DCIS group was better than that of the IDC group, but there was no statistical difference in OS between the two groups. In addition, the coexistence of DCIS, lymph node stage, tumor location and adjuvant chemotherapy may be independent prognostic factors for DFS.

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.

Investigating the Role of Glutathione S- Transferase Genes, Histopathological and Molecular Subtypes, Gene-Gene Interaction and Its Susceptibility to Breast Carcinoma in Ethnic North- Indian Population.

Breast Cancer (BC) is a genetically and clinically heterogeneous disease including complex interactions between gene-gene and gene-environment components. This study aimed, to explore whether the Glutathione S- transferase (GSTs) gene polymorphism has role in BC susceptibility. We further evaluated the frequency of four subtypes of BC based on molecular classification followed by microscopic histological analysis to study the grades of invasive ductal carcinoma (IDC). Polymorphism in GST genes in North-Indian BC patients was assessed by multiplex-PCR and PCR-RFLP methods. 105 BC patients and 145 healthy controls were enrolled for this study. Data was analyzed by calculating the odds ratio (OR) and 95% CI from logistic regression analyses. Our findings revealed that GSTM1 null genotype (OR = 2.231; 95% CI = 1.332-3.737; p-value= 0.002) is significantly associated to BC risk in ethnic North- Indian population. However, the risk for BC susceptibility in North-Indians does not appear to be associated with GSTT1 null genotype. The GSTP1 (Val/Val) genotype (OR=1.545; CI=0.663-3.605; p-value= 0.314) was also found to be susceptible for BC risk. Combination of three high risk GST genotypes association exhibiting gene-gene interaction further confirmed the increased risk to BC in this region. The results of present study indicated that polymorphism in GSTM1 and rs1695 of GSTP1 genes may influence BC development among North-Indian women. Thus, the screening of GSTM1 and GSTP1 gene should be recommended for the earlier investigation for BC as a precautionary measure.

Automatic Detection System to Identify Invasive Ductal Carcinoma by Predicting Bloom Richardson Grading from Histopathological Images

After skin cancer, the most common type of cancer is breast cancer among the world population. Breast cancer is the leading cause of cancer-induced mortality among women. Breast cancer is frequently diagnosed by using biopsies in which tissue is removed from the breast and studied under a microscope. The results of these biopsies are based on the qualifications and experience of the pathologist who diagnoses the abnormal cell under the microscope. With the emergence of advancements in the fields of image processing and artificial intelligence, there is an area of interest in developing a deep learning model to improve and enhance the quality and accuracy of breast cancer diagnosis. This study proposed a deep learning model that automatically analyses the multiclass classification of hematoxylin and eosin-stained histological images of invasive ductal carcinoma (IDC) by discriminating the IDC into grades such as G-1, G-2, and G-3. The methodology is focused on a deep learning model to detect grades of invasive ductal carcinoma by adopting the Sequential Convolutional Neural Network Two-Dimensional (CNN2D). We used DataBiox, a public dataset taken from an internet source consisting of 922 images. We evaluate the result of multiclass classification by dividing 80% and 20% of the dataset into training and testing data, respectively. As a result of the training and testing of the pre-trained CNN model, sequential CNN yields the accuracy of the model of 92.81%. Our model accurately classifies a multi-class classification of histological images of grades of breast cancer, specifically IDC. It is ready to be tested with a more diverse and massive database in the future.

Histopathological Grading and Staging of Invasive Ductal Carcinoma in Modified Radical Mastectomy Specimens

Background: Breast carcinoma is the most common malignancy in females associated with high mortality rate. Objective: The purpose of present study was to describe the histological grade and histopathological stage of invasive ductal carcinoma of breast among female patients and to determine the association of tumor size and histological grade withnodal metastasis. Methods: This is an analytical cross sectional study, conducted in the Department of Pathology Fatima Jinnah Medical University Lahore, Pakistan from September, 2019 till February, 2020. Female patients of all age groups who were diagnosedwith invasive ductal carcinoma and underwent modified radical mastectomy were included in the study. Tissue processed and Hematoxylin and Eosin staining was performed. All the sections were examined under the light microscope by myself and two other consultant pathologists independently. Histological grading of invasive ductal carcinoma was done by following Modified Scarff Bloom Richardson grading system and histopathological staging was done according to CAP protocols. Results: Total of 60 female patients diagnosed with invasive ductal carcinoma were included in the present study. Mean age of the patient calculated was 48.17± 13.12 years with age range from 26 to 90 years. Size of the tumor ranged from 1cm to 10cm. On microscopy, histological grade III was the most frequent grade (36, 60%). Out of total 60 cases n=43(71.6%) were diagnosed with regional lymph node metastasis and T2N1Mx( 17,38.3%) was the most frequent stage of the tumor. Statistically no significant association of tumor size and histological grade was observed with regional lymph node metastasis (p &gt;0.05).

BDP1 Alterations Correlate with Clinical Outcomes in Breast Cancer.

Simple SummaryBreast cancer accounts for 30% of all new cancer diagnoses in the United States. The most common type of breast cancer is invasive breast cancer. A hallmark trait of breast cancer is uncontrolled cell growth due to genetic alterations. TFIIIB-mediated RNA polymerase III transcription is specifically deregulated in human cancers. The TFIIIB BDP1 subunit is not well characterized in human cancer. The objective of this study was to analyze publicly available clinical cancer datasets to determine if BDP1 alterations correlate with clinical outcomes in available breast cancer datasets. BDP1 copy number and expression negatively correlate with breast cancer outcomes, including stage, grade, and mortality. TFIIIB is deregulated in a variety of cancers. However, few studies investigate the TFIIIB subunit BDP1 in cancer. BDP1 has not been studied in breast cancer patients. Herein, we analyzed clinical breast cancer datasets to determine if BDP1 alterations correlate with clinical outcomes. BDP1 copy number (n = 1602; p = 8.03 × 10−9) and mRNA expression (n = 130; p = 0.002) are specifically decreased in patients with invasive ductal carcinoma (IDC). In IDC, BDP1 copy number negatively correlates with high grade (n = 1992; p = 2.62 × 10−19) and advanced stage (n = 1992; p = 0.005). BDP1 mRNA expression also negatively correlated with high grade (n = 55; p = 6.81 × 10−4) and advanced stage (n = 593; p = 4.66 × 10−4) IDC. Decreased BDP1 expression correlated with poor clinical outcomes (n = 295 samples): a metastatic event at three years (p = 7.79 × 10−7) and cancer reoccurrence at three years (p = 4.81 × 10−7) in IDC. Decreased BDP1 mRNA correlates with patient death at three (p = 9.90 × 10−6) and five (p = 1.02 × 10−6) years. Both BDP1 copy number (n = 3785; p = 1.0 × 10−14) and mRNA expression (n = 2434; p = 5.23 × 10−6) are altered in triple-negative invasive breast cancer (TNBC). Together, these data suggest a role for BDP1 as potential biomarker in breast cancer and additional studies are warranted.

Feasibility of Shear Wave Elastography Imaging for Evaluating the Biological Behavior of Breast Cancer.

ObjectiveTo explore the feasibility of shear wave elastography (SWE) parameters for assessing the biological behavior of breast cancer.Materials and MethodsIn this prospective study, 224 breast cancer lesions in 216 female patients were examined by B-mode ultrasound and shear wave elastography in sequence. The maximum size (Smax) of the lesion was measured by B-mode ultrasound, and then shear wave elastography was performed on this section to obtain relevant parameters, including maximum elasticity (Emax), mean elasticity (Emean), standard deviation of elasticity (SD), and the area ratio of shear wave elastography to B-mode ultrasound (AR). The relationship between SWE parameters and pathological type, histopathological classification, histological grade, lymphovascular invasion status (LVI), axillary lymph node status (ALN), and immunohistochemistry of breast cancer lesions was performed according to postoperative pathology.ResultsIn the univariate analysis, the pathological type and histopathological classification of breast cancer were not significantly associated with SWE parameters; with an increase in the histological grade of invasive ductal carcinoma (IDC), SD (p = 0.016) and Smax (p = 0.000) values increased. In the ALN-positive group, Smax (p = 0.004) was significantly greater than in the ALN-negative group; Smax (p = 0.003), Emax (p = 0.034), and SD (p = 0.045) were significantly higher in the LVI-positive group than in the LVI-negative group; SD (p = 0.043, p = 0.047) and Smax (p = 0.000, p = 0.000) were significantly lower in the ER+ and PR+ groups than in the ER- and PR- groups, respectively; AR (p = 0.032) was significantly higher in the ER+ groups than in the ER- groups, and Smax (p = 0.002) of the HER2+ group showed higher values than that of the HER2- group; Smax (p = 0.000), SD (p = 0.006), and Emax (p = 0.004) of the Ki-67 high-expression group showed significantly higher values than those of the Ki-67 low-expression group. In the multivariate analysis, Ki-67 was an independent factor of Smax (p = 0.005), Emax (p = 0.004), and SD (p = 0.006); ER was an independent influencing factor of Smax (p = 0.000) and AR (p = 0.032). LVI independently influences Smax (p = 0.006).ConclusionsThe SWE parameters Emax, SD, and AR can be used to evaluate the biological behavior of breast cancer.

Evaluation of Number Density of Tumor-Associated Macrophages by Immunohistochemistry and Semiquantitative Scoring in Invasive Breast Cancer: An Indian Study.

Tumor microenvironment is emerging as a critical factor for progression of breast cancer. Tumor-associated macrophages (TAMs) play an important role in promoting tumor growth. This study was aimed at correlation of number density (ND) of TAMs with invasive ductal carcinoma (IDC) grading utilizing an image morphometric technique. We also sought to compare the TAMs and ND in the tumoral area and stromal region. We also explored the relationship between the clinical and pathological prognostic parameters. The study included 75 cases of IDC that had undergone modified radical mastectomy. The Institutional Ethics Committee approved the study. Samples were classified as Grade 1, 2, and 3. Cases were graded as per the modified Bloom and Richardson criterion. Mean with standard deviation was calculated for each group. We utilized CD68 and CD163 immunostained sections for determining the ND of TAMs. TAMs were evaluated using computerized digital photomicrograph system with image analyzing software. ND was defined as the number of TAMs in total number of TAMs in five high-power fields/total area of five fields. ND was calculated separately in tumor and tumor stroma (TS). Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2/neu (HER2/neu) were scored in accordance with recommendations. Ki-67 was scored as per the recommended guidelines. Data were tabulated in Microsoft Excel. SPSS version 20.0 (IBM Corp., Armonk, NY, USA) was used for statistical analysis. To determine the relationship between macrophage density and clinicopathologic parameters, we used the independent t-test. To determine the differences in the parameters, analysis of variance (ANOVA) was utilized. Age of the patients ranged from 34 to 58 years (mean: 55.5). One-way ANOVA between various grades of tumor indicating significant differences in terms of CD68 and CD163 densities in tumor and stroma (P < 0.0001). i.e., significant increased density of CD68 and CD163 was observed in Grade 3 tumor as compared to other two groups. A greater histological grade, ER, PR negative status, and a high Ki-67 index were all associated with TAM ND. There was no relation to HER2/neu status. Result of unpaired t-test indicates increased density in stroma as compared to tumor among various grades of IDC. We analyzed images with a software using photographs of the stained slides. This helped in quantitative analysis of TAMs on the CD68 and CD163 stained sections. This approach standardizes and reproducibly counts TAMs per unit area. We found significant difference between the number densities of TAMs in grades of invasive breast carcinoma. There were statistically significant differences in numerical densities of TAMs with ER, PR negativity, and Ki-67. There was no correlation with HER2/neu. Densities of CD68 and CD163 densities are more prevalent in TS as compared to intratumoral region.

Study of angiogenesis in invasive breast carcinoma by morphometry and immunohistochemistry

BackgroundBreast cancer is the leading cause of cancer-related deaths in Asia and is emerging as the commonest female malignancy. Angiogenesis or neovascularization is important for the growth and spread of malignant tumors, and quantitative assessment of angiogenesis may prove valuable in prognostication. This study was undertaken to quantify and explore angiogenesis with immunohistochemistry with CD 34, CD 105, and vascular endothelial growth factor (VEGF), as well as morphometric analysis and correlate with the grades of the invasive breast carcinoma. MethodsAngiogenesis was assessed by morphometry and immunohistochemistry. Seventy cases of invasive ductal carcinoma (IDC) and twenty-five benign cases as controls were included in the study. Morphometry was performed on the CD34 and CD105 (Endoglin) stained representative histologic sections with the use of a computerized digital photomicrograph system using image analyzing software. Morphometric analysis and evaluation of vascular parameters, i.e. microvessel density (MVD), microvessel caliber (VC), and total microvessel boundary density (TVBD), were calculated. Semiquantitative assessment of angiogenesis of VEGF-stained sections was done by scoring. Immunohistochemical staining was correlated with the histological grade of the tumors. MVD, mean VC, TVBD with their mean values, SD, and range were calculated using Statistical Package for The Social Sciences (Version 20). One-way analysis of variance (ANOVA) with Tukey HSD was performed to assess the difference of the parameters for the groups. Spearman rank correlation coefficients ρ were calculated. ResultsThe vascular parameters were significantly more in malignant lesions as compared to benign lesions and showed differences with increasing grade. Grades of breast carcinoma showed a mild positive correlation with VEGF (ρ = 0.467), MVD-CD34 (ρ = 0.422) and VC-CD34 (ρ = 0.482); and moderate positive correlation with TVBD-CD34 (ρ = 0.615), VC-CD105 (ρ = 0.527), and TVBD-CD105 (ρ = 0.354). When these parameters were compared with each other for all four groups, VEGF showed a mild positive correlation with MVD-CD34 (ρ = 0.295), TVBD-CD34 (ρ = 0.339), and TVBD-CD105 ((ρ = 0.277). MVD-CD105 showed a mild positive correlation with MVD-CD34 TVBD-CD105 also showed a strong positive correlation with MVD-CD34. VC-CD105 showed a moderate positive correlation with VC-CD34. CD 105 stained fewer but larger caliber vessels. ConclusionsIn this study, vascular parameters showed significant differences in three grades of IDC with CD34. Differences were seen in vascular parameters stained with CD105 in three grades of IDC. Expression of VEGF also showed significant differences with positive correlations in the three grades of IDC. CD34 highlighted both old and newly formed microvessels. CD 105 stained fewer but larger caliber microvessels. VC-CD105 can be an extremely useful adjunct along with VEGF and CD34 to study angiogenesis of vessels in IDC.

Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models

Invasive ductal carcinoma (IDC) breast cancer is a significant health concern for women all around the world and early detection of the disease may increase the survival rate in patients. Therefore, Computer-Aided Diagnosis (CAD) based systems can assist pathologists to detect the disease early. In this study, we present an ensemble model to detect IDC using DenseNet-121 and DenseNet-169 followed by test time augmentation (TTA). The model achieved a balanced accuracy of 92.70% and an F1-score of 95.70% outperforming the current state-of-the-art. Comparative analysis against various pre-trained deep learning models and preprocessing methods have been carried out. Qualitative analysis has also been conducted on the test dataset. After the detection of IDC breast cancer, it is important to grade it for further treatment. In our study, we also propose an ensemble model for the grading of IDC using the pre-trained DenseNet-121, DenseNet-201, ResNet-101v2, and ResNet-50 architectures. The model is inferred from two validation cohorts. For the patch-level classification, the model yielded an overall accuracy of 69.31%, 75.07%, 61.85%, and 60.50% on one validation cohort and 62.44%, 79.14%, 76.62%, and 71.05% on the second validation cohort for 4×, 10×, 20×, and 40× magnified images respectively. The same architecture is further validated using a different IDC dataset where it achieved an overall accuracy of 90.07%. The performance of the models on the detection and grading of IDC shows that they can be useful to help pathologists detect and grade the disease.

Assessment of the immunoexpression profile of breast ductal carcinoma with Cyclin D1, Her-2/Neu and p53 at Yangon General Hospital, Myanmar

One hundred cases of histologically proven invasive ductal carcinomas were histologically graded based on modified Bloom and Richardson Grading. Out of these 17 cases each of low grade, intermediate grade, and high grade invasive ductal carcinomas were selected for Immunostaining using the monoclonal antibodies Cyclin D1,pP53 and Her2/neu. It was found that for all three monoclonal antibodies the lowest histological grade of Invasive Ductal Carcinoma of the Breast showed the lowest positivity with Cyclin D1 ( 11.76%) and p53 ( 17.64%) and Her2/neu ( 47.05%). The intermediate grade tumour showed ( 70.58% ) positivity with Cyclin D1 and 58.58 % in p53 and Her2/neu. The high grade invasive ductal carcinoma of the breast showed the highest positivity of Cyclin D1 (76.47%) , p53 (88.24% ) ,Her2/neu ( 94.12% ); These suggest that Cyclin D1 , P53 and Her2/neuimmunoexpression positivity increases with rising histological grades of invasive ductal carcinoma of breast.

NEK7 Promotes Pancreatic Cancer Progression And Its Expression Is Correlated With Poor Prognosis.

The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients is still dismal. Elucidation of associated genomic alteration may provide effective therapeutic strategies for PDAC treatment. NIMA-related protein kinase 7 is widely expressed in various tumors, including breast cancer, colorectal cancer and lung cancer, and promotes the proliferation of liver cancer cells in vitro and in vivo. We investigated the protein expression level of NEK7 in tumor tissues and adjacent normal tissues using immunohistochemistry of 90 patients with PADC. Meanwhile, the RNA expression level of NEK7 was examined using database-based bioinformatic analysis. Correlation and significance of NEK7 expression with patient clinicopathological features and prognosis were examined. Cell proliferation, cell adhesion, migration and invasion capabilities were measured following downregulation of NEK7 expression. 3D tumor organoids of pancreatic cancer were established and splenic xenografted into nude mice, then liver metastatic ability of NEK7 was evaluated in following 4 weeks. We observed NEK7 expression was upregulated in tumor tissues compared to normal tissues at both RNA and protein levels using bioinformatic analysis and immunohistochemistry analysis in PDAC. NEK7 expression was undetectable in normal pancreatic ducts; NEK7 was overexpressed in primary tumor of PDAC; NEK7 expression was highly correlated with advanced T stage, poorly differentiated histological grade invasive ductal carcinoma, and lymphatic invasion. Meanwhile, patients with higher NEK7 expression accompanied by worse survival outcome. Moreover, NEK7 promoted migration, invasion, adhesion, proliferation and liver metastatic ability of pancreatic cancer cells. Taken together, our data indicate that NEK7 promotes pancreatic cancer progression and it may be a potential marker for PDAC prognosis.

Expression of B-cell Lymphoma 2 in Breast Cancer.

Immunohistochemical expression of B-cell lymphoma 2 (BCL-2) is seen variably in invasive ductal carcinoma. This study was conducted to determine the frequency of BCL-2 expression in different histologic grades of invasive ductal carcinoma. A cross-sectional study was conducted in the Department of Pathology at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Pakistan, on subjects with invasive ductal carcinoma of various histologic grades. Immunohistochemistry was done using the BCL-2 antibody in all cases. The frequency of BCL-2-positive cases in different histologic grades was noted. Post-stratification, the Chi-square test was applied. P ≤ 0.05 was considered statistically significant. All 52 subjects were female (100%) with a mean age of 47.58 ± 1.43 years. BCL-2 expression was observed in 28 (53.85 %) subjects with breast cancer. Out of 33 participants with Grade III, 13 (39.39 %) participants were positive for BCL- 2 expression. Among 18 subjects with Grade II, 14 (77.78 %) subjects were positive for BCL-2 expression. Reduced frequency of BCL-2 expression was observed with increasing histologic grade (i.e., more in low-grade tumours and less in Grade III), but the difference was statistically not significant. A differential expression of BCL-2 was observed across different grades of invasive ductal carcinoma. However, the difference was not statistically significant.

Robinson Cytological Grading of Invasive Ductal Carcinoma of Breast And Comparison with Histological grading

Robinson Cytological Grading of Invasive Ductal Carcinoma of Breast And Comparison with Histological grading