Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

© 2018 Awan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053. The content of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University

A Simple Model for Cell Type Recognition Using 2D-Correlation Analysis of FTIR Images From Breast Cancer Tissue

Breast cancer is the second most common cancer after lung cancer. So far, in clinical practice, most cancer parameters originating from histopathology rely on the visualization by a pathologist of microscopic structures observed in stained tissue sections, including immunohistochemistry markers. Fourier transform infrared spectroscopy (FTIR) spectroscopy provides a biochemical fingerprint of a biopsy sample and, together with advanced data analysis techniques, can accurately classify cell types. Yet, one of the challenges when dealing with FTIR imaging is the slow recording of the data. One cm2 tissue section requires several hours of image recording. We show in the present paper that 2D covariance analysis singles out only a few wavenumbers where both variance and covariance are large. Simple models could be built using 4 wavenumbers to identify the 4 main cell types present in breast cancer tissue sections. Decision trees provide particularly simple models to reach discrimination between the 4 cell types. The robustness of these simple decision-tree models were challenged with FTIR spectral data obtained using different recording conditions. One test set was recorded by transflection on tissue sections in the presence of paraffin while the training set was obtained on dewaxed tissue sections by transmission. Furthermore, the test set was collected with a different brand of FTIR microscope and a different pixel size. Despite the different recording conditions, separating extracellular matrix (ECM) from carcinoma spectra was 100% successful, underlying the robustness of this univariate model and the utility of covariance analysis for revealing efficient wavenumbers. We suggest that 2D covariance maps using the full spectral range could be most useful to select the interesting wavenumbers and achieve very fast data acquisition on quantum cascade laser infrared imaging microscopes

Discrimination of Breast Cancer from Benign Tumours Using Raman Spectroscopy

Breast cancer is the most common cancer among women worldwide, with an estimated 1.7 million cases and 522,000 deaths in 2012. Breast cancer is diagnosed by histopathological examination of breast biopsy material but this is subjective and relies on morphological changes in the tissue. Raman spectroscopy uses incident radiation to induce vibrations in the molecules of a sample and the scattered radiation can be used to characterise the sample. This technique is rapid and non-destructive and is sensitive to subtle biochemical changes occurring at the molecular level. This allows spectral variations corresponding to disease onset to be detected. The aim of this work was to use Raman spectroscopy to discriminate between benign lesions (fibrocystic, fibroadenoma, intraductal papilloma) and cancer (invasive ductal carcinoma and lobular carcinoma) using formalin fixed paraffin preserved (FFPP) tissue. Haematoxylin and Eosin stained sections from the patient biopsies were marked by a pathologist. Raman maps were recorded from parallel unstained tissue sections. Immunohistochemical staining for estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2/neu) was performed on a further set of parallel sections. Both benign and cancer cases were positive for ER while only the cancer cases were positive for HER2. Significant spectral differences were observed between the benign and cancer cases and the benign cases could be differentiated from the cancer cases with good sensitivity and specificity. This study has shown the potential of Raman spectroscopy as an aid to histopathological diagnosis of breast cancer, in particular in the discrimination between benign and malignant tumours

What Matters Most: Determinants Associated with the Selection of Medical Residencies in Qatar.

PURPOSE: There have been several studies into medical student career decision making in occidental countries (eg US, UK), but medical career selection in a Middle Eastern context has not been as well studied. This study aims to explore determinants underpinning medical students' residency choice in Qatar. PATIENTS AND METHODS: During the Fall semester of the 2022-2023 academic year, all (n=358) medical students from the College of Medicine at Qatar University were invited to participate in an online explorative questionnaire about students' career choices and the factors determining their selection. RESULTS: Of the 358 students, 184 responded (51%). Respondents had a mean age of 20 years; the majority were female (73.9%), Qatari (54.3%), unmarried (97.3%), and enrolled in a pre-clerkship year (55.0%). The most important career determinant was specialty-specific factors. The relative importance of other determinants differed by gender and stage of training. Among our respondents, male students were more likely to rate role models and influencers as being important to their choice, while female students and Qatari students overall were more likely to cite societal obligation. Medical students in Qatar seemed to have career preferences in mind upon entry into medical education. Later-year students were more likely to identify the importance of work-life balance and place of practice, but were less likely to rank prestige and income as an important determinant. CONCLUSION: The results of this baseline study suggest that socioeconomic and cultural context influence medical student career decisions

Glandular structure-guided classification of microscopic colorectal images using deep learning

In this work, we propose to automate the pre-cancerous tissue abnormality analysis by performing the classification of image patches using a novel two-stage convolutional neural network (CNN) based framework. Rather than training a model with features that may correlate among various classes, we propose to train a model using the features which vary across the different classes. Our framework processes the input image to locate the region of interest (glandular structures) and then feeds the processed image to a classification model for abnormality prediction. Our experiments show that our proposed approach improves the classification performance by up to 7% using CNNs and more than 10% while using texture descriptors. When testing with gland segmented images, our experiments reveal that the performance of our classification approach is dependent on the gland segmentation approach which is a key task in gland structure-guided classification

Glandular structure-guided classification of microscopic colorectal images using deep learning

In this work, we propose to automate the pre-cancerous tissue abnormality analysis by performing the classification of image patches using a novel two-stage convolutional neural network (CNN) based framework. Rather than training a model with features that may correlate among various classes, we propose to train a model using the features which vary across the different classes. Our framework processes the input image to locate the region of interest (glandular structures) and then feeds the processed image to a classification model for abnormality prediction. Our experiments show that our proposed approach improves the classification performance by up to 7% using CNNs and more than 10% while using texture descriptors. When testing with gland segmented images, our experiments reveal that the performance of our classification approach is dependent on the gland segmentation approach which is a key task in gland structure-guided classification

Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful?

<div><p>The spectral imaging technique has been shown to provide more discriminative information than the RGB images and has been proposed for a range of problems. There are many studies demonstrating its potential for the analysis of histopathology images for abnormality detection but there have been discrepancies among previous studies as well. Many multispectral based methods have been proposed for histopathology images but the significance of the use of whole multispectral cube versus a subset of bands or a single band is still arguable. We performed comprehensive analysis using individual bands and different subsets of bands to determine the effectiveness of spectral information for determining the anomaly in colorectal images. Our multispectral colorectal dataset consists of four classes, each represented by infra-red spectrum bands in addition to the visual spectrum bands. We performed our analysis of spectral imaging by stratifying the abnormalities using both spatial and spectral information. For our experiments, we used a combination of texture descriptors with an ensemble classification approach that performed best on our dataset. We applied our method to another dataset and got comparable results with those obtained using the state-of-the-art method and convolutional neural network based method. Moreover, we explored the relationship of the number of bands with the problem complexity and found that higher number of bands is required for a complex task to achieve improved performance. Our results demonstrate a synergy between infra-red and visual spectrum by improving the classification accuracy (by 6%) on incorporating the infra-red representation. We also highlight the importance of how the dataset should be divided into training and testing set for evaluating the histopathology image-based approaches, which has not been considered in previous studies on multispectral histopathology images.</p></div

Single image showing tissue of two different labels.

<p>The ground truth label given by pathologist for this whole image is HP. While the sub-images on the left side contain normal tissue and sub-images on the right side comprise HP tissue.</p

Results of ensemble learning approach and other comparison algorithms.

<p>LoG, DW and CNN represent Laplacian of Gaussian, discrete wavelet transform and convolutional neural network respectively. In <i>Our Approach 1</i>, parameters fine-tuned on our dataset were used while in <i>Our Approach 2</i>, parameters were fine-tuned for this dataset.</p