Background: Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes.Methods: Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment.Results: Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set.Conclusions: We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository.

EYA proteins (EYA1-4) are critical developmental transcriptional cofactors that contain an EYA domain (ED) harboring Tyr phosphatase activity. EYA proteins are largely downregulated after embryogenesis but are reexpressed in cancers, and their Tyr phosphatase activity plays an important role in the DNA damage response and tumor progression. We previously identified a class of small-molecule allosteric inhibitors that specifically inhibit the Tyr phosphatase activity of EYA2. Herein, we determined the crystal structure of the EYA2 ED in complex with NCGC00249987 (a representative compound in this class), revealing that it binds to an induced pocket distant from the active site. NCGC00249987 binding leads to a conformational change of the active site that is unfavorable for Mg²⁺ binding, thereby inhibiting EYA2′s Tyr phosphatase activity. We demonstrate, using genetic mutations, that migration, invadopodia formation, and invasion of lung adenocarcinoma cells are dependent on EYA2 Tyr phosphatase activity, whereas growth and survival are not. Further, we demonstrate that NCGC00249987 specifically targets migration, invadopodia formation, and invasion of lung cancer cells, but that it does not inhibit cell growth or survival. The compound has no effect on lung cancer cells carrying an EYA2 F290Y mutant that abolishes compound binding, indicating that NCGC00249987 is on target in lung cancer cells. These data suggest that the NCGC00249987 allosteric inhibitor can be used as a chemical probe to study the function of the EYA2 Tyr phosphatase activity in cells and may have the potential to be developed into an antimetastatic agent for cancers reliant on EYA2′s Tyr phosphatase activity.

Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.

Objective: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeline to categorize nuclei, leading to cumbersome, low-throughput and error-prone assessments. To address this issue, we propose a novel deep fully convolutional network for single-stage nucleus recognition. Methods: Instead of conducting direct pixel-wise classification, we formulate nucleus identification as a deep structured regression model. For each input image, it produces multiple proximity maps, each of which corresponds to one nucleus category and exhibits strong responses in central regions of nuclei. In addition, by taking into consideration the nucleus distribution in histopathology images, we further introduce an auxiliary task, region of interest (ROI) extraction, to assist and boost the nucleus quantification with weak ROI annotation. The proposed network can be learned in an end-to-end, pixel-to-pixel manner for simultaneous nucleus detection and classification. Results: We have evaluated this network on a pancreatic neuroendocrine tumor Ki67 image dataset, and the experiments demonstrate that our method outperforms recent state-of-the-art approaches. Conclusion: We present a new, pixel-to-pixel deep neural network with two sibling branches for effective nucleus recognition and observe that learning with another relevant task, ROI extraction, can further boost individual nucleus localization and classification. Significance: Our method provides a clean, single-stage nucleus recognition pipeline for histopathology image analysis, especially a new perspective for Ki67 image quantification, which would potentially benefit individual object quantification in whole-slide images.