scholarly journals Impact of Pathological Characteristics on Local Relapse After Breast-Conserving Therapy: A Subgroup Analysis of the EORTC Boost Versus No Boost Trial

2009 ◽  
Vol 27 (30) ◽  
pp. 4939-4947 ◽  
Author(s):  
Heather A. Jones ◽  
Ninja Antonini ◽  
Augustinus A.M. Hart ◽  
Johannes L. Peterse ◽  
Jean-Claude Horiot ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Breast Conserving Therapy ◽  
Local Relapse ◽  
High Grade ◽  
Invasive Tumor ◽  
Boost Dose ◽  
Central Pathology ◽  
Pathology Review

Purpose To investigate the long-term impact of pathologic characteristics and an extra boost dose of 16 Gy on local relapse, for stage I and II invasive breast cancer patients treated with breast conserving therapy (BCT). Patients and Methods In the European Organisation for Research and Treatment of Cancer boost versus no boost trial, after whole breast irradiation, patients with microscopically complete excision of invasive tumor, were randomly assigned to receive or not an extra boost dose of 16 Gy. For a subset of 1,616 patients central pathology review was performed. Results The 10-year cumulative risk of local breast cancer relapse as a first event was not significantly influenced if the margin was scored negative, close or positive for invasive tumor or ductal carcinoma in situ according to central pathology review (log-rank P = .45 and P = .57, respectively). In multivariate analysis, high-grade invasive ductal carcinoma was associated with an increased risk of local relapse (P = .026; hazard ratio [HR], 1.67), as was age younger than 50 years (P < .0001; HR, 2.38). The boost dose of 16 Gy significantly reduced the local relapse rate (P = .0006; HR, 0.47). For patients younger than 50 years old and in patients with high grade invasive ductal carcinoma, the boost dose reduced the local relapse from 19.4% to 11.4% (P = .0046; HR, 0.51) and from 18.9% to 8.6% (P = .01; HR, 0.42), respectively. Conclusion Young age and high-grade invasive ductal cancer were the most important risk factors for local relapse, while margin status had no significant influence. A boost dose of 16 Gy significantly reduced the negative effects of both young age and high-grade invasive cancer.

Introducing a new scoring system based on HER2/neu, p53 oncogenes results and mammographic findings for prediction of tumor grade in breast cancer

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 10687-10687
Author(s):  
M. Guity ◽  
M. Mokri ◽  
M. Shakiba ◽  
M. Atri
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Predictive Value ◽  
Scoring System ◽  
Ductal Carcinoma ◽  
Tumor Grade ◽  
High Grade ◽  
Group Iii ◽  
Immunohistochemical Studies ◽  
New Scoring

10687 Background: During recent years, several oncogenes have been introduced in relation to breast cancer including her2 and P53. These are related with initiation, degree of progression, invasion and prognosis of breast cancer. In this research the rate of positivity of these oncogenes in 150 patients with invasive ductal carcinoma is introduced and the correlation of the results of a new scoring system comprised of mammograms, P53 and her2 with the tumor grade is assessed. Methods: 150 cases of invasive ductal carcinoma of a private clinic were chosen. The diagnosis of cancer and its grade was confirmed by pathology. All patients underwent mammography befor surgery and the results were classified according to BIRADS system to benign (groups of I and II in BIRADS): Score I, suspicious (group III in BIRADS): Score II and malignant (groups of IV and V in BIRADS): score III. P53 and her2 presence were assessed by immunohistochemical studies and the results were classified as negative (score I) and positive (score II). The final score of each patients was calculated by adding scores of all three studies (P53, her2, mammography) which ranged between 3 to 7. Results: The average age of the patients was 48.2 + 11.2 years; most of them were between 30–50 years old. Three had benign mammograms (2%), 81 had suspicious (54%) and 66 had malignant findings (44%). We showed positive P53in 59 (39%), positive her2 in 69 (46%) and high grade tumor in 77 (51%) patients. On scoring, 2 patients gained 3 (1.3%), 36 scored 4 (24%), 53 patients received 5 (35.3%), 37 reached 6 scores (24.7%) and 22 patients received 7 scores (14.7%). Placing scores 3 and 4 in one group and scores 5–7 in another, the sensitivity and negative predictive value of the system for high grade tumors reached 97.7% and 89.5% respectively. By placing scores 3–6 in one group and score 7 in another, the specificity and positive predictive value of the system reached to 100%. Conclusions: The results of mammography, P53 and her2 seems to have a good correlation with tumor grade, meaning when all three parameters are positive, the patients’ tumor is almost always high grade. No significant financial relationships to disclose.

scholarly journals Lactating Adenoma Co-Existing with High Grade Invasive Ductal Carcinoma; Collision Tumour or Mere Co-Incidence

2020 ◽  
Vol 5 (12) ◽  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Young Patient ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Positive Family History ◽  
Tumour Cells ◽  
High Grade ◽  
Collision Tumour ◽  
History Of

Background: Lactating adenoma are benign lesions that can presents as a solitary or multiple freely movable breast mass during pregnancy or puerperium. The lesion is actually a localized focus of hyperplasia in the lactating breast, which may also develop in ectopic locations such as the axilla, chest wall, or vulva. Breast cancer developing during pregnancy or puerperium is known as pregnancy associated breast cancer. We report a case of lactating adenoma co-existing with high grade invasive ductal carcinoma in young patient in puerperium with a positive family history of breast cancer. We present a 19-year-old female with a palpable mass on her right upper outer quadrant of her right breast measuring 5x4x2cm with ipsilateral supraclavicular lymph node enlargement. Cytomorphology of the lesion showed tumour cells arranged in nests and solid sheets with abundant fibromyxoid stroma. Also seen are abnormal mitosis and areas of lymphovascular invasion. Proliferating glands are seen lined by cuboidal cells with cytoplasmic vacuolations. Immunohistochemical stain show tumour cells were triple negative (negative for progesterone receptor (PR), estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) and strongly positive for EMA in both tumours. Conclusion: This study indicated that lactating adenoma can co-exist with high grade invasive ductal carcinoma in a young patient in puerperium. The fact that this patient has a positive family history of breast cancer in first degree relative may explain the presentation at a very young age. It may be very difficult to ascertain whether this is a collision tumour or a mere co-incidence of lactating adenoma with breast cancer in this patient.

Invasive Ductal Carcinoma Detection in Histopathology Images Using Machine Learning Techniques Without Deploying Feature Extraction

2020 ◽  
Vol 17 (6) ◽  
pp. 2589-2595
Author(s):  
Isha Gupta ◽  
Sheifali Gupta ◽  
Swati Singh
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Imaging Techniques ◽  
New Method ◽  
Machine Learning Techniques ◽  
Invasive Tumor ◽  
Histopathology Images

Breast cancer is one of the common malignant diseases in female all over the world. Microscopic investigation of tissues in breast is essential for analysis of breast cancer. For detection of breast cancer, pathologist uses various magnificent stages for obtaining accurate diagnosis of biopsy images which is time consuming. Development in digital imaging techniques has helped in assessment of pathology images using machine learning and computerized methods which could computerize a few of the pathology stages in the diagnosis of breast cancer. This kind of automation can be helpful in achieving quick and exact results reducing the observers’ inconsistency, thus increasing the accuracy. In this work, a new method is proposed to categorize breast cancer histopathology images. The objective is to evaluate the robustness and accuracy of a classification system based on machine learning, to automatically identify invasive tumor on digitized images without extracting the features. Here, a new method is presented that employs machine learning classifiers for classification of invasive tumor on whole slide images. The accuracy of different classifiers varies from 80% to 85%, leaving scope for improvement. The aim is to gather different researchers in both machine learning and medical field to proceed toward this Computer Aided Diagnosis (CAD) system for classification of invasive ductal carcinoma (IDC).

Expression of Let-7c in invasive ductal carcinoma of breast cancer and its clinical significance

2009 ◽  
Vol 29 (4) ◽  
pp. 400-403
Author(s):  
Shu-rong SHEN ◽  
Jun-yi SHI ◽  
Xian SHEN ◽  
Guan-li HUANG ◽  
Xiang-yang XUE
Keyword(s):  
Breast Cancer ◽  
Clinical Significance ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma

scholarly journals Relevant impact of central pathology review on nodal classification in individual breast cancer patients

2012 ◽  
Vol 23 (10) ◽  
pp. 2561-2566 ◽  
Author(s):  
J.H.M.J. Vestjens ◽  
M.J. Pepels ◽  
M. de Boer ◽  
G.F. Borm ◽  
C.H. M. van Deurzen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Central Pathology ◽  
Pathology Review

Uncommon Breast Malignancies: Presentation Pattern, Prognostic Issue and Treatment Outcome in an Italian Single Institution Experience

2013 ◽  
Vol 99 (1) ◽  
pp. 39-44
Author(s):  
Claudia Maria Regina Bareggi ◽  
Dario Consonni ◽  
Barbara Galassi ◽  
Donatella Gambini ◽  
Elisa Locatelli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Treatment Outcome ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Early Stage ◽  
Medullary Carcinoma ◽  
Mucinous Carcinoma ◽  
Lymph Node Involvement ◽  
Tubular Carcinoma ◽  
Breast Malignancies

Aims and background Often neglected by large clinical trials, patients with uncommon breast malignancies have been rarely analyzed in large series. Patients and methods Of 2,052 patients diagnosed with breast cancer and followed in our Institution from January 1985 to December 2009, we retrospectively collected data on those with uncommon histotypes, with the aim of investigating their presentation characteristics and treatment outcome. Results Rare histotypes were identified in 146 patients (7.1% of our total breast cancer population), being classified as follows: tubular carcinoma in 75 (51.4%), mucinous carcinoma in 36 (24.7%), medullary carcinoma in 25 (17.1%) and papillary carcinoma in 10 patients (6.8%). Whereas age at diagnosis was not significantly different among the diverse diagnostic groups, patients with medullary and papillary subtypes had a higher rate of lymph node involvement, similar to that of invasive ductal carcinoma. Early stage diagnosis was frequent, except for medullary carcinoma. Overall, in comparison with our invasive ductal carcinoma patients, those with rare histotypes showed a significantly lower risk of recurrence, with a hazard ratio of 0.28 (95% CI, 0.12–0.62; P = 0.002). Conclusions According to our analysis, patients with uncommon breast malignancies are often diagnosed at an early stage, resulting in a good prognosis with standard treatment.

StackBC: Deep learning and transfer learning techniques based stacking approach for accurate Invasive Ductal Carcinoma classification using histology images

2022 ◽  
pp. 1-12
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Samad Wali ◽  
Sultan Ahmad ◽  
Zafar Ali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Transfer Learning ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Early Recognition ◽  
Predictive Performance ◽  
Operating Characteristics ◽  
Data Set ◽  
Learning Techniques

Artificial intelligence (AI) based computer-aided diagnostic (CAD) systems can effectively diagnose critical disease. AI-based detection of breast cancer (BC) through images data is more efficient and accurate than professional radiologists. However, the existing AI-based BC diagnosis methods have complexity in low prediction accuracy and high computation time. Due to these reasons, medical professionals are not employing the current proposed techniques in E-Healthcare to effectively diagnose the BC. To diagnose the breast cancer effectively need to incorporate advanced AI techniques based methods in diagnosis process. In this work, we proposed a deep learning based diagnosis method (StackBC) to detect breast cancer in the early stage for effective treatment and recovery. In particular, we have incorporated deep learning models including Convolutional neural network (CNN), Long short term memory (LSTM), and Gated recurrent unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, data augmentation and transfer learning techniques have been incorporated for data set balancing and for effective training the model. To further improve the predictive performance of model we used stacking technique. Among the three base classifiers (CNN, LSTM, GRU) the predictive performance of GRU are better as compared to individual model. The GRU is selected as a meta classifier to distinguish between Non-IDC and IDC breast images. The method Hold-Out has been incorporated and the data set is split into 90% and 10% for training and testing of the model, respectively. Model evaluation metrics have been computed for model performance evaluation. To analyze the efficacy of the model, we have used breast histology images data set. Our experimental results demonstrated that the proposed StackBC method achieved improved performance by gaining 99.02% accuracy and 100% area under the receiver operating characteristics curve (AUC-ROC) compared to state-of-the-art methods. Due to the high performance of the proposed method, we recommend it for early recognition of breast cancer in E-Healthcare.

scholarly journals 925 Utilizing multiplex immunofluorescence to explore the epithelial-mesenchymal transition in breast, ovarian cancers

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A970-A970
Author(s):  
Danielle Fails ◽  
Michael Spencer
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Poor Prognosis ◽  
Epithelial Mesenchymal Transition ◽  
Ductal Carcinoma ◽  
Beta Catenin ◽  
Mesenchymal Transition ◽  
Cell Counts ◽  
Grade Ii ◽  
E Cadherin

BackgroundEpithelial-mesenchymal transition (EMT) is instrumental during embryonic development—assisting in extensive movement and differentiation of cells. However, during metastasis and tumorigenesis, this process is hijacked. The disruption of this developmental process, and subsequent acquisition of a mesenchymal phenotype, has been shown to increase therapeutic resistance and often leads to poor prognosis in breast cancer.1 Using bioinformatic resources and current clinical data, we designed a panel of biomarkers of value to specifically observe this epithelial/mesenchymal transition.MethodsHuman breast cancer FFPE tissue samples were stained with Bethyl Laboratories IHC-validated primary antibodies, followed by Bethyl HRP-conjugated secondary antibodies, and detected using Akoya Opal™ Polaris 7-color IHC kit fluorophores (Akoya Biosciences [NEL861001KT]). The panel consisted of beta-Catenin, E-Cadherin, Ki67, CD3e, PD-L1, and FOXP3. Antibody staining order was optimized using tissue microarray serial sections, three slides per target, and stained in either the first, third, or sixth position via heat-induced epitope retrieval (HIER) methods. Exposure time was maintained for all three slides/target and cell counts, signal intensity, background, and autofluorescence were analyzed. The final optimized order was then tested on the breast cancer microarray in seven-color mIF. Whole slide scans were generated using the Vectra Polaris® and analyses performed using InForm® and R® Studio.ResultsTwo integral EMT targets, E-Cadherin and beta-Catenin, were used to observe a key occurrence in this transition. Under tumorigenic circumstances, when released from the complex they form together (E-cadherin-B-catenin complex), Beta-catenin can induce EMT. This disjunction/activation of EMT can be seen in the invasive ductal carcinoma below (figure 1).The disorganized E-cadherin cells are in direct contrast to normal, non-cancerous cells in similar tissue. Total CD3e cell counts were down (2%), with 35% cells restricted to the stroma vs. the 1% seen intra-tumorally. Coupled with the elevated presence of Ki67 (10%), a level of rapid cancer growth and potential metastasis (Invasive Ductal Carcinoma Grade II) can be observed.Abstract 925 Figure 1Invasive ductal carcinoma, grade II stained with a 6-plex mIF panel designed to show the epithelial-mesenchymal transitionConclusionsThe presence of EMT in breast cancers is often indicative of a poor prognosis, so the need for reliable markers is imperative. E-Cadherin and beta-Catenin are both up-and-coming clinical targets that can serve to outline this transition within the tumor microenvironment. By utilizing these markers in mIF, closer spatial examination of proteins of interest can be achieved. The application of this mIF panel has the potential to provide invaluable insights into how tumor infiltrating lymphocytes behave in cancers exhibiting the hallmarks of EMT.AcknowledgementsWe would like to acknowledge Clemens Deurrschmid, PhD, Technical Applications Scientist Southeast/South Central, Akoya Biosciences for his assistance with image analysis.ReferencesHorne HN, Oh H, Sherman ME, et al. E-cadherin breast tumor expression, risk factors and survival: pooled analysis of 5,933 cases from 12 studies in the breast cancer association consortium. Sci Rep 2018;8:6574.

scholarly journals The Utilization of Physics Parameter to Classify Histopathology Types of Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) by using K-Nearest Neighbourhood (KNN) Method

2018 ◽  
Vol 8 (4) ◽  
pp. 2442
Author(s):  
Anak Agung Ngurah Gunawan ◽  
I Wayan Supardi ◽  
S. Poniman ◽  
Bagus G. Dharmawan
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Invasive Lobular Carcinoma ◽  
Ductal Carcinoma ◽  
Lobular Carcinoma ◽  
Diagnose Breast Cancer ◽  
Object Based ◽  
Computer Aided ◽  
The Mean ◽  
Learning Data

<p>Medical imaging process has evolved since 1996 until now. The forming of Computer Aided Diagnostic (CAD) is very helpful to the radiologists to diagnose breast cancer. KNN method is a method to do classification toward the object based on the learning data which the range is nearest to the object. We analysed two types of cancers IDC dan ILC. 10 parameters were observed in 1-10 pixels distance in 145 IDC dan 7 ILC. We found that the Mean of Hm(yd,d) at 1-5 pixeis the only significant parameters that distingguish IDC and ILC. This parameter at 1-5 pixels should be applied in KNN method. This finding need to be tested in diffrerent areas before it will be applied in cancer diagnostic.</p>

Sign in / Sign up

Export Citation Format

Share Document