scholarly journals Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images

2020 ◽  
Author(s):  
Mohammad Abdolahi ◽  
Mohammad Salehi ◽  
Iman Shokatian ◽  
Reza Reiazi
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Digital Pathology ◽  
Automatic Classification ◽  
Carcinoma Breast

Histological classification of invasive ductal carcinoma and the biological parameters in breast cancer

Breast Cancer ◽  
2003 ◽  
Vol 10 (2) ◽  
pp. 149-152 ◽  
Author(s):  
Shinichi Tsutsui ◽  
Shinji Ohno ◽  
Shigeru Murakami ◽  
Akemi Kataoka ◽  
Junko Kinoshita ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Histological Classification ◽  
Biological Parameters

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 Fully automatic classification of breast cancer microarray images

2016 ◽  
Vol 3 (2) ◽  
pp. 348-359 ◽  
Author(s):  
Nastaran Dehghan Khalilabad ◽  
Hamid Hassanpour ◽  
Mohammad Reza Abbaszadegan
Keyword(s):  
Breast Cancer ◽  
Automatic Classification ◽  
Fully Automatic

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 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.

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