Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer

Abstract

Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour resection samples, automated scoring showed a concordance of 83% with a pathologist. The twelve discordant cases were then independently reviewed, leading to a modification of diagnosis from initial pathologist assessment for eight cases. Diagnostic discordance was found to be largely caused by perceptual differences in assessing HER2 expression due to high HER2 staining heterogeneity. This study provides evidence that deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying cases at high risk of misdiagnosis.

Introduction

Cancer is an ensemble of diseases with vast molecular diversity between tumours of afflicted patients. In order to maximize the chances of clinical benefit, newly developed cancer treatments are targeted at specific molecular alterations that can be identified in the tumour of each patient prior to treatment initiation1. One of the most broadly established approaches to predict targeted treatment efficacy is based on the visual inspection of biomarker expression on tissue sections from a tumour by a pathologist. An example in breast cancer is the semi-quantitative assessment of the expression of the human epidermal growth factor receptor 2 (HER2) as determined by immunohistochemistry (IHC) which defines patient eligibility for anti-HER2 therapies. For patients whose tumour strongly overexpresses HER2, the addition of treatment targeted against HER2 is particularly effective at improving clinical outcome compared to chemotherapy alone2. The prevalence of HER2 overexpressing cancers is estimated to lie between 15% and 20%3 of the 2.7 million patients diagnosed with breast cancer annually in the world4. Accurate assessment of HER2 expression is therefore critical in ensuring patients receive the appropriate therapeutic option. According to the recommendations from the College of American Pathologists and the American Society of Clinical Oncology (CAP/ASCO)3, a tumour is determined as HER2 positive if the number of tumour cells displaying strong HER2 overexpression (3+ cells) exceeds 10% of the total tumour population; equivocal if the number of tumour cells displaying moderate HER2 overexpression (2+ cells) exceeds 10% of the total tumour population and negative otherwise (Fig. 1). Patients with positive HER2 status are eligible for targeted therapy, whilst equivocal cases are reflexed to in situ hybridization (ISH) testing to determine HER2 status. Negative cases are not considered for anti-HER2 therapy. Significant diagnostic variability has been reported between pathologists5,6,7,8,9,10 and it is inferred that 4% of negative cases and 18% of positive cases are misdiagnosed7,11. In particular, scoring variability has been shown to be important for cases that show heterogeneous HER2 expression within the tumour cell population12,13. To ensure diagnostic accuracy, pathologists and oncologists routinely request second opinions. However, second opinions are not always easily accessible and can take several weeks. This situation is likely to become more problematic in the next decade with the increasing number of biomarkers to be evaluated by pathologists for clinical decision making and the shortage of newly trained pathologists14.

Figure 1: Breast carcinoma HER-2 immunohistochemistry (IHC).
figure1

(a) Low-resolution view of a breast carcinoma tissue section stained by HER-2 IHC (brown) and haematoxylin (blue). The overall HER-2 status for this case has been determined as equivocal by a pathologist and it displays important HER2 staining heterogeneity. Solid line and dotted line rectangles corresponds to areas shown in (b) and (c), respectively. Scale bar: 1 mm. (b) Clusters of tumour cells surrounded by immune infiltration and stroma. The majority of cancer cells display a moderate (2+) HER-2 expression. (c) Clusters of tumour cells with strongly positive HER-2 expression (3+) surrounded by stroma.

Computer-aided diagnosis holds great promise to facilitate clinical decision making in personalised oncology. Potential benefits of using computer-aided diagnosis include reduced diagnostic turn-around time and increased biomarker scoring reproducibility. In the last decade, commercial algorithms have been approved by the Food and Drug Administration (FDA) for computer-aided HER2 scoring. Yet, despite evidence that image analysis improves IHC biomarker scoring accuracy and reproducibility in tumours8,10,15, the adoption of computer-aided diagnosis by pathologists has remained limited in practice. This can be explained by limited evidence of added clinical value and by the surplus of time required to predefine tumour regions in the tissue sample16. Recently, deep learning techniques have dramatically improved the ability of computers to recognize objects in images17 raising the possibility for fully automated computer-aided diagnosis. Among deep learning models, convolutional neural networks (ConvNets) is arguably the most studied and validated approach in a range of image understanding tasks such as human face detection18,19 and hand-written character recognition20. The pathology community is showing increasing interest in deep learning21 as demonstrated by studies reporting deep learning based image analysis that can accurately localize cells, classify cells into different cell types22,23,24,25 and detect tumour regions within tissues26,27,28,29,30. Further studies are required to assess the validity and utility of deep learning for clinical decision making.

The objectives of this study were (1) to evaluate the ability of a convolutional neural network (ConvNets) model to automatically recognize cancer cell types compared to classical machine learning techniques, (2) to evaluate the performance of ConvNets to provide accurate HER2 status reviews in clinically realistic conditions and (3) to assess the potential utility of computer-aided diagnosis to facilitate clinical decision making.

Results

Deep learning outperforms classical machine learning techniques for cell classification

To automatically score HER2 expression in tumour cells, we propose a simple approach wherein images are first processed to detect cells and machine learning is subsequently used to classify candidate cells into one of the following categories (Fig. 2): stroma cells, immune cells, tumour cells displaying strong HER2 overexpression (3+ cells), tumour cells displaying moderate HER2 overexpression (2+ cells), tumour cells displaying weak HER2 overexpression (1+ cells), tumour cells displaying no HER2 overexpression (0 cells) and artefacts (tissue folds and debris mistakenly detected as cells by image processing). In this section, we describe two classical machine learning approaches as well as the deep learning approach and compare the performance of each approach using 10-fold cross-validation.

Figure 2: Tumour cells detection and scoring.
figure2

The original image is deconvolved to generate separate images for haematoxylin staining and HER2 staining; cells are detected using a watershed algorithm; cells are classified using deep learning into either immune cells (Imm.), stroma cells (Str.), artefacts (Art.), tumour 0 cells (T0), tumour 1+ cells (T1+), tumour 2+ cells (T2+) and tumour 3+ cells (T3+).

A total of 44 image tiles, from a subset of 18 whole-slide images, were included to benchmark the performance of each cell classification algorithm. Image tiles were processed to detect cells using a ruleset implemented in Definiens Developer XD (Definiens AG). A total of 12200 cells were manually annotated (Supplementary Figure 1). For each annotated cell, 18 biologically relevant features were extracted to describe cell morphology, nuclear colour, texture and HER2 membrane staining (Supplementary Table 1). Classical machine learning models were fit using implementations provided in the R programming environment (R Foundation) in order to predict cell type based on cell features. Two models, linear Support Vector Machine (LSVM)31 and Random Forests (RF)32, were chosen based on their popularity in a number of classification tasks, including cell classification in microscopy images33,34. In contrast to the two previous models, ConvNets directly learns representations from images, bypassing the need to manually define features. The neural network architecture was designed based on studies by LeCun et al.35 and Srivastava et al.36 and is described in more details in the Materials and Methods section. Overall, the model had 466283 parameters that were learned with the backpropagation algorithm in the R programming environment (R Foundation).

Table 1 shows the 10-fold cross-validation performance of the three approaches in terms of F1 scores for each class and overall accuracy. The ConvNets model significantly outperformed LSVM and RF across five of the seven classes and overall (+10% in overall accuracy versus LSVM, two-tailed paired t-test, p = 0.0005;+8% in overall accuracy versus RF, two-tailed paired t-test, p = 0.0001). There was no significant difference in classification performance between ConvNets and either SVM or RF for 2+ cells and 3+ cells.

Table 1 Cross-validation (CV) classification performance for each method.

To better understand the advantage of ConvNets over the classical machine learning models, we investigated the discriminative power of the features utilized in both approaches. Principal Components Analysis was carried-out to project the high-dimensional hand-crafted features and the ConvNets learned features into a more representable 3D space. Figure 3 shows that cells are highly segregated by phenotype in the ConvNets learned features space while cells with different phenotypes are much more overlapping in the hand-crafted features space.

Figure 3: Principal Components Analysis of the hand-crafted and learned features.
figure3

The scatterplot shows the 3 first principal components values of (a) the hand-crafted features and (b) the convolutional neural network learned features. Dots correspond to single cells in one cross-validation fold. Colours correspond to manually defined classes.

Deep learning based scoring is substantially concordant with a pathologist scoring

The HER2 status of 71 confirmed cases of invasive breast carcinoma was determined automatically by deep learning based image analysis and by a pathologist. A pathologist estimated the percentages of 3+, 2+, 1+ and 0 scoring cells within tumour cells. Whole-slide image analysis was performed to detect cells and to classify them using ConvNets (Fig. 4). The percentage of 3+, 2+, 1+ and 0 tumour cells present in the total tumour cell population was calculated for each whole-slide image. Using a standard workstation (16 GB RAM, 6-cores 2 GHz processor), the time required to analyse a typical whole-slide image (27000 × 27000 pixels) was 45 minutes. Finally, a pair of HER2 scores were obtained for each case by applying the CAP/ASCO decision rules3 to the tumour cell percentages estimated by the pathologist and obtained via automated image analysis.

Figure 4: Whole-slide cell classification based on convolutional neural networks.
figure4

For each phenotype, cell density expressed as the number of cells per 0.015 mm2 is shown overlaid with a low resolution view of the original whole-slide image (scale bar: 1 mm). Black squares correspond to areas of high density for each phenotype. A high density area is displayed for each phenotype at full resolution overlaid with the cells classified in that particular phenotype shown as purple dots and the remaining cells shown as black dots.

The prevalence of HER2 statuses determined by automated image analysis and by the pathologist were, respectively, 62% and 61% for negative cases, 15% and 24% for equivocal cases and 23% and 15% for positive cases. Table 2 reports the agreement between deep learning based scores and the pathologist (the full image analysis outputs and pathologist-based scores for each sample can be found in Supplementary Table 2). Three measures were calculated to evaluate the concordance between automated image analysis and the pathologist: the overall accuracy was equal to 83% (95CI: 0.74–0.92), Cohen’s κ coefficient was equal to 0.69 (95CI: 0.55–0.84) and Kendall’s τb correlation coefficient was equal to 0.84 (95CI: 0.75–0.93). Removing from analysis the 18 slides that contained some of the 12200 cells used for learning the ConvNet model did not change the level of agreement between automated scoring and the pathologist (overall agreement = 83%, 95CI: 0.73–0.93; Cohen’s κ = 0.69, 95CI: 0.50 – 0.86; Kendall’s τb = 0.83, 95CI: 0.71–0.95). The concordance between automated image analysis and the pathologist was in the range of previously reported concordance measures between pathologists (Cohen’s κ = 0.5610, Cohen’s κ = 0.6937 and Kendall’s τb = 0.698) suggesting that in this cohort, deep learning based scoring provided HER2 scores at least as accurately as a pathologist. Twelve discordant cases were found between the pathologist and automated image analysis. The majority of discordant cases were diagnosed as equivocal by the pathologist but either negative or positive by automated image analysis: 6 cases were scored as equivocal by the pathologist but positive by automated image analysis and 3 cases were scored as equivocal by the pathologist but negative by automated image analysis.

Table 2 Confusion matrix between pathologist-based scoring and automated scoring for the seventy-one cases in the cohort.

Deep learning identifies cases at risk of misdiagnosis

Independent scoring of the 12 discordant cases and of 12 selected concordant cases (4 negative cases, 4 equivocal cases and 4 positive cases) was carried out to provide a review of the initial diagnosis for each case (Supplementary Table 3). The review was performed by two experienced HER2 raters blinded to the initial pathologist and deep learning based scores. To prevent intra-rater variability, each case was reviewed a second time after a washout period of several days. Results were averaged across the two sessions and the two reviewers to obtain the review scores. For the cases that were initially concordant between automated image analysis and the pathologist, the review was highly concordant with the initial pathologist assessment (concordance on 11 out of 12 cases; Fig. 5). For the cases that were initially discordant between automated image analysis and the pathologist, the agreement between the initial pathologist assessment and the review was low (concordance on 4 out of 12 cases; Fig. 5). These results indicate that deep learning based review enables to highlight challenging cases which should be carefully assessed to ensure diagnostic accuracy.

Figure 5: HER2 scoring concordance.
figure5

Top: comparison between the initial pathologist assessment (IP) and deep learning (ConvNets); bottom-left: comparison between the initially concordant scores and the independent review; bottom-right: comparison between the initially discordant scores and the independent review. Vertical bars represent single cases and pie charts represent overall agreement between either ConvNets or the independent review and the initial pathologist assessment.

After unblinding the scores of each rater, the reviewers investigated each case to identify causes of HER2 assessment variability. Perceptual differences in tumour cell percentage estimation due to widespread HER2 staining heterogeneity was identified as the main driver of disagreement between raters. In most of the ambiguous cases, HER2 staining heterogeneity was caused by commonly occurring technical artefacts and poor tissue quality (Supplementary Figure 2). The effect of HER2 staining heterogeneity on diagnostic ambiguity was confirmed quantitatively. Based on the percentages of 3+, 2+, 1+ and 0 cells defined either by initial pathologist assessment or by automated image analysis, HER2 staining heterogeneity was quantified using Shannon’s entropy, a widely used metric to measure heterogeneity12,38. HER2 staining heterogeneity was significantly higher for discordant cases compared to concordant cases based on pathologist defined tumour cell percentages (concordant cases: median = 0.42, N = 59; discordant cases: median = 0.69, N = 12; W = 186, p = 0.01, Wilcoxon rank sum test; Fig. 6a) and based on tumour cell percentages defined by automated image analysis (concordant cases: median = 0.71, N = 59; discordant cases: median = 1.04, N = 12; W = 162, p = 0.003, Wilcoxon rank sum test; Fig. 6b).

Figure 6: Association of intratumour HER2 heterogeneity with diagnostic ambiguity.
figure6

Comparison of tumour cells Shannon’s entropy between cases that were consistently scored by the pathologist and deep learning (N = 59) and cases were inconsistently scored by the pathologist and deep learning (N = 12). Shannon’s entropy was calculated either using pathologist defined tumour cell percentages (left) or using deep learning defined tumour cell percentages (right).

Discussion and Conclusion

Historically, the benefit of using computer-aided diagnosis for tissue biomarker was weakened by the shortcomings of image analysis to automatically recognize clinically relevant areas. A growing number of methodological studies are suggesting that the ability of deep learning to achieve complex pattern recognition tasks could lead to a new generation of computer-aided diagnosis tools. However, there is no evidence that deep learning algorithms can assist clinical decision making in oncology. This study constitutes a proof-of-concept that deep learning-based analysis of breast tissue samples enables automated and accurate scoring of a tissue biomarker. It further suggests that computer-aided diagnosis can be instrumental in segregating diagnostically unambiguous from challenging cases that are at risk of misdiagnosis.

Automated tumour biomarker scoring was first assessed by evaluating ConvNets ability to recognize the various cell phenotypes usually present in breast tumour samples. ConvNets achieved significantly higher accuracy compared to SVM and RF, two state-of-the-art classical machine learning algorithms. The accuracy of ConvNets for pattern recognition is generally explained by their capacity to learn abstract representations from training data. Due to the pleomorphy of breast cancer cells39, ConvNets are particularly useful to infer discriminatory features that are robust to the diversity of cells within each particular phenotype. A notable exception, however, occurs with 2+ tumour cells and 3+ tumour cells that are distinguishable solely based on membrane staining intensity3. ConvNets did not outperform classical machine learning approaches for these two phenotypes, arguably because classical machine learning benefits from the explicit use of membrane staining intensity as a feature. Thus, one approach to reinforce ConvNets performance for HER2 scoring could be to explicitly introduce membrane staining intensity in addition to the trainable features during ConvNet model fitting. An extension of this work would be to evaluate the impact of the training set on ConvNets performance. In particular, the effect of the training set size and the effect of the depth of experience of the operator annotating the training set on ConvNets performance have not been evaluated here and could provide valuable information to optimize the accuracy of cell classification.

The level of agreement between automated scoring and the initial pathologist scoring reported here suggests that once training is performed, the proposed approach can generate valid HER2 scores in a fully automated fashion. Notably, this study did not explore generalizability to samples emanating from various laboratories. Although IHC procedures are becoming increasingly standardised due to the use of automated staining systems, variation in staining intensity can arise between laboratories due to pre-analytical factors (tissue collection and processing). Additionally, differences between slide scanner vendors and slide scanner calibration can affect image intensity levels. Thus, it remains to be determined if and under which conditions a ConvNets model trained in one laboratory can be applied to score slide images generated from different laboratories or if training and validation should be performed separately in each laboratory.

As HER2 heterogeneity has been reported to be a source of inter-pathologist variability12,40, computer-aided diagnosis could be particularly useful to bring objective and accurate biomarker quantification for these difficult cases. Here, we identified that the discordant cases between automated scoring and the pathologist were significantly associated with HER2 staining heterogeneity. This confirms the confounding role of staining heterogeneity on diagnosis. Among the twelve discordant cases, the independent review established eight cases for which diagnosis differed from the initial pathologist assessment. These results demonstrate that, in this dataset, disagreement between automated scoring and pathologist scoring was mainly due to the presence of ambiguous cases. They further suggests that disagreement between computer-based scores and initial pathologist assessment can be used as an indicator to trigger requests for second opinions. It should be noted that, in this study, case reviews were all performed on digital images whereas the initial diagnosis was performed using the glass-slides with conventional microscopy. Although, studies have reported overall good agreement between diagnoses performed with digital and conventional microscopy41,42,43, the effect of the imaging modality on HER2 status diagnosis was not tested here and could potentially contribute to the observed variability between the initial diagnosis and the review.

In radiology, where computer-aided diagnosis is more widely adopted than in pathology, the usefulness of a computer-aided diagnosis system has been shown to depend not only on the reliability of the information it provides but also on its ability to easily integrate the existing diagnostic workflow44. Here, the ability of deep learning to automatically provide biomarker scoring is an important step towards usability of computer-aided diagnosis. In addition, while deep learning is generally associated with prohibitive computing time without the use of a high-performance computing facility, the implementation of the current approach was designed to meet time and computing resources in a typical pathology laboratory. Thus, a choice was made to use time-efficient image processing techniques for cell detection and ConvNets for cell classification. Additionally, the ConvNets model architecture was designed to accommodate realistic memory and computing processor unit specifications. As a result, a regular desktop computer can handle approximately 30 whole-slide images per day enabling the integration of automated HER2 scoring as part of a diagnostic workflow in a high volume pathology laboratory7.

Owing to an increasing number of biomarkers that are being quantitatively evaluated to determine patient treatment, cancer diagnosis is becoming more complex and more demanding for pathologists. Thus, increasingly computer-aided diagnosis of tissue biomarkers could become a crucial aspect of ensuring that patients are prescribed the appropriate therapeutic option. The capacity for adoption of automated scoring in the routine clinical workflow is also reinforced by the increasing use of whole-slide imaging scanners in pathology departments. This study specifically focused on the HER2 biomarker yet, it opens the door to the application of deep learning in a larger range of immunohistochemistry biomarkers. In conclusion, this study provides evidence for the accuracy and clinical utility of deep learning-based automated scoring of HER2 in breast cancer.

Materials and Methods

Dataset

The dataset consisted of 74 whole-slide images of breast tumour resection samples which either retrieved from the AstraZeneca BioBank or acquired from a commercial provider (Dako Denmark A/S). Slides were obtained by cutting formalin-fixed, paraffin embedded human breast cancer samples into 4 μm-thick sections, stained by IHC for HER2 demonstration (monoclonal Rabbit Anti-Human HER2 antibody, Dako Denmark A/S) and counterstained with haematoxylin using a Dako Autostainer Link48 (Dako Denmark A/S). Slides were digitized with an Aperio ScanScope whole-slide imaging microscope (Aperio, Leica Biosystems Imaging, Inc.) at a resolution of 0.49 μm/pixel. The slides were reviewed to confirm the presence of invasive carcinoma and a total of 71 invasive carcinoma cases were selected for the study.

Cell detection

Whole-slide images were processed in Definiens Developer XD (Definiens AG) with a custom ruleset. In the first step, tissue was segmented from the background with an automatic threshold operation. Tiles of 2000 × 2000 pixels containing tissue were then extracted from each slide and further processed in parallel. For each tile, colour deconvolution was performed as per Van Der Laak et al.45 to extract specific channels for the brown HER2 staining and the blue haematoxylin staining from the original colour image. HER2 staining and haematoxylin staining channels were linearly combined into a single image so that pixels belonging to nuclei had negative values and pixels belonging to positive HER2 membrane staining had positive values. The tissue was then segmented into cells using the watershed algorithm46.

Cell classification

A learning set was constructed in order to train machine learning models to classify detected cells into one of the following categories: stroma cells, immune cells, 0 tumour cells, 1+ tumour cells, 2+ tumour cells, 3+ tumour cells or artefacts. First, a set of 44 image tiles were extracted from 18 whole-slide images selected at random from the dataset. Manual annotations were performed within each tile to define regions that contained a unique class of cells. Regions were carefully delineated to contain cells that were as representative as possible of the entire cell type population and to ensure that each cell class was approximately equally represented in the learning set (on average 1600 cells per class).

The first cell classification approach consisted of a classical machine learning workflow: a set of biologically relevant features were extracted for each cell in the learning set and a model was inferred in order to predict the cell’s phenotype given its feature values. In order to extract features, nuclei were first segmented within each cell with an automatic adaptive threshold operation in the haematoxylin channel and positive membrane was detected at the cell borders with a threshold in the HER2 IHC channel. For every detected cell, a total of 18 features, describing nuclear colour, nuclear texture, nuclear morphology, HER2 membrane staining intensity and proportion of positive HER2 membrane staining were extracted (Supplementary Table 1). Feature extraction was performed using Definiens Developer XD (Definiens AG). Two machine learning models were chosen to predict cell type: Support Vector Machines (SVM) and Random Forest (RF)32. Both models were fitted using the 18 features extracted from cells detected within the manually annotated regions of the learning set. Each model’s performance was assessed using 10-fold cross-validation. Hyper-parameters for SVM (regularization parameter) and RF (number of features considered per split) were tuned via internal 3-fold cross validation (Supplementary Figure 3). For RF, the number of trees was kept constant at 500 and trees were allowed to grow until each leaf contained only a single class of cells. SVM and RF models were fit with the implementations provided in the caret package in R (R Foundation).

The second approach consisted of a convolutional neural network (ConvNets). Compared with other neural networks architectures, ConvNets takes advantage of the spatial structure of images to share weights across units, which therefore limits the number of parameters to be learned and improves rotation, translation and scale invariance. An image patch around each detected cell was extracted and directly used as input to the ConvNets model. A fixed patch-size of 44 × 44 pixels was selected to ensure that even large cells were entirely captured. Given, the image patch, the class of the cell was inferred by passing it through a set of 3 convolution layers followed by fully connected layers. Each convolution layer consisted of 25 learnable 3 × 3 pixels filters. Similarly to Srivastava et al.36, each convolution was followed with a max-pooling operation and a ReLU activation. The sequence of convolutional layers was followed by a fully connected layer with 500 units and an output fully connected layer with one unit per class. Class probabilities were obtained by applying the softmax function to the output of the last fully-connected layer. A connection dropout probability of 0.5 was added prior to the fully connected layers to prevent from overfitting36. Learning was performed by minimizing the training error calculated as the log loss between the inferred class probabilities and ground-truth labels:

where is the indicator function, yis the vector of ground-truth classes for cells indexed by i = 1…n and P is a matrix of probabilities such that Pi,c is the probability of cell i belonging to class c. The back-propagation algorithm was run over 70 training epochs to minimize the training error. Model performance was compared with RF and SVM using 10-fold cross-validation. The ConvNets model was fit and evaluated within the R programming environment (R Foundation).

HER2 scoring

The HER2 status for each of the 71 breast carcinoma cases was first determined by a pathologist and by the automated scoring approach based on the CAP/ASCO recommendations3. A case was determined as HER2 positive if the number of 3+ tumour cells exceeded 10% of the total tumour population; equivocal if the number of 2+ tumour cells exceeded 10% of the total tumour population and HER2 negative otherwise.

The discordant cases between automated scoring and initial pathologist assessment were reviewed as well as a subset of concordant cases. The concordant cases were selected uniformly across the range of possible HER2 scores (4 negative cases, 4 equivocal cases and 4 positive cases). The reviews were performed by two experienced HER2 raters over two scoring sessions separated by several days for each rater. The reviewers were blinded to the initial diagnosis and the scores from the image analysis. The review score was obtained for each case by averaging the percentages of 3+ cells, 2+ cells and 1+ cells over all the four review sessions and applying the ASCO/CAP rules to the averaged results.

Statistical analysis

All statistical analyses were performed in the R programming environment (R Foundation). Machine learning model performance was assessed with the overall accuracy as well as F1 scores for each class. The performance of Random Forest, SVM and ConvNets models was evaluated with the overall agreement:

Where y is the vector of ground-truth class for each cell and is the class inferred by machine learning. In addition, for each class c, the F1 score was calculated as:

with and .

Cross-validation was performed by creating 10 subsets from 44 image tiles representing 18 cases. Each subset contained either 4 or 5 tiles which were used as the test set uniquely for that iteration and the remaining tiles were used for training. The scores were calculated for each cross-validation iteration and scores were averaged across the iterations. This cross-validation design ensured that cells belonging to the same tile were not used for both learning and testing which could bias performance metrics upward (Supplementary Figure 3). Metrics were compared between ConvNets and either SVM or RF using a two-tailed paired t-test. P-values were not adjusted for multiple comparisons.

The overall agreement, Cohen’s κ coefficient and Kendall’s τb correlation coefficient were calculated to assess the concordance between automated scoring and the pathologist scores. The overall agreement was calculated as in Eq. 2 but replacing cell classes by HER2 scores. Cohen’s κ is considered to be a more robust estimator as it adjusts the overall agreement with random agreement47. Furthermore, it has been previously employed to measure inter-pathologist agreement for HER2 scoring. Kendall’s τ coefficient measures the correlation between ordered categorical variables48 and has also been used in inter-pathologist concordance studies. Confidence intervals for each concordance measure were obtained using bootstrap resampling. For each concordance measure, 1000 bootstrap samples were drawn from the original sample with replacement, the concordance was calculated for each bootstrap sample and the 95% confidence intervals of the normal fit to the resampling concordance measure distribution were reported. Similarly to Potts et al.12, HER2 expression heterogeneity was quantified for each slide with the Shannon entropy38 of the distribution of 3+, 2+, 1+ and 0 tumour cells. Shannon’s entropy was compared between concordant and discordant cases with a Wilcoxon rank sum test.

Code availability

The source code will be made available upon request to the corresponding author.

Additional Information

How to cite this article: Vandenberghe, M. E. et al. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci. Rep. 7, 45938; doi: 10.1038/srep45938 (2017).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1

    La Thangue, N. B. & Kerr, D. J. Predictive biomarkers: a paradigm shift towards personalized cancer medicine. Nat. Rev. Clin. Oncol. 8, 587–96 (2011).

    CAS  Article  Google Scholar 

  2. 2

    Arteaga, C. L. et al. Treatment of HER2-positive breast cancer: current status and future perspectives. Nat. Rev. Clin. Oncol. 9, 16–32 (2012).

    CAS  Article  Google Scholar 

  3. 3

    Wolff, A. C. et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. Arch. Pathol. Lab. Med. 138, 241–56 (2014).

    Article  Google Scholar 

  4. 4

    Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. cancer 136, E359–86 (2015).

    CAS  Article  Google Scholar 

  5. 5

    Vogel, C. et al. P1-07-02: Discordance between Central and Local Laboratory HER2 Testing from a Large HER2−Negative Population in VIRGO, a Metastatic Breast Cancer Registry. Cancer Res. 71, P1-7-2–P1-7–2 (2011).

    Google Scholar 

  6. 6

    Roche, P. C. et al. Concordance Between Local and Central Laboratory HER2 Testing in the Breast Intergroup Trial N9831. JNCI J. Natl. Cancer Inst. 94, 855–857 (2002).

    Article  Google Scholar 

  7. 7

    Perez, E. A. et al. HER2 testing by local, central, and reference laboratories in specimens from the North Central Cancer Treatment Group N9831 intergroup adjuvant trial. J. Clin. Oncol. 24, 3032–8 (2006).

    Article  Google Scholar 

  8. 8

    Gavrielides, M. A., Gallas, B. D., Lenz, P., Badano, A. & Hewitt, S. M. Observer variability in the interpretation of HER2/neu immunohistochemical expression with unaided and computer-aided digital microscopy. Arch. Pathol. Lab. Med. 135, 233–42 (2011).

    Google Scholar 

  9. 9

    Bueno-de-Mesquita, J. M. et al. The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment. Ann. Oncol. 21, 40–7 (2010).

    CAS  Article  Google Scholar 

  10. 10

    Bloom, K. & Harrington, D. Enhanced accuracy and reliability of HER-2/neu immunohistochemical scoring using digital microscopy. Am. J. Clin. Pathol. 121, 620–30 (2004).

    Article  Google Scholar 

  11. 11

    Kaufman, P. A. et al. Assessing the discordance rate between local and central HER2 testing in women with locally determined HER2-negative breast cancer. Cancer 120, 2657–64 (2014).

    CAS  Article  Google Scholar 

  12. 12

    Potts, S. J. et al. Evaluating tumor heterogeneity in immunohistochemistry-stained breast cancer tissue. Lab. Investig. 92, 1342–135791 (2012).

    Article  Google Scholar 

  13. 13

    Buckley, N. E. et al. Quantification of HER2 heterogeneity in breast cancer–implications for identification of sub-dominant clones for personalised treatment. Sci. Rep. 6, 23383 (2016).

    CAS  ADS  Article  Google Scholar 

  14. 14

    Robboy, S. J. et al. Pathologist workforce in the United States: I. Development of a predictive model to examine factors influencing supply. Arch. Pathol. Lab. Med. 137, 1723–32 (2013).

    Article  Google Scholar 

  15. 15

    Stålhammar, G. et al. Digital image analysis outperforms manual biomarker assessment in breast cancer. Mod. Pathol. doi: 10.1038/modpathol.2016.34 (2016).

  16. 16

    Montalto, M. C. An industry perspective: An update on the adoption of whole slide imaging. J. Pathol. Inform. 7, 18 (2016).

    Article  Google Scholar 

  17. 17

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    CAS  ADS  Article  Google Scholar 

  18. 18

    Lawrence, S., Giles, C. L., Ah Chung Tsoi, A. C. & Back, A. D. Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Networks 8, 98–113 (1997).

    CAS  Article  Google Scholar 

  19. 19

    Taigman, Y., Yang, M., Ranzato, M. & Wolf, L. DeepFace: Closing the Gap to Human-Level Performance in Face Verification. in 2014 IEEE Conference on Computer Vision and Pattern Recognition 1701–1708 doi: 10.1109/CVPR.2014.220 (IEEE, 2014).

  20. 20

    Simard, P. Y., Steinkraus, D. & Platt, J. C. Best practices for convolutional neural networks applied to visual document analysis. in Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings. 1, 958–963 (IEEE Comput. Soc, 2003).

    Article  Google Scholar 

  21. 21

    Janowczyk, A. & Madabhushi, A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016).

    Article  Google Scholar 

  22. 22

    Cireşan, D. C., Giusti, A., Gambardella, L. M. & Schmidhuber, J. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. in Medical image computing and computer-assisted intervention : MICCAI … International Conference on Medical Image Computing and Computer-Assisted Intervention 411–418 doi: 10.1007/978-3-642-40763-5_51 (Springer Berlin: Heidelberg, 2013).

    Google Scholar 

  23. 23

    Sirinukunwattana, K. et al. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2525803 (2016).

  24. 24

    Chen, T. & Chefd’hotel, C. Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images. In Medical image computing and computer-assisted intervention : MICCAI … International Conference on Medical Image Computing and Computer-Assisted Intervention 17–24 doi: 10.1007/978-3-319-10581-9_3 (Springer International Publishing, 2014).

  25. 25

    Su, H. et al. Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders. In Medical image computing and computer-assisted intervention : MICCAI … International Conference on Medical Image Computing and Computer-Assisted Intervention 383–390 doi: 10.1007/978-3-319-24574-4_46 (Springer International Publishing, 2015).

    Google Scholar 

  26. 26

    Su, H. et al. Region segmentation in histopathological breast cancer images using deep convolutional neural network. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) 55–58 doi: 10.1109/ISBI.2015.7163815 (IEEE, 2015).

  27. 27

    Litjens, G. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016).

    CAS  ADS  Article  Google Scholar 

  28. 28

    Xu, J., Luo, X., Wang, G., Gilmore, H. & Madabhushi, A. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191, 214–223 (2016).

    Article  Google Scholar 

  29. 29

    Cruz-Roa, A. et al. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In SPIE Medical Imaging 904103 doi: 10.1117/12.2043872 (2014).

  30. 30

    Hou, L. et al. Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification. Proceedings. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016, 2424–2433

  31. 31

    Cortes, C. & Vapnik, V. Support-Vector Networks. Mach. Learn. 20, 273–297 (1995).

    MATH  Google Scholar 

  32. 32

    Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  33. 33

    Kumar, R. et al. Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features. J. Med. Eng. 2015, 1–14 (2015).

    Article  Google Scholar 

  34. 34

    Lan, C. et al. Quantitative histology analysis of the ovarian tumour microenvironment. Sci. Rep. 5, 16317 (2015).

    CAS  ADS  Article  Google Scholar 

  35. 35

    Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).

    Article  Google Scholar 

  36. 36

    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).

    MathSciNet  MATH  Google Scholar 

  37. 37

    Thomson, T. A. et al. HER-2/neu in breast cancer: interobserver variability and performance of immunohistochemistry with 4 antibodies compared with fluorescent in situ hybridization. Mod. Pathol. 14, 1079–86 (2001).

    CAS  Article  Google Scholar 

  38. 38

    Shannon, C. & Weaver, W. The Mathematical Theory of Communication. (1949).

  39. 39

    Elston, C. W. & Ellis, I. O. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19, 403–10 (1991).

    CAS  Article  Google Scholar 

  40. 40

    Allott, E. H. et al. Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification. Breast Cancer Res. 18, 68 (2016).

    Article  Google Scholar 

  41. 41

    Kondo, Y., Iijima, T. & Noguchi, M. Evaluation of immunohistochemical staining using whole-slide imaging for HER2 scoring of breast cancer in comparison with real glass slides. Pathol. Int. 62, 592–9 (2012).

    CAS  Article  Google Scholar 

  42. 42

    Gavrielides, M. A., Conway, C., O’Flaherty, N., Gallas, B. D. & Hewitt, S. M. Observer performance in the use of digital and optical microscopy for the interpretation of tissue-based biomarkers. Anal. Cell. Pathol. (Amst). 2014, 157308 (2014).

    Google Scholar 

  43. 43

    Goacher, E., Randell, R., Williams, B. & Treanor, D. The Diagnostic Concordance of Whole Slide Imaging and Light Microscopy: A Systematic Review. Arch. Pathol. Lab. Med. doi: 10.5858/arpa.2016-0025-RA (2016).

  44. 44

    van Ginneken, B., Schaefer-Prokop, C. M. & Prokop, M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261, 719–32 (2011).

    Article  Google Scholar 

  45. 45

    van Der Laak, J. A., Pahlplatz, M. M., Hanselaar, A. G. & de Wilde, P. C. Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy. Cytometry 39, 275–84 (2000).

    CAS  Article  Google Scholar 

  46. 46

    Vincent, L. & Soille, P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13, 583–598 (1991).

    Article  Google Scholar 

  47. 47

    Cohen, J. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 20, 37–46 (1960).

    Article  Google Scholar 

  48. 48

    Kendall, M. G. A New Measure of Rank Correlation. Biometrika 30, 81 (1938).

    Article  Google Scholar 

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Acknowledgements

This work was financially supported by the AstraZeneca IMED postdoc program. We would like to thank Alison Bigley for her constructive comments on the methodology.

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M.L.J.S., D.B. and C.B. planned and supervised the project. M.S. and P.W.S. reviewed the slides. M.E.V. developed and performed image analysis, performed statistical analysis and wrote the manuscript. All authors reviewed and approved the manuscript.

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Correspondence to Michel E. Vandenberghe or Craig Barker.

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Vandenberghe, M., Scott, M., Scorer, P. et al. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep 7, 45938 (2017). https://doi.org/10.1038/srep45938

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