Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation

Microscopic Tumour Classification by Digital Mammography

In this paper, we investigate the classification of microscopic tumours using full digital mammography images. Firstly, to address the shortcomings of traditional image segmentation methods, two different deep learning methods are designed to achieve the segmentation of uterine fibroids. The deep lab model is used to optimize the lesion edge detailed information by using the void convolution algorithm and fully connected CRF, and the two semantic segmentation networks are compared to obtain the best results. The Mask RCNN case segmentation model is used to effectively extract features through the ResNet structure, combined with the RPN network to achieve effective use and fusion of features, and continuously optimize the network training to achieve a fine segmentation of the lesion area, and demonstrate the accuracy and feasibility of the two models in medical image segmentation. Histopathology was used to obtain ER, PR, HER scores, and Ki-67 percentage values for all patients. The Kaplan-Meier method was used for survival estimation, the Log-rank test was used for single-factor analysis, and Cox proportional risk regression was used for multifactor analysis. The prognostic value of each factor was calculated, as well as the factors affecting progression-free survival. This study was done to compare the imaging characteristics and diagnostic value of mammography and colour Doppler ultrasonography in nonspecific mastitis, improve the understanding of the imaging characteristics of nonspecific mastitis in these two examinations, improve the accuracy of the diagnosis of this type of disease, improve the ability of distinguishing it from breast cancer, and reduce the rate of misdiagnosis.

Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm

BACKGROUND

As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples.

OBJECTIVE

In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique.

METHOD

Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector.

RESULTS

An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features.

CONCLUSION

Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.

A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers

PURPOSE

To address high false-positive results of FFDM issue, we make the first effort to develop a computer-aided diagnosis (CAD) scheme to analyze and distinguish breast lesions.

METHOD

The breast lesion regions were first segmented and depicted on FFDM images from 106 patients. In this work, 11 gray-level gap-length matrix texture features and 12 shape features were extracted form craniocaudal view and mediolateral oblique view, and then Student's t-test, Fisher-score and Relief-F were introduced to select features. We also investigated the effect of three factors, i.e., discretisation, selection methods and classifier methods, of the classification performance via analysis of variance. Finally, a classification model was constructed. Spearman's correlation coefficient analysis was conducted to assess the internal relevance of features.

RESULTS

The proposed scheme using Student's t-test achieved an area under the receiver operating characteristic curve (AUC) value of 0.923 at 512 bins. The AUC values are 0.884, 0.867, 0.874 and 0.901 for the low gray-level gaps emphasis (LGGE), solidity, extent, and the combined set, respectively. Solidity and extent depicts the correlation coefficient of 0.86 (P < 0.05).

CONCLUSIONS

We present a new CAD scheme based on the contribution of the significant factors. The experimental results demonstrate that the presented scheme can be used to successfully distinguish breast carcinoma lesions and benign fibroadenoma lesions in our FFDM dataset and the MIAS dataset, which may provide a CAD method to assist radiologists in diagnosing and interpreting screening mammograms. Moreover, we found that LGGE, solidity and extent features show great potential for breast lesion classification.

A similarity-based method for prediction of drug side effects with heterogeneous information

Drugs can produce intended therapeutic effects to treat different diseases. However, they may also cause side effects at the same time. For an approved drug, it is best to detect all side effects it can produce. Otherwise, it may bring great risks for pharmaceuticals companies as well as be harmful to human body. It is urgent to design quick and reliable identification methods to detect the side effects for a given drug. In this study, a binary classification model was proposed to predict drug side effects. Different from most previous methods, our model termed the pair of drug and side effect as a sample and convert the original problem to a binary classification problem. Based on the similarity idea, each pair was represented by five features, each of which was derived from a type of drug property. The strong machine learning algorithm, random forest, was adopted as the prediction engine. The ten-fold cross-validation on five datasets with different negative samples indicated that the proposed model yielded a good performance of Matthews correlation coefficient around 0.550 and AUC around 0.8492. In addition, we also analyzed the contribution of each drug property for construction of the model. The results indicated that drug similarity in fingerprint was most related to the prediction of drug side effects and all drug properties gave less or more contributions.

Combined diagnosis of breast cancer in the early stage by MRI and detection of gene expression

Breast cancer is one of the most common cancer types in humans. Magnetic resonance imaging (MRI) is an efficient method for the detection of human breast cancer. However, the efficacy of MRI in detecting breast cancer in the early stage requires to be improved. The present study investigated the diagnostic efficacy of a combination of MRI and detection of gene expression in patients with breast cancer in the early stage. The gene expression levels of Ki-67, BCL11A, FOXC1, HOXD13, PCDHGB7 and her-2 were used as an auxiliary diagnostic index for patients with breast cancer in the early stage. Higher expression levels of TPA and C2erbB22 were observed in tumor tissue obtained from diagnostic biopsy and determined by immunohistochemistry, which indicated a higher risk of breast cancer in a total of 84 participants. Diagnostic data revealed that combination MRI and detection of gene expression had a significantly higher diagnostic rate (66/84) in diagnosing breast cancer in an early stage compared with either MRI (78/360) or detection of gene expression (72/84; P<0.01). It was indicated that the combination of MRI and detection of gene expression had a higher diagnostic rate (94.5%) than either MRI (81.4%) or detection of gene expression (75.5%). Histological analysis confirmed the diagnosis determined by MRI and detection of gene expression. These results suggest that the combination of MRI and detection of gene expression may be a potential diagnostic method for assessing patients with early-stage breast cancer.

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets

Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.

A novel and reliable computational intelligence system for breast cancer detection

Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. This paper suggests a hybrid computational intelligence model based on unsupervised and supervised learning techniques, i.e., self-organizing map (SOM) and complex-valued neural network (CVNN), for reliable detection of breast cancer. The dataset used in this paper consists of 822 patients with five features (patient's breast mass shape, margin, density, patient's age, and Breast Imaging Reporting and Data System assessment). The proposed model was used for the first time and can be categorized in two stages. In the first stage, considering the input features, SOM technique was used to cluster the patients with the most similarity. Then, in the second stage, for each cluster, the patient's features were applied to complex-valued neural network and dealt with to classify breast cancer severity (benign or malign). The obtained results corresponding to each patient were compared to the medical diagnosis results using receiver operating characteristic analyses and confusion matrix. In the testing phase, health and disease detection ratios were 94 and 95%, respectively. Accordingly, the superiority of the proposed model was proved and can be used for reliable and robust detection of breast cancer.