Detection Of Invasive Ductal Carcinoma Research Articles

MicroRNA signatures differentiate types, grades, and stages of breast invasive ductal carcinoma (IDC): miRNA-target interacting signaling pathways

BackgroundInvasive ductal carcinoma (IDC) is the most common form of breast cancer which accounts for 85% of all breast cancer diagnoses. Non-invasive and early stages have a better prognosis than late-stage invasive cancer that has spread to lymph nodes. The involvement of microRNAs (miRNAs) in the initiation and progression of breast cancer holds great promise for the development of molecular tools for early diagnosis and prognosis. Therefore, developing a cost effective, quick and robust early detection protocol using miRNAs for breast cancer diagnosis is an imminent need that could strengthen the health care system to tackle this disease around the world.MethodsWe have analyzed putative miRNAs signatures in 100 breast cancer samples using two independent high fidelity array systems. Unique and common miRNA signatures from both array systems were validated using stringent double-blind individual TaqMan assays and their expression pattern was confirmed with tissue microarrays and northern analysis. In silico analysis were carried out to find miRNA targets and were validated with q-PCR and immunoblotting. In addition, functional validation using antibody arrays was also carried out to confirm the oncotargets and their networking in different pathways. Similar profiling was carried out in Brca2/p53 double knock out mice models using rodent miRNA microarrays that revealed common signatures with human arrays which could be used for future in vivo functional validation.ResultsExpression profile revealed 85% downregulated and 15% upregulated microRNAs in the patient samples of IDC. Among them, 439 miRNAs were associated with breast cancer, out of which 107 miRNAs qualified to be potential biomarkers for the stratification of different types, grades and stages of IDC after stringent validation. Functional validation of their putative targets revealed extensive miRNA network in different oncogenic pathways thus contributing to epithelial-mesenchymal transition (EMT) and cellular plasticity.ConclusionThis study revealed potential biomarkers for the robust classification as well as rapid, cost effective and early detection of IDC of breast cancer. It not only confirmed the role of these miRNAs in cancer development but also revealed the oncogenic pathways involved in different progressive grades and stages thus suggesting a role in EMT and cellular plasticity during breast tumorigenesis per se and IDC in particular. Thus, our findings have provided newer insights into the miRNA signatures for the classification and early detection of IDC.

Optimized Bayesian convolutional neural networks for invasive breast cancer diagnosis system

Deep learning (DL) has depicted unprecedented results in the diagnosis of invasive ductal carcinoma (IDC). However, most DL methods are not able to infer the uncertainty in their predictions, and are often too confident which may cause undesirable consequences. The ability of DL models to express their uncertainty in their predictions is particularly important in diagnosing diseases. Therefore, this paper proposes an uncertainty-aware approach called optimized Bayesian convolutional neural network (OBCNN) for the automated detection of IDC from histopathology images and is capable of estimating and inferring the uncertainty in its predictions. The proposed OBCNN employs an approximate Bayesian version of a pre-trained convolutional neural network (CNN) model named ResNet101V2 that is fine-tuned to the histopathology images of IDC. The Bayesian approximation is performed by adding Monte Carlo dropout (MC-dropout) to ResNet101V2 architecture. Due to the effect of MC-dropout on the obtained posterior predictive distribution (PPD), the value of MC-dropout should be chosen carefully because if the MC-dropout value is too large, the predictive distribution (PD) will be very diverse. Whereas if the MC-dropout value is very small, the PD will be very similar. Therefore, an optimization algorithm known as the slime mould algorithm is utilized to set the optimal value of the MC-dropout. During inference, the proposed OBCNN runs for T forward passes to configure the PPD. The mean, standard deviation (SD) and entropy are then calculated over the obtained PD to estimate the predictive uncertainty (PU) of the OBCNN. The proposed OBCNN achieved an accuracy of 93.83%, a precision of 96.14%, a G-mean of 93.04%, False Negative Rate of 4.72%, and False Positive Rate of 9.96 %. The proposed OBCNN has also been compared with recent related work, the comparison’s results demonstrated that OBCNN performed better in the diagnosis of IDC.

Proteomics-Based Identification of Dysregulated Proteins and Biomarker Discovery in Invasive Ductal Carcinoma, the Most Common Breast Cancer Subtype

Invasive ductal carcinoma (IDC) is the most common histological subtype of malignant breast cancer (BC), and accounts for 70-80% of all invasive BCs. IDC demonstrates great heterogeneity in clinical and histopathological characteristics, prognoses, treatment strategies, gene expressions, and proteomic profiles. Significant proteomic determinants of the progression from intraductal pre-invasive malignant lesions of the breast, which characterize a ductal carcinoma in situ (DCIS), to IDC, are still poorly identified, validated, and clinically applied. In the era of "6P" medicine, it remains a great challenge to determine which patients should be over-treated versus which need to be actively monitored without aggressive treatment. The major difficulties for designating DCIS to IDC progression may be solved by understanding the integrated genomic, transcriptomic, and proteomic bases of invasion. In this review, we showed that multiple proteomics-based techniques, such as LC-MS/MS, MALDI-ToF MS, SELDI-ToF-MS, MALDI-ToF/ToF MS, MALDI-MSI or MasSpec Pen, applied to in-tissue, off-tissue, BC cell lines and liquid biopsies, improve the diagnosis of IDC, as well as its prognosis and treatment monitoring. Classic proteomics strategies that allow the identification of dysregulated protein expressions, biological processes, and interrelated pathway analyses based on aberrant protein-protein interaction (PPI) networks have been improved to perform non-invasive/minimally invasive biomarker detection of early-stage IDC. Thus, in modern surgical oncology, highly sensitive, rapid, and accurate MS-based detection has been coupled with "proteome point sampling" methods that allow for proteomic profiling by in vivo "proteome point characterization", or by minimal tissue removal, for ex vivo accurate differentiation and delimitation of IDC. For the detection of low-molecular-weight proteins and protein fragments in bodily fluids, LC-MS/MS and MALDI-MS techniques may be coupled to enrich and capture methods which allow for the identification of early-stage IDC protein biomarkers that were previously invisible for MS-based techniques. Moreover, the detection and characterization of protein isoforms, including posttranslational modifications of proteins (PTMs), is also essential to emphasize specific molecular mechanisms, and to assure the early-stage detection of IDC of the breast.

Point‐of‐care devices based on fluorescence imaging and spectroscopy for tumor margin detection during breast cancer surgery: Towards breast conservation treatment

Fluorescence-based methods are highly specific and sensitive and have potential in breast cancer detection. Simultaneous fluorescence imaging and spectroscopy during intraoperative procedures of breast cancer have great advantages in detection of tumor margin as well as in classification of tumor to healthy tissues. Intra-operative real-time confirmation of breast cancer tumor margin is the aim of surgeons, and therefore, there is an urgent need for such techniques and devices which fulfill the surgeon's priorities. In this article, we propose the development of fluorescence-based smartphone imaging and spectroscopic point-of-care multi-modal devices for detection of invasive ductal carcinoma in tumor margin during removal of tumor. These multimodal devices are portable, cost-effective, noninvasive, and user-friendly. Molecular level sensitivity of fluorescence process shows different behavior in normal, cancerous and marginal tissues. We observed significant spectral changes, such as, red-shift, full-width half maximum (FWHM), and increased intensity as we go towards tumor center from normal tissue. High contrast in fluorescence images and spectra are also recorded for cancer tissues compared to healthy tissues. Preliminary results for the initial trial of the devices are reported in this article. A total 44 spectra from 11 patients of invasive ductal carcinoma (11 spectra for invasive ductal carcinoma and rest are normal and negative margins) are used. Principle component analysis is used for the classification of invasive ductal carcinoma with an accuracy of 93%, specificity of 75% and sensitivity of 92.8%. We obtained an average 6.17 ± 1.66 nm red shift for IDC with respect to normal tissue. The red shift and maximum fluorescence intensity indicates p < 0.01. These results described here are supported by histopathological examination of the same sample. In the present manuscript, simultaneous fluorescence-based imaging and spectroscopy is accomplished for the classification of IDC tissues and breast cancer margin detection.

Recurrence Network Analysis of Histopathological Images for the Detection of Invasive Ductal Carcinoma in Breast Cancer.

The histopathological image analysis is one of the most crucial diagnostic procedures to identify Invasive ductal carcinoma (IDC) in breast cancers. However, this diagnosis process is currently time-consuming and heavily dependent on human expertise. Prior research has shown that different degrees of tumors present various microstructures in the histopathological images. However, very little has been done to utilize spatial recurrence features of microstructures for identifying IDC. This paper presents a novel recurrence analysis methodology for automatic image-guided IDC detection. We first utilize wavelet decomposition to delineate the subtle information in the images. Then, we model the patches with a weighted recurrence network approach to characterize the recurrence patterns of the histopathological images. Finally, we develop automated IDC detection models leveraging machine learning methods with spatial recurrence features extracted. The developed recurrence analysis models successfully characterize the complex microstructures of histopathological images and achieve the IDC detection performances of at least AUC = 0.96. This research developed a spatial recurrence analysis methodology to effectively identify IDC regions in histopathological images for BC. It shows a high potential to assist physicians in the decision-making process. The proposed methodology can further be applicable to image processing for other medical or biological applications.

An interpretable semi-supervised framework for patch-based classification of breast cancer

Developing effective invasive Ductal Carcinoma (IDC) detection methods remains a challenging problem for breast cancer diagnosis. Recently, there has been notable success in utilizing deep neural networks in various application domains; however, it is well-known that deep neural networks require a large amount of labelled training data to achieve high accuracy. Such amounts of manually labelled data are time-consuming and expensive, especially when domain expertise is required. To this end, we present a novel semi-supervised learning framework for IDC detection using small amounts of labelled training examples to take advantage of cheap available unlabeled data. To gain trust in the prediction of the framework, we explain the prediction globally. Our proposed framework consists of five main stages: data augmentation, feature selection, dividing co-training data labelling, deep neural network modelling, and the interpretability of neural network prediction. The data cohort used in this study contains digitized BCa histopathology slides from 162 women with IDC at the Hospital of the University of Pennsylvania and the Cancer Institute of New Jersey. To evaluate the effectiveness of the deep neural network model used by the proposed approach, we compare it to different state-of-the-art network architectures; AlexNet and a shallow VGG network trained only on the labelled data. The results show that the deep neural network used in our proposed approach outperforms the state-of-the-art techniques achieving balanced accuracy of 0.73 and F-measure of 0.843. In addition, we compare the performance of the proposed semi-supervised approach to state-of-the-art semi-supervised DCGAN technique and self-learning technique. The experimental evaluation shows that our framework outperforms both semi-supervised techniques and detects IDC with an accuracy of 85.75%, a balanced accuracy of 0.865, and an F-measure of 0.773 using only 10% labelled instances from the training dataset while the rest of the training dataset is treated as unlabeled.

The Use of Convolutional Neural Networks in the Prediction of Invasive Ductal Carcinoma in Histological Images of Breast Cancer.

This paper presents a deep learning approach for automatic detection and visual analysis of Invasive Ductal Carcinoma (IDC) tissue regions. The method proposed in this work is a convolutional neural network (CNN) for visual semantic analysis of tumor regions for diagnostic support. Detection of IDC is a time-consuming and challenging task, mainly because a pathologist needs to examine large tissue regions to identify areas of malignancy. Deep Learning approaches are particularly suitable for dealing with this type of problem, especially when many samples are available for training, ensuring high quality of the learned features by the classifier and, consequently, its generalization capacity. A 3-hidden-layer CNN with data balancing reached both accuracy and F1-Score of 0.85 and outperforming other approaches from the literature. Thus, the proposed method in this article can serve as a support tool for the identification of invasive breast cancer.

Architectures Based on Deep Learning for the Detection of Invasive Ductal Carcinoma

Image processing techniques have improved dramatically in recent years to help pathologists identify cancer cells. Like convolutional neural networks (CNNs), deep learning methods are increasingly used for imaging processes and analysis in histopathology images. This research aims to demonstrate the detection of histopathological pictures linked with the prediction of invasive ductal carcinomas (IDC) and non-IDC in the breasts. A complex problem is the detection of IDC in histopathological images, as cancer includes minor entities with a variety of shapes that can readily be confused with other objects or facts in the picture. As a result, the suggested research recommends three distinct CNN architectures for identifying IDC using histopathological images, referred to as 10-layer, 19-layer, and 20-layer convolutional neural networks, respectively. Excellent values of sensitivity, precision, and low classification error rate have been achieved to detect IDC in histopathology images using deep layer-convolutional neural networks. Using 19 layer-convolutional neural networks, the efficiency obtained was 87%, revealing improved results for deep layer convolutional neural network architecture.

Classification and enhancement of invasive ductal carcinoma samples using convolutional neural networks

In the medical area, the detection of invasive ductal carcinoma is the most common sub-type of all breast cancers; about 80% of all breast cancers are invasive ductal carcinomas. Detection of this type of cancer shows a great challenge for specialist doctors since the digital images of the sample must be analyzed by sections because the spatial dimensions of this kind of image are above 50k × 50k pixels; doing this operation manually takes long time to determine if the patient suffers this type of cancer. Time is essential for the patient because this cancer can invade quickly other parts of the body. Its name reaffirms this characteristic, with the term "invasive" forming part of its name. With the purpose of solving this task, we propose an automatic methodology consisting in improving the performance of a convolutional neural network that classifies images containing invasive ductal carcinoma cells by highlighting cancer cells using several preprocessing methods such as histogram stretching and contrast enhancement. In this way, characteristics of the sub-images are extracted from the panoramic sample and it is possible to learn to classify them in a better way.

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.

Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features.

Breast cancer, like most forms of cancer, is a fatal disease that claims more than half a million lives every year. In 2020, breast cancer overtook lung cancer as the most commonly diagnosed form of cancer. Though extremely deadly, the survival rate and longevity increase substantially with early detection and diagnosis. The treatment protocol also varies with the stage of breast cancer. Diagnosis is typically done using histopathological slides from which it is possible to determine whether the tissue is in the Ductal Carcinoma In Situ (DCIS) stage, in which the cancerous cells have not spread into the encompassing breast tissue, or in the Invasive Ductal Carcinoma (IDC) stage, wherein the cells have penetrated into the neighboring tissues. IDC detection is extremely time-consuming and challenging for physicians. Hence, this can be modeled as an image classification task where pattern recognition and machine learning can be used to aid doctors and medical practitioners in making such crucial decisions. In the present paper, we use an IDC Breast Cancer dataset that contains 277,524 images (with 78,786 IDC positive images and 198,738 IDC negative images) to classify the images into IDC(+) and IDC(-). To that end, we use feature extractors, including textural features, such as SIFT, SURF and ORB, and statistical features, such as Haralick texture features. These features are then combined to yield a dataset of 782 features. These features are ensembled by stacking using various Machine Learning classifiers, such as Random Forest, Extra Trees, XGBoost, AdaBoost, CatBoost and Multi Layer Perceptron followed by feature selection using Pearson Correlation Coefficient to yield a dataset with four features that are then used for classification. From our experimental results, we found that CatBoost yielded the highest accuracy (92.55%), which is at par with other state-of-the-art results—most of which employ Deep Learning architectures. The source code is available in the GitHub repository.

Automatic Detection of Invasive Ductal Carcinoma Based on the Fusion of Multi-Scale Residual Convolutional Neural Network and SVM

Invasive ductal carcinoma (IDC) is the most common type of breast cancer which is the leading cause of cancer-related deaths in middle-aged women. Pathological analysis of biopsy is the gold standard for diagnosis of breast cancer, and early detection, diagnosis, and treatment can significantly increase the survival rate. This paper proposes a method for the automatic detection of IDC based on the fusion of multi-scale residual convolutional neural network (MSRCNN) and SVM. First, the patches from whole slide images (WSI) were preprocessed by data enhancement and normalization and then input into the MSRCNN for features extraction. Second, the extracted features were input to the SVM and are classified into two categories: healthy and diseased patches. Finally, it is restored to the WSI according to the coordinate information of the patches, therefore the IDC and healthy tissue regions were built. Experimental results show that after 5-fold cross-validation, our method obtained an average accuracy of 87.45±0.81%, an average balance accuracy of 85.7±0.95%, and an average F1 score of 79.89±1.11%. Consequently, it has important practical value and scientific research significance.

Different CNN-based Architectures for Detection of Invasive Ductal Carcinoma in Breast Using Histopathology Images

In recent years, many improvements have been made in image processing techniques which aid pathologists to identify cancer cells. Nowadays, convolutional neural networks (CNNs), also known as deep learning algorithms have become popular for the applications of image processing and examination in histopathology image (tissue and cell images). This study aims to present the detection of histopathology images associated to detection of invasive ductal carcinoma (IDC) and non-IDC in breast. However, detection of IDC is a challenging task in histopathology image which needs deep examination as cancer comprises of minor entities with a diversity of forms which can be easily mixed up with different objects or facts contained in image. Hence, the proposed study suggests three types of CNN architectures which is called 8-layer CNNs, 9-layer CNNs and 19-layer CNNs, respectively, in the detecting IDC using histopathology images. The purpose of the study is to identify IDC from histopathology images by taking proper layer in deep layer CNNs with the maximum accuracy, highest sensitivity, precision and least classification error. The result shows better performance for deep layer-convolutional neural networks architecture by using 19-layer CNNs.

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

Background: Breast cancer is one of the most causes of death in women. Early diagnosis and detection of Invasive Ductal Carcinoma (IDC) is an important key for the treatment of IDC. Computer-aided approaches have great potential to improve diagnosis accuracy. In this paper, we proposed a deep learning-based method for the automatic classification of IDC in whole slide images (WSI) of breast cancer. Furthermore, different types of deep neural networks training such as training from scratch and transfer learning to classify IDC were evaluated. Methods: In total, 277524 image patches with 50×50-pixel size form original images were used for model training. In the first method, we train a simple convolutional neural network (named it baseline model) on these images. In the second approach, we used the pre-trained VGG-16 CNN model via feature extraction and fine-tuning for the classification of breast pathology images. Results: Our baseline model achieved a better result for the automatic classification of IDC in terms of F-measure and accuracy (83%, 85%) in comparison with original paper on this data set and achieved a comparable result with a new study that introduced accepted-rejected pooling layer. Also, transfer learning via feature extraction yielded better results (81%, 81%) in comparison with handcrafted features. Furthermore, transfer learning via feature extraction yielded better classification results in comparison with the baseline model. Conclusion: The experimental results demonstrate that using deep learning approaches yielded better results in comparison with handcrafted features. Also, using transfer learning in histopathology image analysis yielded significant results in comparison with training from scratch in much less time.

Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images

Advances in artificial intelligence technologies have made it possible to obtain more accurate and reliable results using digital images. Due to the advances in digital histopathological images obtained using whole slide image (WSI) scanners, automated analysis of digital images by computer support systems has become interesting. In particular, deep learning architectures, are one of the preferred approaches in the analysis of digital histopathology images. The deeper networks trained on large amounts of image data are adapted for different tasks using transfer learning technique. In this study, automated detection of invasive ductal carcinoma (IDC), which is the most common subtype of breast cancers, is proposed using deep transfer learning technique. We have used deep learning pre-trained models, ResNet-50 and DenseNet-161 for the IDC detection task. The public histopathology dataset containing 277,524 image patches were used in our experimental studies. As a result of training on the last layers of pre-trained deep networks, DenseNet-161 model has yielded F-sore of 92.38% and balanced accuracy value of 91.57%. Similarly, we have obtained F-score of 94.11% and balanced accuracy value of 90.96% using ResNet-50 architecture. In addition, our developed model is validated using the publicly available BreakHis breast cancer dataset and obtained promising results in classifying magnification independent histopathology images into benign and malignant classes. Our developed system obtained the highest classification performance as compared to the state-of-art techniques and is ready to be tested with more diverse huge databases.

Detection Rates for Benign and Malignant Diagnoses on Breast Cancer Screening With Digital Breast Tomosynthesis in a Statewide Mammography Registry Study.

The objective of our study was to determine whether detection rates of specific benign and malignant diagnoses differ for breast cancer screening with digital breast tomosynthesis (DBT) versus full-field digital mammography (FFDM) alone. We analyzed observational data from the Vermont Breast Cancer Surveillance System, including 86,349 DBT screening examinations and 97,378 FFDM screening examinations performed at eight radiology facilities in Vermont that adopted DBT screening during 2012-2016. We determined the most severe diagnosis made within 6 months after positive screening examinations. Multivariable-adjusted logistic regression was used to compare detection rates for specific diagnoses on DBT versus FFDM. Compared with FFDM, DBT had a lower recall rate (adjusted odds ratio [OR], 0.81; 95% CI, 0.77-0.85) but comparable biopsy rate (OR = 1.05; 95% CI, 0.93-1.17), benign biopsy rate (OR = 1.12; 95% CI, 0.97-1.29), and cancer detection rate (OR = 0.94; 95% CI, 0.78-1.14). Among benign diagnoses, DBT and FFDM had comparable detection rates for nonproliferative lesions (OR = 1.19; 95% CI, 0.92-1.53), fibroepithelial proliferations (OR = 1.24; 95% CI, 0.85-1.81), proliferative lesions without atypia (OR = 1.13; 95% CI, 0.90-1.42), atypical lesions (OR = 0.77; 95% CI, 0.43-1.38), and lobular carcinoma in situ (LCIS) (OR = 0.92; 95% CI, 0.53-1.61). Among malignant diagnoses, DBT and FFDM had comparable detection rates for ductal carcinoma in situ (OR = 1.05; 95% CI, 0.70-1.57) and invasive breast cancer (OR = 0.92; 95% CI, 0.74-1.13), with no statistically significant differences in detection of invasive ductal carcinoma (OR = 0.83; 95% CI, 0.66-1.06), invasive lobular carcinoma (OR = 1.11; 95% CI, 0.59-2.07), or invasive mixed ductal-lobular carcinoma (OR = 1.49; 95% CI, 0.65-3.39). Compared with FFDM, breast cancer screening with DBT has a lower recall rate while detecting a similar distribution of benign and malignant diagnoses.

A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images

Accurately identifying and categorizing cancer structures/sub-types in histological images is an important clinical task involving a considerable workload and a specific subspecialty of pathologists. Digitizing pathology is a current trend that provides large amounts of visual data allowing a faster and more precise diagnosis through the development of automatic image analysis techniques. Recent studies have shown promising results for the automatic analysis of cancer tissue by using deep learning strategies that automatically extract and organize the discriminative information from the data. This paper explores deep learning methods for the automatic analysis of Hematoxylin and Eosin stained histological images of breast cancer and lymphoma. In particular, a deep learning approach is proposed for two different use cases: the detection of invasive ductal carcinoma in breast histological images and the classification of lymphoma sub-types. Both use cases have been addressed by adopting a residual convolutional neural network that is part of a convolutional autoencoder network (i.e., FusionNet). The performances have been evaluated on the public datasets of digital histological images and have been compared with those obtained by using different deep neural networks (UNet and ResNet). Additionally, comparisons with the state of the art have been considered, in accordance with different deep learning approaches. The experimental results show an improvement of 5.06% in F-measure score for the detection task and an improvement of 1.09% in the accuracy measure for the classification task.

Analysis of imaging features of dynamic contrast-enhanced MRI in the detection of invasive ductal carcinoma of breast cancer

Objective To investigate the imaging features of MRI in patients with invasive ductal carcinoma of breast cancer. Methods 75 patients with breast ductal carcinoma treated by surgical treatment were selected.According to the age, 75 patients were divided into young group(≤40 years) with 22 cases and elderly group(>40 years) with 53 cases.The morphology, location, size, border and edge of the two groups were analyzed.The time signal intensity curve(TIC) was analyzed. Results The proportion of patients with non-luminal invasive ductal carcinoma of the young group was 54.55%, which was higher than 30.19% of the elderly group, the difference was statistically significant(χ2=3.94, P=0.047). The proportion of breast cancer infiltrating ductal carcinoma in the young group was 63.64%, which was higher than 37.74% in the elderly group, the difference was statistically significant(χ2=4.21, P=0.040). The proportion of the infarcted ductal carcinoma in the young group was 59.09%, and that in the elderly group was 62.26%, the difference was not statistically significant(χ2=2.19, P=0.14). The proportion of breast invasive ductal carcinoma in the young group was 63.64%, which in the elderly group was 54.72%, the difference was not statistically significant(χ2=0.13, P=0.72). Ⅰ, Ⅱ and Ⅲ type of breast ductal carcinoma TIC in the young group were 0cases, 14cases, 8cases, respectively, which in the elderly group were 0cases, 36cases, 17cases, respectively, there was no statistically significant difference in TIC grade between the two groups(χ2=2.26, P=0.13). Conclusion Breast infiltrating ductal carcinoma is mostly pulmonary lump type, tumor border is unclear; MRI dynamic enhancement technique for the detection of breast invasive ductal carcinoma can reduce misdiagnosis. Key words: Magnetic resonance angiography; Breast neoplasms; Young adult

EVALUATION OF DUAL-SPECTRUM IR SPECTROGRAM SYSTEM ON INVASIVE DUCTAL CARCINOMA (IDC) BREAST CANCER

Invasive Ductal Carcinoma (IDC) is one of the most frequently diagnosed breast cancers. IDC accounts for about 8 out of 10 of all invasive breast cancers. While early detection of breast cancer is essential for the reduction of death rate, there may be already more than 107 cells in a breast cancer when it can be observed by X-ray mammogram. In contrast, the passive IR spectrogram proposed by Szu et al. was shown to be promising in detecting breast cancers several months ahead of mammogram. With energy readings from two IR cameras, middle wavelength IR (MIR, 3–5 μm) and long wavelength IR (LIR, 8–12 μm), dual-spectrum IR (DS-IR) spectrogram may be computed by using the deterministic neighborhood-based blind source separation algorithm developed by Szu et al..4–7 To evaluate the performance of the DS-IR spectrogram on detection of IDC, a DS-IR spectrogram hardware system is built and a sub-pixel super-resolution registration is developed to implement the deterministic neighborhood-based blind source separation algorithm. Clinical tests have been carried out with the approval of Institutional Review Board of National Taiwan University Hospital. From August 2007 to June 2008, 35 patients aged between 30–66 (average age 49) with IDC breast cancers were recruited in this project. The results demonstrate that 62.86% of success rate for IDC detection may be achieved with the cross-sectional data. Longitudinal study shows that breast cancers may be detected more accurately by cross-referencing s1 maps of multiple time-points.