Breast cancer is the most common cancer diagnosed in women and causes over 40,000 deaths annually in the United States. In early-stage, HR+, HER2- invasive breast cancer, the Oncotype DX (ODX) Breast Cancer Recurrence Score Test predicts the risk of recurrence and the benefit of chemotherapy. However, this gene assay is costly and time-consuming, making it inaccessible to many patients. This study proposes a novel deep-learning approach, Deep-ODX, which performs ODX recurrence risk prediction based on routine H&E histopathology images. Deep-ODX is a multiple-instance learning model that leverages a cross-attention neural network, for instance, aggregation. We train and evaluate Deep-ODX on a whole slide image dataset collected from 151 breast cancer patients. As a result, Deep-ODX achieves 0.862 AUC on our dataset, outperforming the existing deep learning models. This study indicates that deep learning methods can predict ODX results from histopathology images, offering a potentially cost-effective prognostic solution with broader accessibility.
Human epidermal growth factor (HER2) is a predictive and prognostic biomarker whose degree of presence in breast cancer informs prognosis and therapeutic decision making. In clinical practice, it is routinely assessed using immunohistochemical (IHC) staining. A pathologist assigns a score from 0 to 3+ depending on the intensity and distribution of staining – 0 or 1+ scores are classified as HER2 negative, 3+ scores as HER2 positive, and 2+ as equivocal. Unfortunately, variations in HER2 staining and the subjectivity in scoring can lead to inaccuracies. Therefore, we sought to develop an automated method to predict HER2 scores from HER2 and H&E slide images. Our database consisted of 52 adjacent HER2 and H&E tissue sections. Positive regions on HER2 were segmented using a previously developed method. Using 13-fold cross-validation, a truncated Resnet18 was then trained to classify extracted patches using HER2 score as labels for positive regions and a score of 0 for negative regions. Using the same folds, attentionbased multiple instance learning was used to aggregate learned patch embeddings into overall slide-level embeddings, which were subsequently classified. The preliminary method achieved 88% accuracy on 0/1+ and 85% accuracy on 2+ and 3+. Subsequent preliminary experiments qualitatively demonstrate that identified positive regions from HER2 can successfully be transferred over to H&E via image registration. Furthermore, applying the proposed method to predict HER2 score from H&E demonstrates that attention is paid to HER2 positive regions on H&E. Results provide preliminary evidence that HER2 can be localized and therefore scored using H&E images alone.
The morphological features that pathologists use to differentiate neoplasms from normal tissue are nonspecific to tissue type. For example, if given a Ki67 stained biopsy of neuroendocrine or breast tumor, a pathologist would be able to correctly identify morphologically abnormal cells in both samples but may struggle to identify the origin of both samples. This is also true for other pathological malignancies such as carcinomas, sarcomas, and leukemia. This implies that computer algorithms trained to recognize tumor from one site should be able to identify tumor from other sites with similar tumor subtypes. Here, we present the results of an experiment that supports this hypothesis. We train a deep learning system to distinguish tumor from non-tumor regions in Ki67 stained neuroendocrine tumor digital slides. Then, we test the same, unmodified, deep learning model to distinguish breast cancer from non-cancer regions. When applied to a sample of 96 high power fields, our system achieved a cumulative pixel-wise accuracy of 86% across these high-power fields. To our knowledge, our results are the first to formally demonstrate generalized segmentation of tumors from different sites of origin through image analysis. This paradigm has the potential to help with the design of tumor identification algorithms as well as the composition of the datasets they draw from.
Immunohistochemical staining (IHC) of tissue sections is routinely used in pathology to diagnose and characterize malignant tumors. Unfortunately, in the majority of cases, IHC stain interpretation is completed by a trained pathologist using a manual method, which consists of counting each positively and negatively stained cell under a microscope. Even in the hands of expert pathologists, the manual enumeration suffers from poor reproducibility. In this study, we propose a novel method to create artificial datasets in silico with known ground truth, allowing us to analyze the accuracy, precision, and intra- and inter-observer variability in a systematic manner and compare different computer analysis approaches. Our approach employs conditional Generative Adversarial Networks. We created our dataset by using 32 different breast cancer patients' Ki67 stained tissues. Our experiments indicated that synthetic images are indistinguishable from real images: The accuracy of five experts (3 pathologists and 2 image analysts) in distinguishing between 15 real and 15 synthetic images was only 47.3% (±8.5%).
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