Abstract
Multispectral imaging (MSI) based on imaging and spectroscopy, as relatively novel to the field of histopathology, has been used in biomedical multidisciplinary researches. We analyzed and compared the utility of multispectral (MS) versus conventional red–green–blue (RGB) images for immunohistochemistry (IHC) staining to explore the advantages of MSI in clinical-pathological diagnosis. The MS images acquired of IHC-stained membranous marker human epidermal growth factor receptor 2 (HER2), cytoplasmic marker cytokeratin5/6 (CK5/6), and nuclear marker estrogen receptor (ER) have higher resolution, stronger contrast, and more accurate segmentation than the RGB images. The total signal optical density (OD) values for each biomarker were higher in MS images than in RGB images (all P < 0.05). Moreover, receiver operator characteristic (ROC) analysis revealed that a greater area under the curve (AUC), higher sensitivity, and specificity in evaluation of HER2 gene were achieved by MS images (AUC = 0.91, 89.1 %, 83.2 %) than RGB images (AUC = 0.87, 84.5, and 81.8 %). There was no significant difference between quantitative results of RGB images and clinico-pathological characteristics (P > 0.05). However, by quantifying MS images, the total signal OD values of HER2 positive expression were correlated with lymph node status and histological grades (P = 0.02 and 0.04). Additionally, the consistency test results indicated the inter-observer agreement was more robust in MS images for HER2 (inter-class correlation coefficient (ICC) = 0.95, r s = 0.94), CK5/6 (ICC = 0.90, r s = 0.88), and ER (ICC = 0.94, r s = 0.94) (all P < 0.001) than that in RGB images for HER2 (ICC = 0.91, r s = 0.89), CK5/6 (ICC = 0.85, r s = 0.84), and ER (ICC = 0.90, r s = 0.89) (all P < 0.001). Our results suggest that the application of MS images in quantitative IHC analysis could obtain higher accuracy, reliability, and more information of protein expression in relation to clinico-pathological characteristics versus conventional RGB images. It may become an optimal IHC digital imaging system used in quantitative pathology.
Highlights
Immunohistochemistry (IHC) has become a potent approach for detecting antigens in tissues
The objective of our study is to investigate if the automated quantification of MS images of the biomarkers could increase the precision and reliability of image analysis and provide more information compared with conventional RGB images, and to develop a standard digital imaging system for IHC diagnosis in clinical pathology
For imaging technology, compared with colorized digital camera or monochromatic camera, MS imaging can obtain the information on color spectrum distribution at each pixel of a color image, whereas conventional RGB imaging can only obtain information on specific color distribution of the three available channels [13, 25], as demonstrated by spectral curves obtained in our results
Summary
Immunohistochemistry (IHC) has become a potent approach for detecting antigens in tissues. In current clinical pathology practice, interpreting IHC images is largely based on the personal experience of the expert pathologist, which is the major cause of different conclusions on the same IHC image among the pathologists, and such inter- and intraobserver variations could pose significant difficulties to. In the era of digital pathology, how to improve the accuracy and repeatability of quantitative analysis of IHC images and minimize the personal errors remains a formidable task [2]. The conventional red–green–blue (RGB) images by common imaging technology are difficult to accurately distinguish labeled targets in the specific target region, due to inherent technical limitations including suboptimal image contrast and sharpness, strong light effect, overlapping chromogens, and varied staining intensities within or between chromogens, which directly affect the accuracy and repeatability in IHC image quantification and analysis [4, 5]. An effective method to overcome this limitation is to use a multispectral imaging (MSI) system to acquire images and perform linear unmixing to separate and quantify each marker without interference from the others, irrespective of image overlap spatially [4,5,6]
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