Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy

© 2015 IEEE.Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systemshave been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field ofresearch for the last ten years. This survey aims to provide a comprehensive review of the state of the art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aidedsystem. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

Histopathological image analysis: a review,”

Abstract-Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Computer-Assisted Characterization of Prostate Cancer on Magnetic Resonance Imaging

Prostate cancer (PCa) is one of the most prevalent cancers among men. Early diagnosis can improve survival and reduce treatment costs. Current inter-radiologist variability for detection of PCa is high. The use of multi-parametric magnetic resonance imaging (mpMRI) with machine learning algorithms has been investigated both for improving PCa detection and for PCa diagnosis. Widespread clinical implementation of computer-assisted PCa lesion characterization remains elusive; critically needed is a model that is validated against a histologic reference standard that is densely sampled in an unbiased fashion. We address this using our technique for highly accurate fusion of mpMRI with whole-mount digitized histology of the surgical specimen. In this thesis, we present models for characterization of malignant, benign and confounding tissue and aggressiveness of PCa. Further validation on a larger dataset could enable improved characterization performance, improving survival rates and enabling a more personalized treatment plan

Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

Metabolomics entails the comprehensive characterization of the ensemble of endogenous and exogenous metabolites present in a biological specimen. Metabolites represent, at the same time, the downstream output of the genome and the upstream input from various external factors, such as the environment, lifestyle, and diet. Therefore, in the last few years, metabolomic phenotyping has provided unique insights into the fundamental and molecular causes of several physiological and pathophysiological conditions. In parallel, metabolomics has been demonstrating an emerging role in monitoring the influence of different manufacturing procedures on food quality and food safety. In light of the above, this collection includes the latest research from various fields of NMR-based metabolomics applications ranging from biomedicine to data mining and food chemistry

Use of multiple platform “omics” datasets to define new biomarkers in oral cancer and to determine biological processes underpinning heterogeneity of the disease

Oral cancer in early stages (I and II) may be curable by surgery or radiation therapy alone but advanced stage disease (III and IV) has a relatively low survival rate. The pathogenic pathways that contribute to Oral Squamous Cell Carcinoma (OSCC) remain poorly characterised and the critical factor in the lack of prognostic improvement is that a significant proportion of cancers initially are asymptomatic lesions and are not diagnosed or treated until they reach an advanced stage. Hence, a clinically applicable gene expression signature is in high demand and improved characterization of the OSCC gene expression profile would constitute substantial progress. For OSCC, possible themes that might be addressed using microarray data include distinguishing the disease from normal at the molecular level; determining whether specific biomarkers or profiles are predictive for tumour behaviour; and identifying biologic pathways necessarily altered in tumourigenesis, potentially illuminating novel therapeutic targets. However, OSCC is a heterogeneous disease, making diagnostic biomarker development difficult. Although this phenotypic variation is striking when one compares OSCC from different geographic locales, little is known about the underpinning biological mechanisms. Cancer may be accompanied by the production and release of a substantial number of proteins, metabolites and/or hormones into the blood, saliva, and other body fluids that could also serve as useful markers for assessing prognosis, metastasis, monitoring treatment, and detecting malignant disease at an early stage. The primary aim of this thesis is to investigate metabolomic and transcriptomic profiles using multiple bioinformatics approaches and biological annotation tools in an attempt to identify specific biomarkers and prediction models for OSCC from each profile as well as from the interface outcomes of integrating the two platforms. Additional aims of the thesis go further to identify the mechanisms underlying the biological changes during tumorigenic transformation of OSCC, as well as to determine biological processes underpinning the heterogeneity of the disease among populations. Two review studies were carried out in this thesis. The review study of published transcriptomic profiles of OSCC specified several genes and pathways exhibiting substantially altered expression in cancerous versus noncancerous states across studies. However, the result of the review suggests not relying on the final set of genes published by the individual studies, but to access the raw data of each study and start subsequent analysis from that stage using unified bioinformatics approaches to acquire useful and complete understanding of the systems biology. The other review study focused on the metabolic profiles of OSCC and revealed a systemic metabolic response to cancer, which bears great potential for biomarker development and diagnosis of oral cancer. However, the metabolic signature still needs to improve specificity for OSCC from other types of cancer. In an attempt to detect a robust gene signature of OSCC overcoming the limitation of the transcriptomic review in accessing the raw data from the previous works, four public microarray raw datasets (comprising 365 tumour and normal samples) of OSCC were successfully integrated using ComBat data integration method in R software, determining the common set of genes, biomarkers, and the relative regulatory pathways possibly accountable for tumour transformation and growth in OSCC. Examination of the meta-analysis datasets showed several discriminating gene expression signatures for OSCC relative to normal oral mucosa; with a signature of 8 genes (MMP1, LAMC2, PTHLH, TPBG, GPD1L, MAL, TMPRSS11B, and SLC27A6) exhibiting the best discriminating performance and show potential as a diagnostic biomarker set. In addition, 32 biomarkers specific to OSCC and HNSCC were identified with the majority involved in extracellular matrix (ECM), interleukins, and peptidase activity where around 2/3 of them are located in the extracellular space and plasma membrane. Additionally, investigation of the interactive network created by merging metabolic and transcriptomic profiles highlighted the significant molecular and cellular biofunctions, pathways, and biomarkers distinguishing OSCC from normal oral mucosa. The results highlighted interactions of significantly altered expression of Dglucose, ethanol, glutathione, GABA, taurine, choline, creatinine, and pyruvate metabolites with the expressed PTGS2, IL1B, IL8, IL6, MMP1, MMP3, MMP9, SERPINE1, COL1A1, COL4A1, LAMC2, POSTN, ADAM12, CDKN2A, PDPN, TGM3, SPINK5, TIMP4, KRT19, and CRYAB biomarkers of OSCC. Such a pattern may represent a clinically useful surrogate for the presence of OSCC which might help in deciphering some of the obscure multifaceted mechanisms underlying carcinogenesis of OSCC which emerged from dysregulated genetic and metabolic system of the body. In an attempt to define pathways of importance in two phenotypically different forms of OSCC, transcriptomic analysis of OSCC from UK and Sri Lankan patients was undertaken. The development of OSCCs in UK and Sri Lankan populations appears largely mediated by similar biological pathways despite the differences related to race, ethnicity, lifestyle, and/or exposure to environmental carcinogens. However, results revealed a highly activated “Cell-mediated Immune Response” in Sri Lankan tumour and normal samples relative to UK cohorts which may reflects a role in resistance of patients to invasiveness, metastasis, and mortality observed in Sri Lankan relative to UK patients. In conclusion, multiple molecular profiles were able to identify a unique transcriptomic profile for OSCC and could further discriminate the tumour from normal oral mucosa on the basis of 8 genes. Altered expression of several metabolic and transcriptomic biomarkers specific for OSCC were identified, along with several dysregulated pathways and molecular processes found common in patient with oral cancer. Integrating both metabolomic and transcriptomic signatures revealed a promising strategy in analysing the concurrent perturbation in both genetic and metabolic systems of the body. Additional results revealed possible impact of specific supplementary dietary components in boosting the immune system of the body against invasion, progression, and metastasis of the disease. Further clinical studies are required to confirm and validate the current results