Key Points Question Can molecular markers of cancer be extracted from tissue morphology as seen in hematoxylin-eosin–stained images? Findings In this diagnostic study of tissue microarray hematoxylin-eosin–stained images from 5356 patients with breast cancer, molecular biomarker expression was found to be significantly associated with tissue histomorphology. A deep learning model was able to predict estrogen receptor expression solely from hematoxylin-eosin–stained images with noninferior accuracy to standard immunohistochemistry. Meaning These results suggest that deep learning models may assist pathologists in molecular profiling of cancer with practically no added cost and time.
In the past several decades, many attempts have been made to model synthetic realistic geometric data. The goal of such models is to generate plausible 3D geometries and textures. Perhaps the best known of its kind is the linear 3D morphable model (3DMM) for faces. Such models can be found at the core of many computer vision applications such as face reconstruction, recognition and authentication to name just a few. Generative adversarial networks (GANs) have shown great promise in imitating high dimensional data distributions. State of the art GANs are capable of performing tasks such as image to image translation as well as auditory and image signal synthesis, producing novel plausible samples from the data distribution at hand. Geometric data is generally more difficult to process due to the inherent lack of an intrinsic parametrization. By bringing geometric data into an aligned space, we are able to map the data onto a 2D plane using a universal parametrization. This alignment process allows for efficient processing of digitally scanned geometric data via image processing tools. Using this methodology, we propose a novel face synthesis model for generation of realistic facial textures together with their corresponding geometry. A GAN is employed in order to imitate the space of parametrized human textures, while corresponding facial geometries are generated by learning the best 3DMM coefficients for each texture. The generated textures are mapped back onto the corresponding geometries to obtain new generated high resolution 3D faces.
Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. In contrast, hematoxylin and eosin (H&E) is a robust staining used routinely for cancer diagnosis. Here, we show that PD-L1 expression can be predicted from H&E-stained images by employing state-of-the-art deep learning techniques. With the help of two expert pathologists and a designed annotation software, we construct a dataset to assess the feasibility of PD-L1 prediction from H&E in breast cancer. In a cohort of 3,376 patients, our system predicts the PD-L1 status in a high area under the curve (AUC) of 0.91 – 0.93. Our system is validated on two external datasets, including an independent clinical trial cohort, showing consistent prediction performance. Furthermore, the proposed system predicts which cases are prone to pathologists miss-interpretation, showing it can serve as a decision support and quality assurance system in clinical practice.
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The Gromov-Hausdorff (GH) distance is traditionally used for measuring distances between metric spaces. It was adapted for non-rigid shape comparison and matching of isometric surfaces, and is defined as the minimal distortion of embedding one surface into the other, while the optimal correspondence can be described as the map that minimizes this distortion. Solving such a minimization is a hard combinatorial problem that requires pre-computation and storing of all pairwise geodesic distances for the matched surfaces. A popular way for compact representation of functions on surfaces is by projecting them into the leading eigenfunctions of the Laplace-Beltrami Operator (LBO). When truncated, The basis of the LBO is known to be the optimal for representing functions with bounded gradient in a min-max sense. Methods such as Spectral-GMDS exploit this idea to simplify and efficiently approximate a minimization related to the GH distance by operating in the truncated spectral domain, and obtain state of the art results for matching of nearly isometric shapes. However, when considering only a specific set of functions on the surface, such as geodesic distances, an optimized basis could be considered as an even better alternative. Moreover, current simplifications of approximating the GH distance introduce errors due to low rank approximations and relaxations of the permutation matrices.Here, we define the geodesic distance basis, which is optimal for compact approximation of geodesic distances, in terms of Frobenius norm. We use the suggested basis to extract the Geodesic Distance Descriptor (GDD), which encodes the geodesic distances information as a linear combination of the basis functions. We then show how these ideas can be used to efficiently and accurately approximate the metric spaces matching problem with almost no loss of information. We incorporate recent methods for efficient approximation of the proposed basis and descriptor without actually computing and storing all geodesic distances. These observations are used to construct a very simple and efficient procedure for shape correspondence. Experimental results show that the GDD improves both accuracy and efficiency of state of the art shape matching procedures.
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