Effective targeted therapies for small-cell lung cancer (SCLC), the most aggressive form of lung cancer, remain urgently needed. Here we report evidence of preclinical efficacy evoked by targeting the overexpressed cell-cycle checkpoint kinase CHK1 in SCLC. Our studies employed RNAi-mediated attenuation or pharmacologic blockade with the novel second-generation CHK1 inhibitor prexasertib (LY2606368), currently in clinical trials. In SCLC models in vitro and in vivo, LY2606368 exhibited strong single-agent efficacy, augmented the effects of cisplatin or the PARP inhibitor olaparib, and improved the response of platinum-resistant models. Proteomic analysis identified CHK1 and MYC as top predictive biomarkers of LY2606368 sensitivity, suggesting that CHK1 inhibition may be especially effective in SCLC with MYC amplification or MYC protein overexpression. Our findings provide a preclinical proof of concept supporting the initiation of a clinical efficacy trial in patients with platinum-sensitive or platinum-resistant relapsed SCLC.

Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change-areas such as medical image analysis for the improved diagnosis of Alzheimer's disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer's, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.

Study Design: Cross sectional database study. Objective: To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis. Methods: Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151). Results: The U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant, P > .05). Conclusion: This study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.

The field of image analysis has seen large gains in recent years due to advances in deep convolutional neural networks (CNNs). Work in biomedical imaging domains, however, has seen more limited success primarily due to limited training data, which is often expensive to collect. We propose a framework that leverages deep CNNs pretrained on large, non-biomedical image data sets. Our hypothesis, which we affirm empirically, is that these pretrained networks learn cross-domain features that improve low-level interpretation of images. We evaluate our model on brain imaging data to show our approach improves the ability to diagnose Alzheimer's Disease from patient brain MRIs. Importantly, our results show that pretraining and the use of deep residual networks are crucial to seeing large improvements in diagnosis accuracy.