From mouse to human: cellular morphometric subtype learned from mouse mammary tumors provides prognostic value in human breast cancer

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.This work was supported by the Department of Defense (DoD)BCRP: BC190820 (J-HM); and the National Cancer Institute (NCI) at the National Institutes of Health (NIH): R01CA184476 (HC). Lawrence Berkeley National Laboratory (LBNL) is a multi-program national laboratory operated by the University of California for the DOE under contract DE AC02-05CH1123

RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the CNN model as simple as possible. The proposed RCCNet model consists of only 1,512,868 learnable parameters which are significantly less compared to the popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The experiments are conducted over publicly available routine colon cancer histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time. The proposed method has achieved a classification accuracy of 80.61% and 0.7887 weighted average F1 score. The proposed RCCNet is more efficient and generalized terms of the training time and data over-fitting, respectively.Comment: Published in ICARCV 201

Fractal analysis of scatter imaging signatures to distinguish breast pathologies

Fractal analysis combined with a label-free scattering technique is proposed for describing the pathological architecture of tumors. Clinicians and pathologists are conventionally trained to classify abnormal features such as structural irregularities or high indices of mitosis. The potential of fractal analysis lies in the fact of being a morphometric measure of the irregular structures providing a measure of the object’s complexity and self-similarity. As cancer is characterized by disorder and irregularity in tissues, this measure could be related to tumor growth. Fractal analysis has been probed in the understanding of the tumor vasculature network. This work addresses the feasibility of applying fractal analysis to the scattering power map (as a physical modeling) and principal components (as a statistical modeling) provided by a localized reflectance spectroscopic system. Disorder, irregularity and cell size variation in tissue samples is translated into the scattering power and principal components magnitude and its fractal dimension is correlated with the pathologist assessment of the samples. The fractal dimension is computed applying the box-counting technique. Results show that fractal analysis of ex-vivo fresh tissue samples exhibits separated ranges of fractal dimension that could help classifier combining the fractal results with other morphological features. This contrast trend would help in the discrimination of tissues in the intraoperative context and may serve as a useful adjunct to surgeons.This work has been supported by CYCIT projects DA2TOI (FIS2010-19860) and TFS (TEC2010-20224-C02-02), as well as FPU PhD Scholarship (FPU12/04130), all funded by the Spanish Government

Single-cell screening of multiple biophysical properties in leukemia diagnosis from peripheral blood by pure light scattering

Abstract Histology and histopathology are based on the morphometric observations of quiescent cells. Their diagnostic potential could largely benefit from a simultaneous screening of intrinsic biophysical properties at single-cell level. For such a purpose, we analyzed light scattering signatures of individual mononuclear blood cells in microfluidic flow. In particular, we extracted a set of biophysical properties including morphometric (dimension, shape and nucleus-to-cytosol ratio) and optical (optical density) ones to clearly discriminate different cell types and stages. By considering distinctive ranges of biophysical properties along with the obtained relative cell frequencies, we can identify unique cell classes corresponding to specific clinical conditions (p < 0.01). Based on such a straightforward approach, we are able to discriminate T-, B-lymphocytes, monocytes and beyond that first results on different stages of lymphoid and myeloid leukemia cells are presented. This work shows that the simultaneous screening of only three biophysical properties enables a clear distinction between pathological and physiological mononuclear blood stream cells. We believe our approach could represent a useful tool for a label-free analysis of biophysical single-cell signatures

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

Helicobacter pylori Infection Promotes Methylation and Silencing of Trefoil Factor 2, Leading to Gastric Tumor Development in Mice and Humans

Background & Aims Trefoil factors (TFFs) regulate mucosal repair and suppress tumor formation in the stomach. Tff1 deficiency results in gastric cancer, whereas Tff2 deficiency increases gastric inflammation. TFF2 expression is frequently lost in gastric neoplasms, but the nature of the silencing mechanism and associated impact on tumorigenesis have not been determined. Methods We investigated the epigenetic silencing of TFF2 in gastric biopsy specimens from individuals with Helicobacter pylori-positive gastritis, intestinal metaplasia, gastric cancer, and disease-free controls. TFF2 function and methylation were manipulated in gastric cancer cell lines. The effects of Tff2 deficiency on tumor growth were investigated in the gp130[superscript F/F] mouse model of gastric cancer. Results In human tissue samples, DNA methylation at the TFF2 promoter began at the time of H pylori infection and increased throughout gastric tumor progression. TFF2 methylation levels were inversely correlated with TFF2 messenger RNA levels and could be used to discriminate between disease-free controls, H pylori-infected, and tumor tissues. Genome demethylation restored TFF2 expression in gastric cancer cell lines, so TFF2 silencing requires methylation. In Tff2-deficient gp130[superscript F/F]/Tff2[superscript −/−] mice, proliferation of mucosal cells and release of T helper cell type-1 (Th-1) 1 cytokines increased, whereas expression of gastric tumor suppressor genes and Th-2 cytokines were reduced, compared with gp130[superscript F/F]controls. The fundus of gp130[superscript F/F]/Tff2[superscript −/−] mice displayed glandular atrophy and metaplasia, indicating accelerated preneoplasia. Experimental H pylori infection in wild-type mice reduced antral expression of Tff2 by increased promoter methylation. Conclusions TFF2 negatively regulates preneoplastic progression and subsequent tumor development in the stomach, a role that is subverted by promoter methylation during H pylori infection.National Health and Medical Research Council (Australia