An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers

G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma

BACKGROUND

Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly understood.

MATERIALS & METHODS

To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samples were obtained from TCGA, ICGC and GEO databases, and the differences in G6PD expression in different tissues were investigated by differential expression analysis, followed by the establishment of Nomogram to determine the percentage of G6PD in causing LIHC by examining the relationship between G6PD and clinical features, and the subsequent validation of the effect of G6PD on the activity, migration, and invasive ability of hepatocellular carcinoma cells by using the low expression of LI-7 and SNU-449. Additionally, we employed machine learning to validate and compare the predictive capacity of four algorithms for LIHC patient prognosis.

RESULTS

Our findings revealed significantly elevated G6PD expression levels in liver cancer tissues as compared to normal tissues. Meanwhile, Nomogram and Adaboost, Catboost, and Gbdt Regression analyses showed that G6PD accounted for 46%, 31%, and 49% of the multiple factors leading to LIHC. Furthermore, we observed that G6PD knockdown in hepatocellular carcinoma cells led to reduced proliferation, migration, and invasion abilities. Remarkably, the Decision Tree C5.0 decision tree algorithm demonstrated superior discriminatory performance among the machine learning methods assessed.

CONCLUSION

The potential diagnostic utility of G6PD and Decision Tree C5.0 for LIHC opens up a novel avenue for early detection and improved treatment strategies for hepatocellular carcinoma.

An ensemble-based serial cascaded attention network and improved variational auto encoder for breast cancer prognosis prediction using data

Breast cancer is one of the most common types of cancer in women and it produces a huge amount of death rate in the world. Early recognition is lessening its impact. The early recognition of breast cancer could convince patients to receive surgical therapy, which will significantly improve the chance of restoration. This information is used by the machine learning technique to find links between them and appraise our forecasts of fresh occurrences. Later recognition of breast cancer can lead to death. An accurate prescient framework for breast cancer prediction is urgently needed in the current era. In order to accomplish the objective, an adaptive ensemble model is proposed for breast cancer prognosis prediction using data. At the initial stage, the raw data are fetched from benchmark datasets. It is then followed by data cleaning and preprocessing. Subsequently, the pre-processed data is fed into the Improved Variational Autoencoder (IVAE), where the deep features are extracted. Finally, the resultant features are given as input to the Ensemble-based Serial Cascaded Attention Network (ESCANet), which is built with Deep Temporal Convolution Network (DTCN), Bi-directional Long Short-Term Memory (BiLSTM), and Recurrent Neural Network (RNN). The effectiveness of the model is validated and compared with conventional methodologies. Therefore, the results elucidate that the proposed methodology achieves extensive results; thus, it increases the system's efficiency.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome

OBJECTIVE

The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome.

METHODS

A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer.

RESULTS

The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set).

CONCLUSIONS

The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.

TP53 Mutation Estimation Based on MRI Radiomics Analysis for Breast Cancer

BACKGROUND

Noninvasive detection of TP53 mutations is useful for the molecular stratification of breast cancer.

PURPOSE

To explore MRI radiomics features reflecting TP53 mutations in breast cancer and propose a classifier for detecting such mutations.

STUDY TYPE

Retrospective.

POPULATION/SUBJECTS

A total of 139 breast cancer patients with TP53 expression profiling (98 with TP53 mutations and 41 without TP53 mutations).

FIELD STRENGTH/SEQUENCE

1.5 T, T1-weighted (T1W) DCE-MRI.

ASSESSMENT

Lesions were manually segmented using subtracted T1WI. A total of 944 radiomics features (including 744 wavelet-related features) and 7 clinicopathological features were extracted from each lesion. Principal component analysis and Pearson's correlation analysis were used to preprocess the features. Linear discriminant analysis, logistic regression (LR), support vector machine (SVM), and random forest (RF) were used as the classifiers.

STATISTICAL TESTS

Analysis of variance, Kruskal-Wallis and recursive features elimination were used to select features. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic accuracy.

RESULTS

For the radiomics model, the validation cohorts AUCs of the four classifiers ranged from 0.69 (RF) to 0.74 (LR), and LR (0.74) attained the highest AUCs. For the clinicopathological-radiomics combined model, the validation AUCs of the four classifiers ranged from 0.68 (RF) to 0.86 (SVM), and SVM (0.86) attained highest AUCs. In the subgroup analysis of triple-negative (TN) and luminal type breast cancer, RF achieved the highest AUCs (0.83 and 0.94).

DATA CONCLUSION

Clinicopathological-radiomics combined model with SVM could be used as noninvasive biomarkers for predicting TP53 mutations. RF was recommended for the detection of TP53 mutations in TN and luminal type breast cancer.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Parkinson’s Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps

Parkinson’s disease (PD) is a complex degenerative brain disease that affects nerve cells in the brain responsible for body movement. Machine learning is widely used to track the progression of PD in its early stages by predicting unified Parkinson’s disease rating scale (UPDRS) scores. In this paper, we aim to develop a new method for PD diagnosis with the aid of supervised and unsupervised learning techniques. Our method is developed using the Laplacian score, Gaussian process regression (GPR) and self-organizing maps (SOM). SOM is used to segment the data to handle large PD datasets. The models are then constructed using GPR for the prediction of the UPDRS scores. To select the important features in the PD dataset, we use the Laplacian score in the method. We evaluate the developed approach on a PD dataset including a set of speech signals. The method was evaluated through root-mean-square error (RMSE) and adjusted R-squared (adjusted R²). Our findings reveal that the proposed method is efficient in the prediction of UPDRS scores through a set of speech signals (dysphonia measures). The method evaluation showed that SOM combined with the Laplacian score and Gaussian process regression with the exponential kernel provides the best results for R-squared (Motor-UPDRS = 0.9489; Total-UPDRS = 0.9516) and RMSE (Motor-UPDRS = 0.5144; Total-UPDRS = 0.5105) in predicting UPDRS compared with the other kernels in Gaussian process regression.

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

A broad range of medical diagnoses is based on analyzing disease images obtained through high-tech digital devices. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of various diseases. AI techniques based on medical image processing are an essential area of research that uses advanced computer algorithms for prediction, diagnosis, and treatment planning, leading to a remarkable impact on decision-making procedures. Machine Learning (ML) and Deep Learning (DL) as advanced AI techniques are two main subfields applied in the healthcare system to diagnose diseases, discover medication, and identify patient risk factors. The advancement of electronic medical records and big data technologies in recent years has accompanied the success of ML and DL algorithms. ML includes neural networks and fuzzy logic algorithms with various applications in automating forecasting and diagnosis processes. DL algorithm is an ML technique that does not rely on expert feature extraction, unlike classical neural network algorithms. DL algorithms with high-performance calculations give promising results in medical image analysis, such as fusion, segmentation, recording, and classification. Support Vector Machine (SVM) as an ML method and Convolutional Neural Network (CNN) as a DL method is usually the most widely used techniques for analyzing and diagnosing diseases. This review study aims to cover recent AI techniques in diagnosing and predicting numerous diseases such as cancers, heart, lung, skin, genetic, and neural disorders, which perform more precisely compared to specialists without human error. Also, AI's existing challenges and limitations in the medical area are discussed and highlighted.

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning

Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score.

Exploring different computational approaches for effective diagnosis of breast cancer

Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.

Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography

Simple Summary

The assessment of breast lesions through mammographic images is currently challenging, especially in dense breasts. Contrast-enhanced mammography has been shown to overcome the limitations of standard mammography but it greatly depends on the interpretative skills of the physician. The aim of this study was to evaluate the potentialities of statistical and artificial intelligence algorithms as a tool for helping the radiologists in the interpretation of images. The most remarkable results were achieved in discriminating benign from malignant lesions and in the identification of the presence of the hormone receptor. A tool to support the physician’s decision-making process may be designed starting from simple logistic regression and tree-based algorithms. This type of tool may help the radiologist in assessing the investigated breast and in choosing the appropriate follow-up without resorting to histology.

Abstract

Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. Methods: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon–Mann–Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. Results: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. Conclusions: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.

Machine Learning Based Comparative Analysis for Breast Cancer Prediction

One of the most prevalent and leading causes of cancer in women is breast cancer. It has now become a frequent health problem, and its prevalence has recently increased. The easiest approach to dealing with breast cancer findings is to recognize them early on. Early detection of breast cancer is facilitated by computer-aided detection and diagnosis (CAD) technologies, which can help people live longer lives. The major goal of this work is to take advantage of recent developments in CAD systems and related methodologies. In 2011, the United States reported that one out of every eight women was diagnosed with cancer. Breast cancer originates as a result of aberrant cell division in the breast, which leads to either benign or malignant cancer formation. As a result, early detection of breast cancer is critical, and with effective treatment, many lives can be saved. This research covers the findings and analyses of multiple machine learning models for identifying breast cancer. The Wisconsin Breast Cancer Diagnostic (WBCD) dataset was used to develop the method. Despite its small size, the dataset provides some interesting data. The information was analyzed and put to use in a number of machine learning models. For prediction, random forest, logistic regression, decision tree, and K-nearest neighbor were utilized. When the results are compared, the logistic regression model is found to offer the best results. Logistic regression achieves 98% accuracy, which is better than the previous method reported.

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.

Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions

Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier's efficiency and training time. The models' diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

Detection of Mammogram Using Improved Watershed Segmentation Algorithm and Classifying with Feed Forward Neural Network (FNN)

Segmentation of breast tumors with more accuracy using computerized methods is essential for breast cancer monitoring and quantification. Both segmentation and classification of breast tumors using a fully automated or Computer-Aided Diagnosis system poses various problems in terms of imaging properties. In this work, a new hybrid algorithm is proposed for segmentation with a two-step process. Initially, a watershed transformation is applied to separate all basins based on pixel density variation from the mass present in tumors, since it has been quite booming in the presence of tumors in all circumstances. Though this is very perceptive to tiny fluctuations in the size of the image, large numbers of areas are produced unacceptably, and the boundaries after segmentations are also quite hard. The second level set is an effective method of segmenting all types of medical images because; it easily flows with, cavities, folds, splits, and merges. To make the recognition step easier and more accurate, the result of segmentation is considered the beginning position of the curve, and the same will be used at the next step of the level set. This produces a closed, smooth, and accurately placed contour or surface. As a result, the present research uses watershed segmentation to isolate tumor regions and performs classification using Feed Forward Neural Network (FNN) to extract features for classification. Experimental results are evaluated based on performance and quality analysis. In the classification process, the study obtained an accuracy rate of 91.2% in the learning model and 71.8% in a testing model.

Benchmarking prognosis methods for survivability - A case study for patients with contingent primary cancers

BACKGROUND

There is an increasing number of patients with a first primary cancer who are diagnosed with a second primary cancer, but prognosis methods to predict the survivability of a patient with multiple primary cancers have not been fully benchmarked.

METHODS

This study investigated the five-year survivability prognosis performances of six machine learning approaches. These approaches are: artificial neural network, decision tree (DT), logistic regression, support vector machine, naïve Bayes (NB), and Bayesian network (BN). A synthetic minority over-sampling technique (SMOTE) was used to solve the imbalanced problem, and a nationwide cancer patient database containing 7,845 subjects in Taiwan was used as a sample source. Ten primary and secondary cancers and their key variables affecting the survivability of the patients were identified.

RESULTS

All the models using SMOTE improved sensitivity and specificity significantly. NB has the highest performance in terms of accuracy and specificity, whereas BN has the highest performance in terms of sensitivity. Further, the computational time and the power of knowledge representation of NB, BN, and DT outperformed the others.

CONCLUSIONS

Selecting the appropriate prognosis models to predict survivability of patients with two contingent primary cancers can aid precise prediction and can support appropriate treatment advice.

Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS)

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an accuracy of approximately 0.87. These results clearly indicate that properly trained learning algorithms can aid in the diagnosis of LGESS.

A machine-learning modified CART algorithm informs Merkel cell carcinoma prognosis

BACKGROUND

Merkel cell carcinoma (MCC) is a rare neuroendocrine skin cancer with a high mortality rate. MCC staging is currently based on tumour primary size, clinical detectability of lymph node metastases, performance of a lymph node biopsy, and presence of distant metastases.

OBJECTIVE

We aimed to use a modified classification and regression tree (CART) algorithm using available data points in the National Cancer Database (NCDB) to elucidate novel prognostic factors for MCC.

METHODS

Retrospective cohort study of the NCDB and Surveillance, Epidemiology, and End Results (SEER) registries. Cases from the NCDB were randomly assigned to either the training or validation cohorts. A modified CART algorithm was created with data from the training cohort and used to identify prognostic groups that were validated in the NCDB validation and SEER cohorts.

RESULTS

A modified CART algorithm using tumour variables available in the NCDB identified prognostic strata as follows: I: local disease, II: ≤3 positive nodes, III: ≥4 positive nodes, and IV: presence of distant metastases. Three-year survival for these groups in the NCDB validation cohort were 81.2% (SE: 1.7), 59.6% (SE: 3.0), 38.0% (SE: 6.0), and 20.2% (SE: 7.0), respectively. These strata were exhibited greater within-group homogeneity than AJCC groups and were more predictive of survival.

CONCLUSIONS

Risk-stratified grouping of MCC patients incorporating positive lymph node count were strongly predictive of survival and demonstrated a high degree of within-group homogeneity and survival prediction. Incorporation of positive lymph node count within overall staging or sub-staging may help to improve future MCC staging criteria.

Label-free identification of human coronary atherosclerotic plaque based on a three-dimensional quantitative assessment of multiphoton microscopy images

The rupture of coronary atherosclerotic plaque (CAP) and the resulting intracoronary thrombosis account for most acute coronary syndromes. Thus, the early identification and risk assessment of CAP is crucial for timely medical intervention. In this study, we propose a quantitative and label-free method for human CAP identification using multiphoton microscopy (MPM) and three-dimensional (3D) image analysis techniques. By detecting the intrinsic MPM signals, the microstructures of collagen and elastin fibers within normal and CAP-lesioned human coronary artery walls were imaged. Using a 3D gray level co-occurrence matrix method and 3D weighted vector summation algorithm, quantitative indicators that characterize the spatial texture and orientation features of the fibers were extracted. We demonstrate that these indicators show superior accuracy and repeatability over 2D texture features in CAP discrimination. Furthermore, by combining the 3D microstructural indicators, a support vector machine model that classifies CAP from the normal arterial wall with an accuracy of >97% was established. In conjunction with advances in multiphoton endoscopy, the proposed method shows great potential in providing a quantitative, label-free, and real-time tool for the early identification and risk assessment of CAP in the future.

Application of decision tree-based ensemble learning in the classification of breast cancer

As a common screening and diagnostic tool, Fine Needle Aspiration Biopsy (FNAB) of the suspicious breast lumps can be used to distinguish between malignant and benign breast cytology. In this study, we first review published works on the classification of breast cancer where the machine learning and data mining algorithms have been applied by using the Wisconsin Breast Cancer Database (WBCD). This work then introduces useful new tools, based on Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) algorithms to classify breast cancer. The RF and ET strategies use the decision trees as proper classifiers to attain the ultimate classification. The RF and ET approaches include four main stages: input identification, determination of the optimal number of trees, voting analysis, and final decision. The models implemented in this research consider important factors such as uniformity of cell size, bland chromatin, mitoses, and clump thickness as the input parameters. According to the statistical analysis, the proposed methods are able to classify the type of breast cancer accurately. The error analysis results reveal that the designed RF and ET models offer easy-to-use outcomes and the highest diagnostic performance, compared to previous tools/models in the literature for the WBCD classification. The highest and lowest magnitudes of relative importance are attributed to the uniformity of cell size and mitoses among the factors. It is expected that the RF and ET algorithms play an important role in medicine and health systems for screening and diagnosis in the near future.

A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems

Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular clinical dataset or not. The decision to utilize classifiers mostly relies upon the type of data and classification task, thus making it often made arbitrarily. In this study, a comparative evaluation of a wide-array classifier pertaining to six different families, i.e., tree, ensemble, neural, probability, discriminant, and rule-based classifiers are dealt with. A number of real-world publicly datasets ranging from different diseases are taken into account in the experiment in order to demonstrate the generalizability of the classifiers in multiple disease prediction. A total of 25 classifiers, 14 datasets, and three different resampling techniques are explored. This study reveals that the classifier that is likely to become the best performer is the conditional inference tree forest (cforest), followed by linear discriminant analysis, generalize linear model, random forest, and Gaussian process classifier. This work contributes to existing literature regarding a thorough benchmark of classification algorithms for multiple diseases prediction.

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.

Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning

Applying artificial intelligence techniques for diagnosing diseases in hospitals often provides advanced medical services to patients such as the diagnosis of leukemia. On the other hand, surgery and bone marrow sampling, especially in the diagnosis of childhood leukemia, are even more complex and difficult, resulting in increased human error and procedure time decreased patient satisfaction and increased costs. This study investigates the use of neuro-fuzzy and group method of data handling, for the diagnosis of acute leukemia in children based on the complete blood count test. Furthermore, a principal component analysis is applied to increase the accuracy of the diagnosis. The results show that distinguishing between patient and non-patient individuals can easily be done with adaptive neuro-fuzzy inference system, whereas for classifying between the types of diseases themselves, more pre-processing operations such as reduction of features may be needed. The proposed approach may help to distinguish between two types of leukemia including acute lymphoblastic leukemia and acute myeloid leukemia. Based on the sensitivity of the diagnosis, experts can use the proposed algorithm to help identify the disease earlier and lessen the cost.

Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

Assessment and Classification of Mass Lesions Based on Expert Knowledge Using Mammographic Analysis

BACKGROUND

Masses are one of the most important indicators of breast cancer in mammograms, and their classification into two groups as benign and malignant is highly necessary. Computer Aided Diagnosis (CADx) helps radiologists enhance the accuracy of their decision. Hence, the system is required to support and assess with radiologist's interaction as an expert.

METHODS

In this research, classification of breast masses using mammography in the two main views which include MLO and CC, is evaluated with respect to the shape, texture and asymmetry aspect. Additionally, a method was developed and proposed using the classification of breast tissue density based on the decision tree.

DISCUSSION

This study therefore, aims to provide a method based on the human decision-making model that will help in designing the perfect tool for radiologists, regardless of the complexity of computing, costly procedures and also reducing the diagnosis error.

CONCLUSION

Results show that the proposed system for entirely fat, scattered fibroglandular densities, heterogeneously dense, and extremely dense breast achieved 100, 99, 99 and 98% true malignant rate, respectively with cross-validation procedure.

Copy number variation in plasma as a tool for lung cancer prediction using Extreme Gradient Boosting (XGBoost) classifier

Background

The main cause of cancer death is lung cancer (LC) which usually presents at an advanced stage, but its early detection would increase the benefits of treatment. Blood is particularly favored in clinical research given the possibility of using it for relatively noninvasive analyses. Copy number variation (CNV) is a common genetic change in tumor genomes, and many studies have indicated that CNV‐derived cell‐free DNA (cfDNA) from plasma could be feasible as a biomarker for cancer diagnosis.

Methods

In this study, we determined the possibility of using chromosomal arm‐level CNV from cfDNA as a biomarker for lung cancer diagnosis in a small cohort of 40 patients and 41 healthy controls. Arm‐level CNV distributions were analyzed based on z score, and the machine‐learning algorithm Extreme Gradient Boosting (XGBoost) was applied for cancer prediction.

Results

The results showed that amplifications tended to emerge on chromosomes 3q, 8q, 12p, and 7q. Deletions were frequently detected on chromosomes 22q, 3p, 5q, 16q, 10q, and 15q. Upon applying a trained XGBoost classifier, specificity and sensitivity of 100% were finally achieved in the test group (12 patients and 13 healthy controls). In addition, five‐fold cross‐validation proved the stability of the model. Finally, our results suggested that the integration of four arm‐level CNVs and the concentration of cfDNA into the trained XGBoost classifier provides a potential method for detecting lung cancer.

Conclusion

Our results suggested that the integration of four arm‐level CNVs and the concentration from of cfDNA integrated withinto the trained XGBoost classifier could become provides a potentially method for detecting lung cancer detection.

Key points

Significant findings of the study:

Healthy individuals have different arm‐level CNV profiles from cancer patients.

Amplifications tend to emerge on chromosome 3q, 8q, 12p, 7q and deletions tend to emerge on chromosome 22q, 3p, 5q, 16q, 10q, 15q.

What this study adds:

CfDNA concentration, arm 10q, 3q, 8q, 3p, and 22q are key features for prediction.

Trained XGBoost classifier is a potential method for lung cancer detection.

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions.

Prediction of progression in idiopathic pulmonary fibrosis using CT scans at baseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive decline in lung function. Natural history of IPF is unknown and the prediction of disease progression at the time of diagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosis of IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictive model for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, there are two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans and their follow-up status; and (b) simultaneously selecting important features from high-dimensional space, and optimizing the prediction performance. We resolved the first challenge by implementing a study design and having an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-up visits. For the second challenge, we integrated the feature selection with prediction by developing an algorithm using a wrapper method that combines quantum particle swarm optimization to select a small number of features with random forest to classify early patterns of progression. We applied our proposed algorithm to analyze anonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields a parsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROI level. These results are superior to other popular feature selections and classification methods, in that our method produces higher accuracy in prediction of progression and more balanced sensitivity and specificity with a smaller number of selected features. Our work is the first approach to show that it is possible to use only baseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence.

A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features

BACKGROUND

It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time.

RESULTS

In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution.

CONCLUSIONS

The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.

Using Resistin, glucose, age and BMI to predict the presence of breast cancer

Background

The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis.

Methods

For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models.

Results

Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91].

Conclusions

These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3877-1) contains supplementary material, which is available to authorized users.

Point-of-care testing in the early diagnosis of acute pesticide intoxication: The example of paraquat

Acute pesticide intoxication is a common method of suicide globally. This article reviews current diagnostic methods and makes suggestions for future development. In the case of paraquat intoxication, it is characterized by multi-organ failure, causing substantial mortality and morbidity. Early diagnosis may save the life of a paraquat intoxication patient. Conventional paraquat intoxication diagnostic methods, such as symptom review and urine sodium dithionite assay, are time-consuming and impractical in resource-scarce areas where most intoxication cases occur. Several experimental and clinical studies have shown the potential of portable Surface Enhanced Raman Scattering (SERS), paper-based devices, and machine learning for paraquat intoxication diagnosis. Portable SERS and new SERS substrates maintain the sensitivity of SERS while being less costly and more convenient than conventional SERS. Paper-based devices provide the advantages of price and portability. Machine learning algorithms can be implemented as a mobile phone application and facilitate diagnosis in resource-limited areas. Although these methods have not yet met all features of an ideal diagnostic method, the combination and development of these methods offer much promise.

Hadamard Kernel SVM with applications for breast cancer outcome predictions

BACKGROUND

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation.

RESULTS

Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods.

CONCLUSIONS

Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

An intelligent prognostic system for analyzing patients with paraquat poisoning using arterial blood gas indexes

The arterial blood gas (ABG) test is used to assess gas exchange in the lung, and the acid-base level in the blood. However, it is still unclear whether or not ABG test indexes correlate with paraquat (PQ) poisoning. This study investigates the predictive value of ABG tests in prognosing patients with PQ poisoning; it also identifies the most significant indexes of the ABG test. An intelligent machine learning-based system was established to effectively give prognostic analysis of patients with PQ poisoning based on ABG indexes. In the proposed system, an enhanced support vector machine combined with a feature selection strategy was developed to predict the risk status from a pool of 103 patients (56 males and 47 females); of these, 52 subjects were deceased and 51 patients were alive. The proposed method was rigorously evaluated against the real-life dataset in terms of accuracy, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for the risk status. The results demonstrated that there were significant differences in ABG indexes between deceased and alive subjects (p-value <0.01). According to the feature selection, we found that the most important correlated indexes were associated with partial pressure of carbon dioxide (PCO₂). This study discovered the relationship between ABG test and poisoning degree to provide a new avenue for prognosing PQ poisoning.

A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine

An accurate classification of neuromuscular disorders is important in providing proper treatment facilities to the patients. Recently, the microarray technology is employed to monitor the level of activity or expression of large number of genes simultaneously. The gene expression data derived from the microarray experiment usually involve a large number of genes but a very few number of samples. There is a need to reduce the dimension of gene expression data which intends to find a small set of discriminative genes that accurately classifies the samples of various kinds of diseases. So, our goal is to find a small subset of genes which ensures the accurate classification of neuromuscular disorders. In the present paper, we propose a novel hybrid feature selection model for classification of neuromuscular disorders. The process of feature selection is done in two phases by integrating Bhattacharyya coefficient and genetic algorithm (GA). In the first phase, we find Bhattacharyya coefficient to choose a candidate gene subset by removing the most redundant genes. In the second phase, the target gene subset is created by selecting the most discriminative gene subset by applying GA wherein the fitness function is calculated using radial basis function support vector machine (RBF SVM). The proposed hybrid algorithm is applied on two publicly available microarray neuromuscular disorders datasets. The results are compared with two individual techniques of feature selection, namely Bhattacharyya coefficient and GA, and one integrated technique, i.e., Bhattacharyya-GA wherein the fitness function of GA is calculated using four other classifiers, which shows that the proposed integrated method is capable of giving the better classification accuracy.

Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

An Effective Machine Learning Approach for Prognosis of Paraquat Poisoning Patients Using Blood Routine Indexes

The early identification of toxic paraquat (PQ) poisoning in patients is critical to ensure timely and accurate prognosis. Although plasma PQ concentration has been reported as a clinical indicator of PQ poisoning, it is not commonly applied in practice due to the inconvenient necessary instruments and operation. In this study, we explored the use of blood routine indexes to identify the degree of PQ toxicity and/or diagnose PQ poisoning in patients via machine learning approach. Specifically, we developed a method based on support vector machine combined with the feature selection technique to accurately predict PQ poisoning risk status, then tested the method on 79 (42 male and 37 female; 41 living and 38 deceased) patients. The detection method was rigorously evaluated against a real-world data set to determine its accuracy, sensitivity and specificity. Feature selection was also applied to identify the factors correlated with risk status, and the results showed that there are significant differences in blood routine indexes between dead and living PQ-poisoned individuals (p-value < 0.01). Feature selection also showed that the most important correlated indexes are white blood cell and neutrophils. In conclusion, the toxicity or prognosis of PQ poisoning can be preliminarily ascertained by blood routine testing without PQ concentration data, representing an additional tool and innovative approach to assess the prognosis of PQ poisoning.