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

Perioperative management of hypertensive neuroblastoma: A study from the Italian Group of Pediatric Surgical Oncologists (GICOP)

Background: Hypertension (HT) is rarely reported in patients affected by Neuroblastoma (NB), and management guidelines are lacking. Clinical features and perioperativemedical treatment insuchpatientswere reviewed to1) ascertain whether a shared treatment strategy exists among centers and 2) if possible, propose some recommendations for the perioperative management of HT in NB patients. Methods: A retrospectivemulticenter surveywas conducted on patients affected by NBwho presented HT symptoms. Results: From 2006 to 2014, 1126 children were registered in the Italian Registry of Neuroblastoma (RINB). Of these, 21 with HT (1.8%) were included in our analysis. Pre- and intraoperative HT management was somewhat dissimilar among the participating centers, apart from a certain consistency in the intraoperative use of the alpha-1 blocker urapidil. Six of the 21 patients (28%) needed persistent antihypertensive treatment at a median follow-up of 36 months (range 4\u2013-96 months) despite tumor removal. Involvement of the renal pedicle was the only risk factor constantly associated to HT persistency following surgery. A correlation between the presence of HT and the secretion of specific catecholamines and/or compression of the renal vascular pedicle could not be demonstrated. Conclusion: Based on this retrospective review of NB patients with HT, no definite therapeutic protocol can be recommended owing to heterogeneity of adopted treatments in different centers. A proposal of perioperative HT management in NB patients is however presented. Level of evidence: IV

Topological analysis of the tumour microenvironment to study Neuroblastoma

Solid tumours and their tumour microenvironment (TME) can be considered as complex networks whose elements are in constant physical stress. All the elements of the TME, including tumour cells, stromal cells, immune and stem cells, blood/lymphatic vessels, nerve fibers and extracellular matrix components, belong to a highly balanced compressiontension molecular and cellular structure. Through mechanical signals, each element could affect its surroundings modulating tumour growth and migration. The analysis of these complex interactions and the understanding of the structural organization of a tumour requires the collaboration of different disciplines. In this thesis, we focus on a particular solid tumour: Neuroblastoma, a rare type of cancer, originated during the embryo development. We apply computational and mathematical tools to analyse the topology of vitronectin, a glycoprotein of the extracellular matrix, in neuroblastoma tumours. Vitronectin has a particular interest in tumour biology where it is associated with cell migration, angiogenesis, and matrix degradation. Still, its role in Neuroblastoma is not clear. Here, we study the organization of vitronectin within the TME considering Neuroblastoma patient prognosis and tumoral aggressiveness. Combing graph theory and image analysis, we characterize histopathological images taken, from a human sample, by analysing different topological features that capture the organizational cues of vitronectin. By means of statistical analyses, we find that two topological features (Euler number and branching), related to the organization of the existing vitronectin within and surrounding the cells (territorial), correlates with risk pre-stratification group and genetic instability criterion. We interpret that a large amount of recently synthesized VN would create tracks to aid malignant neuroblasts to invade other organs, pinpointed by both topological features, which in turn would change, dramatically, the constitution and mechanics of the extracellular matrix, increasing tumour aggressiveness and worsen patient outcomes. Further studies will be required to assess the true potential of vitronectin as a future therapeutic target of neuroblastoma.Los tumores sólidos y su microambiente tumoral (TME) pueden ser vistos como redes complejas cuyos elementos están en constante estrés físico. Todos los elementos del TME, incluidas células tumorales, células del estroma, células inmunes y células troncales, vasos sanguíneos o linfáticos, fibras nerviosas y componentes de la matriz extracelular, pertenecen a una maquinaria molecular y celular de tensión-compresión altamente equilibrada. A través de señales mecánicas, cada elemento podría afectar su entorno modulando el crecimiento tumoral y la migración. El análisis de estas interacciones complejas y la comprensión de la organización estructural de un tumor requiere la colaboración de diferentes disciplinas. En esta tesis, nos centramos en un tumor sólido particular: el neuroblastoma, un cáncer considerado como ‘raro’, que se origina durante el desarrollo del embrión. Aplicando herramientas computacionales y matemáticas, analizamos la topología de la vitronectina, una glicoproteína de la matriz extracelular, en tumores de neuroblastoma. La vitronectina tiene un interés particular en la biología tumoral, ya que está asociada con migración celular, angiogénesis y degradación de la propia matriz. Aún así, su papel en el neuroblastoma no está claro. En este trabajo, estudiamos la organización de la vitronectina dentro del microambiente tumoral, considerando el pronóstico del paciente con neuroblastoma y su agresividad tumoral. Combinando la teoría de gráficos y el análisis de imagen, caracterizamos las imágenes histopatológicas tomadas de una muestra humana, mediante el análisis de diferentes características topológicas que capturan la organización de la vitronectina. Mediante análisis estadísticos, encontramos que dos características topológicas (número de Euler y ‘ramificación’), relacionadas con la organización de la vitronectina existente dentro y alrededor de las células (territorial), se correlacionan con el grupo de pre-estratificación de riesgo y la inestabilidad genética del paciente. En consecuencia, interpretamos que una gran cantidad de VN, sintetizada recientemente, crearía una especia de ‘caminos’ para ayudar a los neuroblastos malignos a invadir otros órganos, que a su vez cambiarían dramáticamente la constitución y la mecánica de la matriz extracelular, aumentando la agresividad del tumor y empeorando el pronóstico del paciente. Futuros estudios serán requeridos para evaluar el verdadero potencial de la vitronectina como una diana terapéutica del neuroblastoma a largo plazo

Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

<p>Abstract</p> <p>Background</p> <p>The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images.</p> <p>Results</p> <p>The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, <it>P </it>< 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively.</p> <p>Conclusions</p> <p>The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537</url></p

Retinoblastoma, the visible CNS tumor: A review

The pediatric ocular cancer retinoblastoma is the only central nervous system (CNS) tumor readily observed without specialized equipment: it can be seen by, and in, the naked eye. This accessibility enables unique imaging modalities. Here, we review this cancer for a neuroscience audience, highlighting these clinical and research imaging options, including fundus imaging, optical coherence tomography, ultrasound, and magnetic resonance imaging. We also discuss the subtype of retinoblastoma driven by the MYCN oncogene more commonly associated with neuroblastoma, and consider trilateral retinoblastoma, in which an intracranial tumor arises along with ocular tumors in patients with germline RB1 gene mutations. Retinoblastoma research and clinical care can offer insights applicable to CNS malignancies, and also benefit from approaches developed elsewhere in the CNS

Quantitative analysis with machine learning models for multi-parametric brain imaging data

Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping