Nodal-Stage Classification in Invasive Lobular Breast Carcinoma: Influence of Different Interpretations of the pTNM Classification

Purpose Application of current nodal status classification is complicated in lobular breast carcinoma metastases. The aim of this study was to define the optimal interpretation of the pTNM classification in sentinel node (SN) -positive patients to select patients with limited or with a high risk of non-SN involvement. Patients and Methods SN metastases of 392 patients with lobular breast carcinoma were reclassified according to interpretations of the European Working Group for Breast Screening Pathology (EWGBSP) and guidelines by Turner et al, and the predictive power for non-SN involvement was assessed. Results Reclassification according to definitions of EWGBSP and Turner et al resulted in different pN classification in 73 patients (19%). The rate of non-SN involvement in the 40 patients with isolated tumor cells according to Turner et al and with micrometastases according to EWGBSP was 20%, which is comparable to the established rate for micrometastases. The rate of non-SN involvement in the 29 patients with micrometastases according to Turner et al and with macrometastases according to EWGBSP was 48%, which is comparable to the established rate for macrometastases. Therefore, the EWGBSP method to classify SN tumor load better reflected the risk of non-SN involvement than the Turner et al system. Conclusion Compared with the guidelines by Turner et al, the EWGBSP definitions better reflect SN metastatic tumor load and allow better differentiation between patients with lobular breast carcinoma who have a limited or a high risk of non-SN metastases. Therefore, we suggest using the EWGBSP definitions in these patients to select high-risk patients who may benefit from additional local and/or systemic therapy

Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) TNM staging system provides the most reliable guidelines for the routine prognostication and treatment of colorectal carcinoma. This traditional tumour staging summarizes data on tumour burden (T), the presence of cancer cells in draining and regional lymph nodes (N) and evidence for distant metastases (M). However, it is now recognized that the clinical outcome can vary significantly among patients within the same stage. The current classification provides limited prognostic information and does not predict response to therapy. Multiple ways to classify cancer and to distinguish different subtypes of colorectal cancer have been proposed, including morphology, cell origin, molecular pathways, mutation status and gene expression-based stratification. These parameters rely on tumour-cell characteristics. Extensive literature has investigated the host immune response against cancer and demonstrated the prognostic impact of the in situ immune cell infiltrate in tumours. A methodology named ‘Immunoscore’ has been defined to quantify the in situ immune infiltrate. In colorectal cancer, the Immunoscore may add to the significance of the current AJCC/UICC TNM classification, since it has been demonstrated to be a prognostic factor superior to the AJCC/UICC TNM classification. An international consortium has been initiated to validate and promote the Immunoscore in routine clinical settings. The results of this international consortium may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune). © 2013 The Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland

High quality copy number and genotype data from FFPE samples using Molecular Inversion Probe (MIP) microarrays

BACKGROUND:A major challenge facing DNA copy number (CN) studies of tumors is that most banked samples with extensive clinical follow-up information are Formalin-Fixed Paraffin Embedded (FFPE). DNA from FFPE samples generally underperforms or suffers high failure rates compared to fresh frozen samples because of DNA degradation and cross-linking during FFPE fixation and processing. As FFPE protocols may vary widely between labs and samples may be stored for decades at room temperature, an ideal FFPE CN technology should work on diverse sample sets. Molecular Inversion Probe (MIP) technology has been applied successfully to obtain high quality CN and genotype data from cell line and frozen tumor DNA. Since the MIP probes require only a small (~40 bp) target binding site, we reasoned they may be well suited to assess degraded FFPE DNA. We assessed CN with a MIP panel of 50,000 markers in 93 FFPE tumor samples from 7 diverse collections. For 38 FFPE samples from three collections we were also able to asses CN in matched fresh frozen tumor tissue.RESULTS:Using an input of 37 ng genomic DNA, we generated high quality CN data with MIP technology in 88% of FFPE samples from seven diverse collections. When matched fresh frozen tissue was available, the performance of FFPE DNA was comparable to that of DNA obtained from matched frozen tumor (genotype concordance averaged 99.9%), with only a modest loss in performance in FFPE.CONCLUSION:MIP technology can be used to generate high quality CN and genotype data in FFPE as well as fresh frozen samples.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

Training echo state networks for rotation-invariant bone marrow cell classification

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.ISSN:1433-3058ISSN:0941-064

Robust Bone Marrow Cell Discrimination by Rotation-Invariant Training of Multi-class Echo State Networks

Classification of cell types in context of the architecture in tissue specimen is the basis of diagnostic pathology and decisions for comprehensive investigations rely on a valid interpretation of tissue morphology. Especially visual examination of bone marrow cells takes a considerable amount of time and inter-observer variability can be remarkable. In this work, we propose a novel rotation-invariant learning scheme for multi-class Echo State Networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity

Automated Texture Analysis and Determination of Fibre Orientation of Heart Tissue: A Morphometric Study

<div><p>The human heart has a heterogeneous structure, which is characterized by different cell types and their spatial configurations. The physical structure, especially the fibre orientation and the interstitial fibrosis, determines the electrical excitation and in further consequence the contractility in macroscopic as well as in microscopic areas. Modern image processing methods and parameters could be used to describe the image content and image texture. In most cases the description of the texture is not satisfying because the fibre orientation, detected with common algorithms, is biased by elements such as fibrocytes or endothelial nuclei. The goal of this work is to figure out if cardiac tissue can be analysed and classified on a microscopic level by automated image processing methods with a focus on an accurate detection of the fibre orientation. Quantitative parameters for identification of textures of different complexity or pathological attributes inside the heart were determined. The focus was set on the detection of the fibre orientation, which was calculated on the basis of the cardiomyocytes’ nuclei. It turned out that the orientation of these nuclei corresponded with a high precision to the fibre orientation in the image plane. Additionally, these nuclei also indicated very well the inclination of the fibre.</p></div