Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer

BACKGROUND

It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection.

PURPOSE

To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer.

MATERIAL AND METHODS

This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model.

RESULTS

Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age.

CONCLUSION

The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis

Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.

Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer

Simple Summary

Neoadjuvant chemotherapy (NACT) is offered to breast cancer (BC) patients to downstage the disease. However, some patients may not respond to NACT, being resistant. We used the serum metabolic profile by Nuclear Magnetic Resonance (NMR) combined with disease characteristics to differentiate between sensitive and resistant BC patients. We obtained accuracy above 80% for the response prediction and showcased how NMR can substantially enhance the prediction of response to NACT.

Abstract

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

Tumor segmentation analysis at different post-contrast time points: A possible source of variability of quantitative DCE-MRI parameters in locally advanced breast cancer

PURPOSE

to assess if tumor segmentation analysis performed at different post-contrast time points (TPs) on dynamic images could influence the extraction of dynamic contrast enhanced (DCE)-MRI parameters in locally advanced breast cancer (LABC), and potentially represent a source of variability.

METHOD

forty patients with forty-two LABC lesions were prospectively enrolled and underwent breast DCE-MRI examination at 3 T. On post-processed dynamic images, enhancing tumor lesions were manually segmented at four different TPs: at the first post-contrast dynamic image in which the lesion was appreciable (TP 1) and at 1, 5 and 10 min after contrast-agent administration (TPs 2, 3 and 4, respectively) and corresponding DCE-MRI parameters were extracted. Friedman's test followed by Bonferroni-adjusted Wilcoxon signed rank test for post-hoc analysis was used to compare DCE-MRI parameters. Intra- and inter-observer reliability of DCE-MRI parameters measurements was assessed using the Intraclass Correlation Coefficient (ICC) analysis.

RESULTS

Ktrans, Kep and iAUC were significantly higher when extracted from ROIs placed at TP1 and progressively decreased from TP 2-4. The intra-observer reliability ranged from good to excellent (ICC's: 0.894 to 0.990). The inter-observer reliability varied from moderate to excellent (0.770 to 0.942). The inter-observer reliability was significantly higher for Ktrans and Kep extracted at TPs1 and 2 as compared to TPs 3 and 4.

CONCLUSIONS

A significant variability of DCE-MRI quantitative parameters occurs when tumor segmentation is performed at different TPs. We suggest to performing tumor delineation at an established TP, preferably the earliest, in order to extract reliable and comparable DCE-MRI data.

An interpretation algorithm for molecular diagnosis of bacterial vaginosis in a maternity hospital using machine learning: proof-of-concept study

Allplex Bacterial vaginosis assay (Seegene, South Korea) is a molecular test for bacterial vaginosis (BV). A machine learning algorithm was devised on 200 samples (BV = 23, non-BV = 177) converting 7 identified bacterial strains polymerase chain reaction results to binary output of BV detected or not. Comparing algorithm interpretation of molecular results to the consensus Gram stain (Hay's criteria), the sensitivity was 65% [95% confidence interval (CI) 42-83%], specificity was 98% (95% CI 95-99%), positive predictive value was 83% (95% CI 58-96%), and negative predictive value was 95% (91-98%) with area under the curve of 0.82 (95% CI 0.76-0.87). For the second phase, 100 samples were processed using the 2 techniques in parallel, with the scientists blinded to the result of the other method. There was agreement 90% of the cases (n = 90/100). The samples that were called BV by the algorithm but non-BV by Gram stain all cluster with the concordant BV samples, suggesting that the molecular test was correct.

Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients

PURPOSE

The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients.

MATERIALS AND METHODS

This institutional review board-approved prospective study included 38 women (median age, 46.5 years; range, 25-70 years) with breast cancer who were scheduled for NAC and underwent mpMRI of the breast at 3 T with dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), and T2-weighted imaging before and after 2 cycles of NAC. For each lesion, 23 features were extracted: qualitative T2-weighted and DCE-MRI features according to BI-RADS (Breast Imaging Reporting and Data System), quantitative pharmacokinetic DCE features (mean plasma flow, volume distribution, mean transit time), and DWI apparent diffusion coefficient (ADC) values. To apply machine learning to mpMRI, 8 classifiers including linear support vector machine, linear discriminant analysis, logistic regression, random forests, stochastic gradient descent, decision tree, adaptive boosting, and extreme gradient boosting (XGBoost) were used to rank the features. Histopathologic residual cancer burden (RCB) class (with RCB 0 being a pCR), recurrence-free survival (RFS), and disease-specific survival (DSS) were used as the standards of reference. Classification accuracy with area under the receiving operating characteristic curve (AUC) was assessed using all the extracted qualitative and quantitative features for pCR as defined by RCB class, RFS, and DSS using recursive feature elimination. To overcome overfitting, 4-fold cross-validation was used.

RESULTS

Machine learning with mpMRI achieved stable performance as shown by mean classification accuracies for the prediction of RCB class (AUC, 0.86) and DSS (AUC, 0.92) based on XGBoost and the prediction of RFS (AUC, 0.83) with logistic regression. The XGBoost classifier achieved the most stable performance with high accuracies compared with other classifiers. The most relevant features for the prediction of RCB class were as follows: changes in lesion size, complete pattern of shrinkage, and mean transit time on DCE-MRI; minimum ADC on DWI; and peritumoral edema on T2-weighted imaging. The most relevant features for prediction of RFS were as follows: volume distribution, mean plasma flow, and mean transit time; DCE-MRI lesion size; minimum, maximum, and mean ADC with DWI. The most relevant features for prediction of DSS were as follows: lesion size, volume distribution, and mean plasma flow on DCE-MRI, and maximum ADC with DWI.

CONCLUSIONS

Machine learning with mpMRI of the breast enables early prediction of pCR to NAC as well as survival outcomes in breast cancer patients with high accuracy and thus may provide valuable predictive information to guide treatment decisions.

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer

OBJECTIVES

The aim of this study is to improve the accuracy of dynamic contrast-enhanced ultrasound (DCE-US) for prostate cancer (PCa) localization by means of a multiparametric approach.

MATERIALS AND METHODS

Thirteen different parameters related to either perfusion or dispersion were extracted pixel-by-pixel from 45 DCE-US recordings in 19 patients referred for radical prostatectomy. Multiparametric maps were retrospectively produced using a Gaussian mixture model algorithm. These were subsequently evaluated on their pixel-wise performance in classifying 43 benign and 42 malignant histopathologically confirmed regions of interest, using a prostate-based leave-one-out procedure.

RESULTS

The combination of the spatiotemporal correlation (r), mean transit time (μ), curve skewness (κ), and peak time (PT) yielded an accuracy of 81% ± 11%, which was higher than the best performing single parameters: r (73%), μ (72%), and wash-in time (72%). The negative predictive value increased to 83% ± 16% from 70%, 69% and 67%, respectively. Pixel inclusion based on the confidence level boosted these measures to 90% with half of the pixels excluded, but without disregarding any prostate or region.

CONCLUSIONS

Our results suggest multiparametric DCE-US analysis might be a useful diagnostic tool for PCa, possibly supporting future targeting of biopsies or therapy. Application in other types of cancer can also be foreseen.

KEY POINTS

• DCE-US can be used to extract both perfusion and dispersion-related parameters. • Multiparametric DCE-US performs better in detecting PCa than single-parametric DCE-US. • Multiparametric DCE-US might become a useful tool for PCa localization.

Nuclear spatial and spectral features based evolutionary method for meningioma subtypes classification in histopathology

Meningioma subtypes classification is a real-world multiclass problem from the realm of neuropathology. The major challenge in solving this problem is the inherent complexity due to high intra-class variability and low inter-class variation in tissue samples. The development of computational methods to assist pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this article, we proposed an optimized evolutionary framework for the classification of benign meningioma into four subtypes. This framework investigates the imperative role of RGB color channels for discrimination of tumor subtypes and compute structural, statistical and spectral phenotypes. An evolutionary technique, Genetic Algorithm, in combination with Support Vector Machine is applied to tune classifier parameters and to select the best possible combination of extracted phenotypes that improved the classification accuracy (94.88%) on meningioma histology dataset, provided by the Institute of Neuropathology, Bielefeld. These statistics show that computational framework can robustly discriminate four subtypes of benign meningioma and may aid pathologists in the diagnosis and classification of these lesions.

A computer-aided diagnosis system for dynamic contrast-enhanced MR images based on level set segmentation and ReliefF feature selection

This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations

This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiver-operating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application.

Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines

Dynamic contrast enhanced MRI (DCE-MRI) provides insight into the vascular properties of tissue. Pharmacokinetic models may be fitted to DCE-MRI uptake patterns, enabling biologically relevant interpretations. The aim of our study was to determine whether treatment outcome for 81 patients with locally advanced cervical cancer could be predicted from parameters of the Brix pharmacokinetic model derived from pre-chemoradiotherapy DCE-MRI. First-order statistical features of the Brix parameters were used. In addition, texture analysis of Brix parameter maps was done by constructing gray level co-occurrence matrices (GLCM) from the maps. Clinical factors and first- and second-order features were used as explanatory variables for support vector machine (SVM) classification, with treatment outcome as response. Classification models were validated using leave-one-out cross-model validation. A random value permutation test was used to evaluate model significance. Features derived from first-order statistics could not discriminate between cured and relapsed patients (specificity 0%-20%, p-values close to unity). However, second-order GLCM features could significantly predict treatment outcome with accuracies (~70%) similar to the clinical factors tumor volume and stage (69%). The results indicate that the spatial relations within the tumor, quantified by texture features, were more suitable for outcome prediction than first-order features.

Supervised embedding of textual predictors with applications in clinical diagnostics for pediatric cardiology

OBJECTIVE

Electronic health records possess critical predictive information for machine-learning-based diagnostic aids. However, many traditional machine learning methods fail to simultaneously integrate textual data into the prediction process because of its high dimensionality. In this paper, we present a supervised method using Laplacian Eigenmaps to enable existing machine learning methods to estimate both low-dimensional representations of textual data and accurate predictors based on these low-dimensional representations at the same time.

MATERIALS AND METHODS

We present a supervised Laplacian Eigenmap method to enhance predictive models by embedding textual predictors into a low-dimensional latent space, which preserves the local similarities among textual data in high-dimensional space. The proposed implementation performs alternating optimization using gradient descent. For the evaluation, we applied our method to over 2000 patient records from a large single-center pediatric cardiology practice to predict if patients were diagnosed with cardiac disease. In our experiments, we consider relatively short textual descriptions because of data availability. We compared our method with latent semantic indexing, latent Dirichlet allocation, and local Fisher discriminant analysis. The results were assessed using four metrics: the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), specificity, and sensitivity.

RESULTS AND DISCUSSION

The results indicate that supervised Laplacian Eigenmaps was the highest performing method in our study, achieving 0.782 and 0.374 for AUC and MCC, respectively. Supervised Laplacian Eigenmaps showed an increase of 8.16% in AUC and 20.6% in MCC over the baseline that excluded textual data and a 2.69% and 5.35% increase in AUC and MCC, respectively, over unsupervised Laplacian Eigenmaps.

CONCLUSIONS

As a solution, we present a supervised Laplacian Eigenmap method to embed textual predictors into a low-dimensional Euclidean space. This method allows many existing machine learning predictors to effectively and efficiently capture the potential of textual predictors, especially those based on short texts.

3D lacunarity in multifractal analysis of breast tumor lesions in dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance (DCE-MR) of the breast is especially robust for the diagnosis of cancer in high-risk women due to its high sensitivity. Its specificity may be, however, compromised since several benign masses take up contrast agent as malignant lesions do. In this paper, we propose a novel method of 3D multifractal analysis to characterize the spatial complexity (spatial arrangement of texture) of breast tumors at multiple scales. Self-similar properties are extracted from the estimation of the multifractal scaling exponent for each clinical case, using lacunarity as the multifractal measure. These properties include several descriptors of the multifractal spectra reflecting the morphology and internal spatial structure of the enhanced lesions relatively to normal tissue. The results suggest that the combined multifractal characteristics can be effective to distinguish benign and malignant findings, judged by the performance of the support vector machine classification method evaluated by receiver operating characteristics with an area under the curve of 0.96. In addition, this paper confirms the presence of multifractality in DCE-MR volumes of the breast, whereby multiple degrees of self-similarity prevail at multiple scales. The proposed feature extraction and classification method have the potential to complement the interpretation of the radiologists and supply a computer-aided diagnosis system.

A new endoscopic ultrasonography image processing method to evaluate the prognosis for pancreatic cancer treated with interstitial brachytherapy

AIM: To develop a fuzzy classification method to score the texture features of pancreatic cancer in endoscopic ultrasonography (EUS) images and evaluate its utility in making prognosis judgments for patients with unresectable pancreatic cancer treated by EUS-guided interstitial brachytherapy.

METHODS: EUS images from our retrospective database were analyzed. The regions of interest were drawn, and texture features were extracted, selected, and scored with a fuzzy classification method using a C++ program. Then, patients with unresectable pancreatic cancer were enrolled to receive EUS-guided iodine 125 radioactive seed implantation. Their fuzzy classification scores, tumor volumes, and carbohydrate antigen 199 (CA199) levels before and after the brachytherapy were recorded. The association between the changes in these parameters and overall survival was analyzed statistically.

RESULTS: EUS images of 153 patients with pancreatic cancer and 63 non-cancer patients were analyzed. A total of 25 consecutive patients were enrolled, and they tolerated the brachytherapy well without any complications. There was a correlation between the change in the fuzzy classification score and overall survival (Spearman test, r = 0.616, P = 0.001), whereas no correlation was found to be significant between the change in tumor volume (P = 0.663), CA199 level (P = 0.659), and overall survival. There were 15 patients with a decrease in their fuzzy classification score after brachytherapy, whereas the fuzzy classification score increased in another 10 patients. There was a significant difference in overall survival between the two groups (67 d vs 151 d, P = 0.001), but not in the change of tumor volume and CA199 level.

CONCLUSION: Using the fuzzy classification method to analyze EUS images of pancreatic cancer is feasible, and the method can be used to make prognosis judgments for patients with unresectable pancreatic cancer treated by interstitial brachytherapy.

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

OBJECTIVE

To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building.

RESULTS

The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82.

DISCUSSION

With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem.

CONCLUSIONS

Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions

OBJECTIVE

The early detection of breast cancer is one of the most important predictors in determining the prognosis for women with malignant tumours. Dynamic contrast-enhanced magnetic-resonance imaging (DCE-MRI) is an important imaging modality for detecting and interpreting the different breast lesions from a time sequence of images and has proved to be a very sensitive modality for breast-cancer diagnosis. However, DCE-MRI exhibits only a moderate specificity, thus leading to a high rate of false positives, resulting in unnecessary biopsies that are stressful and physically painful for the patient and lead to an increase in the cost of treatment. There is a strong medical need for a DCE-MRI computer-aided diagnosis tool that would offer a reliable support to the physician's decision providing a high level of sensitivity and specificity.

METHODS

In our study we investigated the possibility of increasing differentiation between the malignant and the benign lesions with respect to the spatial variation of the temporal enhancements of three parametric maps, i.e., the initial enhancement (IE) map, the post-initial enhancement (PIE) map and the signal enhancement ratio (SER) map, by introducing additional methods along with the grey-level co-occurrence matrix, i.e., a second-order statistical method already applied for quantifying the spatiotemporal variations. We introduced the grey-level run-length matrix and the grey-level difference matrix, representing two additional, second-order statistical methods, and the circular Gabor as a frequency-domain-based method. Each of the additional methods is for the first time applied to the DCE-MRI data to differentiate between the malignant and the benign breast lesions. We applied the least-square minimum-distance classifier (LSMD), logistic regression and least-squares support vector machine (LS-SVM) classifiers on a total of 115 (78 malignant and 37 benign) breast DCE-MRI cases. The performances were evaluated using ten experiments of a ten-fold cross-validation.

RESULTS

Our experimental analysis revealed the PIE map, together with the feature subset in which the discriminating ability of the co-occurrence features was increased by adding the newly introduced features, to be the most significant for differentiation between the malignant and the benign lesions. That diagnostic test - the aforementioned combination of parametric map and the feature subset achieved the sensitivity of 0.9193 which is statistically significantly higher compared to other diagnostic tests after ten-experiments of a ten-fold cross-validation and gave a statistically significantly higher specificity of 0.7819 for the fixed 95% sensitivity after the receiver operating characteristic (ROC) curve analysis. Combining the information from all the three parametric maps significantly increased the area under the ROC curve (AUC) of the aforementioned diagnostic test for the LSMD and logistic regression; however, not for the LS-SVM. The LSMD classifier yielded the highest area under the ROC curve when using the combined information, increasing the AUC from 0.9651 to 0.9755.

CONCLUSION

Introducing new features to those of the grey-level co-occurrence matrix significantly increased the differentiation between the malignant and the benign breast lesions, thus resulting in a high sensitivity and improved specificity.

Pattern Discovery from Biological Data

Extracting useful information from structured and unstructured biological data is crucial in the health industry. Some examples include medical practitioner’s need to identify breast cancer patient in the early stage, estimate survival time of a heart disease patient, or recognize uncommon disease characteristics which suddenly appear. Currently there is an explosion in biological data available in the data bases. But information extraction and true open access to data are require time to resolve issues such as ethical clearance. The emergence of novel IT technologies allows health practitioners to facilitate the comprehensive analyses of medical images, genomes, transcriptomes, and proteomes in health and disease. The information that is extracted from such technologies may soon exert a dramatic change in the pace of medical research and impact considerably on the care of patients. The current research will review the existing technologies being used in heart and cancer research. Finally this research will provide some possible solutions to overcome the limitations of existing technologies. In summary the primary objective of this research is to investigate how existing modern machine learning techniques (with their strength and limitations) are being used in the indent of heartbeat related disease and the early detection of cancer in patients. After an extensive literature review these are the objectives chosen: to develop a new approach to find the association between diseases such as high blood pressure, stroke and heartbeat, to propose an improved feature selection method to analyze huge images and microarray databases for machine learning algorithms in cancer research, to find an automatic distance function selection method for clustering tasks, to discover the most significant risk factors for specific cancers, and to determine the preventive factors for specific cancers that are aligned with the most significant risk factors. Therefore we propose a research plan to attain these objectives within this chapter. The possible solutions of the above objectives are: new heartbeat identification techniques show promising association with the heartbeat patterns and diseases, sensitivity based feature selection methods will be applied to early cancer patient classification, meta learning approaches will be adopted in clustering algorithms to select an automatic distance function, and Apriori algorithm will be applied to discover the significant risks and preventive factors for specific cancers. We expect this research will add significant contributions to the medical professional to enable more accurate diagnosis and better patient care. It will also contribute in other area such as biomedical modeling, medical image analysis and early diseases warning.

Computerized three-class classification of MRI-based prognostic markers for breast cancer

The purpose of this study is to investigate whether computerized analysis using three-class Bayesian artificial neural network (BANN) feature selection and classification can characterize tumor grades (grade 1, grade 2 and grade 3) of breast lesions for prognostic classification on DCE-MRI. A database of 26 IDC grade 1 lesions, 86 IDC grade 2 lesions and 58 IDC grade 3 lesions was collected. The computer automatically segmented the lesions, and kinetic and morphological lesion features were automatically extracted. The discrimination tasks-grade 1 versus grade 3, grade 2 versus grade 3, and grade 1 versus grade 2 lesions-were investigated. Step-wise feature selection was conducted by three-class BANNs. Classification was performed with three-class BANNs using leave-one-lesion-out cross-validation to yield computer-estimated probabilities of being grade 3 lesion, grade 2 lesion and grade 1 lesion. Two-class ROC analysis was used to evaluate the performances. We achieved AUC values of 0.80 ± 0.05, 0.78 ± 0.05 and 0.62 ± 0.05 for grade 1 versus grade 3, grade 1 versus grade 2, and grade 2 versus grade 3, respectively. This study shows the potential for (1) applying three-class BANN feature selection and classification to CADx and (2) expanding the role of DCE-MRI CADx from diagnostic to prognostic classification in distinguishing tumor grades.

Computer-aided diagnosis of breast cancer from magnetic resonance imaging examinations by custom radial basis function vector machine

This paper presents a new method for performing supervised learning (classification) and demonstrates the technique by applying it to the detection of breast cancer from the dynamic information obtained in magnetic resonance imaging examinations. The proposed method is a vector machine similar to the established support vector machine (SVM) method, however, our method involves a reformulation of the classification/prediction process. The proposed classification methodology is compared with the SVM, with both methods using the established radial basis function kernel. The proposed vector machine formulation applies test biasing in a new manner and is demonstrated to produce robust solutions as measured by the receiver operating characteristic (ROC) curve area. The technique is compared with SVMs and yields test improvements up to an additional 9.8% sensitivity or 7.2% specificity.

Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images

BACKGROUND

EUS can detect morphologic abnormalities of pancreatic cancer with high sensitivity but with limited specificity.

OBJECTIVE

To develop a classification model for differential diagnosis of pancreatic cancer by using a digital imaging processing (DIP) technique to analyze EUS images of the pancreas.

DESIGN

A retrospective, controlled, single-center design was used.

SETTING

The study took place at the Second Military Medical University, Shanghai, China.

PATIENTS

There were 153 pancreatic cancer and 63 noncancer patients in this study.

INTERVENTION

All patients underwent EUS-guided FNA and pathologic analysis.

MAIN OUTCOME MEASUREMENTS

EUS images were obtained and correlated with cytologic findings after FNA. Texture features were extracted from the region of interest, and multifractal dimension vectors were introduced in the feature selection to the frame of the M-band wavelet transform. The sequential forward selection process was used for a better combination of features. By using the area under the receiver operating characteristic curve and other texture features based on separability criteria, a predictive model was built, trained, and validated according to the support vector machine theory.

RESULTS

From 67 frequently used texture features, 20 better features were selected, resulting in a classification accuracy of 99.07% after being added to 9 other features. A predictive model was then built and trained. After 50 random tests, the average accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for the diagnosis of pancreatic cancer were 97.98 ± 1.23%, 94.32 ± 0.03%, 99.45 ± 0.01%, 98.65 ± 0.02%, and 97.77 ± 0.01%, respectively.

LIMITATIONS

The limitations of this study include the small sample size and that the support vector machine was not performed in real time.

CONCLUSION

The classification of EUS images for differentiating pancreatic cancer from normal tissue by DIP is quite useful. Further refinements of such a model could increase the accuracy of EUS diagnosis of tumors.

Computer-aided diagnostic models in breast cancer screening

Mammography is the most common modality for breast cancer detection and diagnosis and is often complemented by ultrasound and MRI. However, similarities between early signs of breast cancer and normal structures in these images make detection and diagnosis of breast cancer a difficult task. To aid physicians in detection and diagnosis, computer-aided detection and computer-aided diagnostic (CADx) models have been proposed. A large number of studies have been published for both computer-aided detection and CADx models in the last 20 years. The purpose of this article is to provide a comprehensive survey of the CADx models that have been proposed to aid in mammography, ultrasound and MRI interpretation. We summarize the noteworthy studies according to the screening modality they consider and describe the type of computer model, input data size, feature selection method, input feature type, reference standard and performance measures for each study. We also list the limitations of the existing CADx models and provide several possible future research directions.

Effect of the enhancement threshold on the computer-aided detection of breast cancer using MRI

RATIONALE AND OBJECTIVES

To evaluate the effect that variations in the enhancement threshold have on the diagnostic accuracy of two computer-aided detection (CAD) systems for magnetic resonance based breast cancer screening.

MATERIALS AND METHODS

Informed consent was obtained from all patients participating in cancer screening and this study was approved by the participating institution's review board. This retrospective study was nested in a prospective, single-institution, high-risk, breast screening study involving dynamic contrast-enhanced magnetic resonance imaging. Only those screening examinations (n = 223) for which a histopathological diagnosis was available were included. Two CAD methods were performed: the signal enhancement ratio (SER) and support vector machines (SVMs). Statistical analysis was performed by tracking changes in each CAD test's diagnostic accuracy (eg, receiver-operating characteristic [ROC] curve area, maximum possible sensitivity) with changes in the enhancement threshold.

RESULTS

The enhancement threshold plays a significant role in affecting a CAD test's potential sensitivity, ROC curve area, and number of assumed true and false-positive predictions per cancerous examination. A high threshold can also limit the CAD-based detection of the full size of a lesion.

CONCLUSIONS

Enhancement thresholds can limit a CAD test's ability to diagnose a lesion's full size and as such should not be raised above 60%. The clinically used SER method exhibits a high rate of false positives at low enhancement thresholds and as such the threshold should not be set lower than 50%. The SVM method yielded better results in our study than the SER method at clinically realistic enhancement thresholds.

Model-based and model-free parametric analysis of breast dynamic-contrast-enhanced MRI

A wide range of dynamic-contrast-enhanced (DCE) sequences and protocols, image processing methods, and interpretation criteria have been developed and evaluated over the last 20 years. In particular, attempts have been made to better understand the origin of the contrast observed in breast lesions using physiological models that take into account the vascular and tissue-specific features that influence tracer perfusion. In addition, model-free algorithms to decompose enhancement patterns in order to segment and classify different breast tissue types have been developed. This review includes a description of the mechanism of contrast enhancement by gadolinium-based contrast agents, followed by the current status of the physiological models used to analyze breast DCE-MRI and related critical issues. We further describe more recent unsupervised and supervised methods that use a range of different common algorithms. The model-based and model-free methods strive to achieve scientific accuracy and high clinical performance--both important goals yet to be reached.

Visual MRI: merging information visualization and non-parametric clustering techniques for MRI dataset analysis

OBJECTIVE

This paper presents Visual MRI, an innovative tool for the magnetic resonance imaging (MRI) analysis of tumoral tissues. The main goal of the analysis is to separate each magnetic resonance image in meaningful clusters, highlighting zones which are more probably related with the cancer evolution. Such non-invasive analysis serves to address novel cancer treatments, resulting in a less destabilizing and more effective type of therapy than the chemotherapy-based ones. The advancements brought by Visual MRI are two: first, it is an integration of effective information visualization (IV) techniques into a clustering framework, which separates each MRI image in a set of informative clusters; the second improvement relies in the clustering framework itself, which is derived from a recently re-discovered non-parametric grouping strategy, i.e., the mean shift.

METHODOLOGY

The proposed methodology merges visualization methods and data mining techniques, providing a computational framework that allows the physician to move effectively from the MRI image to the images displaying the derived parameter space. An unsupervised non-parametric clustering algorithm, derived from the mean shift paradigm, and called MRI-mean shift, is the novel data mining technique proposed here. The main underlying idea of such approach is that the parameter space is regarded as an empirical probability density function to estimate: the possible separate modes and their attraction basins represent separated clusters. The mean shift algorithm needs sensibility threshold values to be set, which could lead to highly different segmentation results. Usually, these values are set by hands. Here, with the MRI-mean shift algorithm, we propose a strategy based on a structured optimality criterion which faces effectively this issue, resulting in a completely unsupervised clustering framework. A linked brushing visualization technique is then used for representing clusters on the parameter space and on the MRI image, where physicians can observe further anatomical details. In order to allow the physician to easily use all the analysis and visualization tools, a visual interface has been designed and implemented, resulting in a computational framework susceptible of evaluation and testing by physicians.

RESULTS

Visual MRI has been adopted by physicians in a real clinical research setting. To describe the main features of the system, some examples of usage on real cases are shown, following step by step all the actions scientists can do on an MRI image. To assess the contribution of Visual MRI given to the research setting, a validation of the clustering results in a medical sense has been carried out.

CONCLUSIONS

From a general point of view, the two main objectives reached in this paper are: (1) merging information visualization and data mining approaches to support clinical research and (2) proposing an effective and fully automated clustering technique. More particularly, a new application for MRI data analysis, named Visual MRI, is proposed, aiming at improving the support of medical researchers in the context of cancer therapy; moreover, a non-parametric technique for cluster analysis, named MRI-mean shift, has been drawn. The results show the effectiveness and the efficacy of the proposed application.

Advances in computer-aided diagnosis for breast cancer

PURPOSE OF REVIEW

Computer-aided diagnosis (CAD) is a technology used for the detection and characterization of cancer. Although CAD is not limited to a single type of cancer, a large number of CAD systems to date have been designed and used for breast cancer. The aim of this review is to discuss the current state of the CAD systems for breast-cancer diagnosis, their application as a second reader in clinical practice, and studies that have evaluated the effect of CAD on radiologists' performance.

RECENT FINDINGS

A large number of CAD applications are being developed for different imaging modalities. Owing to commercially available Food and Drug Administration (FDA) approved systems, the main clinical use of CAD to date is for screen-film mammography. Many studies have shown that CAD improves radiologists' performance. A large number of academic institutions have devoted a substantial research effort to developing CAD methods.

SUMMARY

CAD systems will play an increasingly important role in the clinic as a second reader. Clinical trials have shown that CAD can improve the accuracy of breast-cancer detection. Preclinical studies have demonstrated the potential of CAD to improve the classification of malignant and benign lesions. An increased number of CAD systems are being developed for different breast-imaging modalities.

Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines

Early detection of breast cancer is one of the most important factors in determining prognosis for women with malignant tumors. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been shown to be the most sensitive modality for screening high-risk women. Computer-aided diagnosis (CAD) systems have the potential to assist radiologists in the early detection of cancer. A key component of the development of such a CAD system will be the selection of an appropriate classification function responsible for separating malignant and benign lesions. The purpose of this study is to evaluate the effects of variations in temporal feature vectors and kernel functions on the separation of malignant and benign DCE-MRI breast lesions by support vector machines (SVMs). We also propose and demonstrate a classifier visualization and evaluation technique. We show that SVMs provide an effective and flexible framework from which to base CAD techniques for breast MRI, and that the proposed classifier visualization technique has potential as a mechanism for the evaluation of classification solutions.