Abstract:Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co‐localized scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies us… Show more
“…NNP employs the NIH ImageJ tool [41] with a multi-stage cell segmentation technique [14] for cell detection. The detected cells are classified into unstained, weak, moderate, and strong groups using the pre-defined thresholds [9] (see Fig.5) for H-Score calculation. In this paper, Mean Absolute Error (MAE), Standard Deviation (SD) and the correlation coefficient (CC) between the predicted H-Score and the average H-Score of the two pathologists are used as the evaluation metrics.…”
Section: H-score Prediction Resultsmentioning
confidence: 99%
“…Numerous computer-assisted approaches have been proposed for cell or nuclei detection and segmentation [7]. Most literature [8], [9] on histopathology image analysis perform various low-level quantification steps, there is still little attempt to perform end-to-end assessment of the image directly.…”
Section: Introductionmentioning
confidence: 99%
“…An illustration of the value of l la and its corresponding stain intensity. The red dot lines are the thresholds of stain intensity groups[9].…”
One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.
“…NNP employs the NIH ImageJ tool [41] with a multi-stage cell segmentation technique [14] for cell detection. The detected cells are classified into unstained, weak, moderate, and strong groups using the pre-defined thresholds [9] (see Fig.5) for H-Score calculation. In this paper, Mean Absolute Error (MAE), Standard Deviation (SD) and the correlation coefficient (CC) between the predicted H-Score and the average H-Score of the two pathologists are used as the evaluation metrics.…”
Section: H-score Prediction Resultsmentioning
confidence: 99%
“…Numerous computer-assisted approaches have been proposed for cell or nuclei detection and segmentation [7]. Most literature [8], [9] on histopathology image analysis perform various low-level quantification steps, there is still little attempt to perform end-to-end assessment of the image directly.…”
Section: Introductionmentioning
confidence: 99%
“…An illustration of the value of l la and its corresponding stain intensity. The red dot lines are the thresholds of stain intensity groups[9].…”
One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.
“…Spa-RQ is open-source, compatible with both Windows and Mac systems. In comparison to other open-source methods/software that operate on whole slide scans of serial sections 16–18 , it uniquely combines multiple image processing functions with a capacity to perform spatial analysis in selected regions-of-interest (ROIs).Figure 1An introduction to the Spa-RQ software tool package. ( a ) Schematic overview of Spa-RQ, which includes software for image registration (Spa-R) and spatial quantification (Spa-Q).…”
To facilitate analysis of spatial tissue phenotypes, we created an open-source tool package named ‘Spa-RQ’ for ‘Spatial tissue analysis: image Registration & Quantification’. Spa-RQ contains software for image registration (Spa-R) and quantitative analysis of DAB staining overlap (Spa-Q). It provides an easy-to-implement workflow for serial sectioning and staining as an alternative to multiplexed techniques. To demonstrate Spa-RQ’s applicability, we analysed the spatial aspects of oncogenic KRAS-related signalling activities in non-small cell lung cancer (NSCLC). Using Spa-R in conjunction with ImageJ/Fiji, we first performed annotation-guided tumour-by-tumour phenotyping using multiple signalling markers. This analysis showed histopathology-selective activation of PI3K/AKT and MAPK signalling in Kras mutant murine tumours, as well as high p38MAPK stress signalling in p53 null murine NSCLC. Subsequently, Spa-RQ was applied to measure the co-activation of MAPK, AKT, and their mutual effector mTOR pathway in individual tumours. Both murine and clinical NSCLC samples could be stratified into ‘MAPK/mTOR’, ‘AKT/mTOR’, and ‘Null’ signature subclasses, suggesting mutually exclusive MAPK and AKT signalling activities. Spa-RQ thus provides a robust and easy to use tool that can be employed to identify spatially-distributed tissue phenotypes.
“…Automated image analysis also allows simultaneous scoring of ER and PR restricted to tumour-rich areas only, as reported in [39] and additional studies demonstrate non-inferiority to manual HER-2 scoring in breast cancer [40].…”
Digital pathology and image analysis potentially provide greater accuracy, reproducibility and standardisation of pathology‐based trial entry criteria and endpoints, alongside extracting new insights from both existing and novel features. Image analysis has great potential to identify, extract and quantify features in greater detail in comparison to pathologist assessment, which may produce improved prediction models or perform tasks beyond manual capability. In this article, we provide an overview of the utility of such technologies in clinical trials and provide a discussion of the potential applications, current challenges, limitations and remaining unanswered questions that require addressing prior to routine adoption in such studies. We reiterate the value of central review of pathology in clinical trials, and discuss inherent logistical, cost and performance advantages of using a digital approach. The current and emerging regulatory landscape is outlined. The role of digital platforms and remote learning to improve the training and performance of clinical trial pathologists is discussed. The impact of image analysis on quantitative tissue morphometrics in key areas such as standardisation of immunohistochemical stain interpretation, assessment of tumour cellularity prior to molecular analytical applications and the assessment of novel histological features is described. The standardisation of digital image production, establishment of criteria for digital pathology use in pre‐clinical and clinical studies, establishment of performance criteria for image analysis algorithms and liaison with regulatory bodies to facilitate incorporation of image analysis applications into clinical practice are key issues to be addressed to improve digital pathology incorporation into clinical trials.
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