The central nervous system (CNS) is an important and increasingly recognized site of treatment failure in ALK-positive, non-small cell lung cancer (NSCLC) patients receiving ALK inhibitors. In this ...report, we describe two ALK-positive patients who experienced initial improvements in CNS metastases on standard-dose alectinib (600 mg twice daily), but subsequently recurred with symptomatic leptomeningeal metastases. Both patients were dose-escalated to alectinib 900 mg twice daily, resulting in repeat clinical and radiographic responses. Our results suggest that dose intensification of alectinib may be necessary to overcome incomplete ALK inhibition in the CNS and prolong the durability of responses in patients with CNS metastases, particularly those with leptomeningeal carcinomatosis.
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and ...intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.
Background Funding toward surgical research through the National Institutes of Health has decreased relative to other medical specialties. This study was initiated to characterize features of ...academically successful surgeon-scientists and departments of surgery. We hypothesized that there may be decreases in young investigators obtaining independent National Institutes of Health awards and that successful academic departments of surgery may be depending increasingly on PhD faculty. Methods The National Institutes of Health RePORTER database was queried for grants awarded to departments of surgery during fiscal years 2003 and 2013. Grant summaries were categorized by research methodology. Training of the principal investigator and academic position were determined through the RePORTER database and publicly available academic biographies. Institutions were ranked by number of grants funded. Results Between 2003 and 2013, total surgery grants awarded decreased by 19%. The number of National Institutes of Health-funded, clinically active surgeons (MDs) decreased 11%, while funded PhDs increased 9%; however, clinically active junior faculty have comprised an increasing proportion of funded MDs (from 20–38%). Shifts in research topics include an increasing proportion of investigators engaged in outcomes research. Among institutions ranking in the top 20 for surgical research in both 2003 and 2013 ( N = 15), the ratio of MDs to PhDs was 2:1 in both fiscal years. Among institutions falling out of the top 20, this ratio was less than 1:1. Conclusion There has been an expansion of outcomes-based surgical research. The most consistently successful institutions are those that actively cultivate MD researchers. Encouragingly, the number of young, independently funded surgeon-scientists in America appears to be increasing.
Accurate polyp segmentation is of great importance for colorectal cancer diagnosis and treatment. However, due to the high cost of producing accurate mask annotations, existing polyp segmentation ...methods suffer from severe data shortage and impaired model generalization. Reversely, coarse polyp bounding box annotations are more accessible. Thus, in this paper, we propose a boosted BoxPolyp model to make full use of both accurate mask and extra coarse box annotations. In practice, box annotations are applied to alleviate the over-fitting issue of previous polyp segmentation models, which generate fine-grained polyp area through the iterative boosted segmentation model. To achieve this goal, a fusion filter sampling (FFS) module is firstly proposed to generate pixel-wise pseudo labels from box annotations with less noise, leading to significant performance improvements. Besides, considering the appearance consistency of the same polyp, an image consistency (IC) loss is designed. Such IC loss explicitly narrows the distance between features extracted by two different networks, which improves the robustness of the model. Note that our BoxPolyp is a plug-and-play model, which can be merged into any appealing backbone. Quantitative and qualitative experimental results on five challenging benchmarks confirm that our proposed model outperforms previous state-of-the-art methods by a large margin.
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn features that account ...for both types of information based on variational optimization of nonparametric learning criteria. Using mutual information and Bayes error rate as example criteria, we show that high-quality features can be learned from a variational graph embedding procedure, which is solved through an iterative EM-style algorithm where the E-Step learns a variational affinity graph and the M-Step in turn embeds this graph by spectral analysis. The resulting feature learner has several appealing properties such as maximum discrimination, maximum-relevance- minimum-redundancy and locality-preserving. Experiments on benchmark face recognition data sets confirm the effectiveness of our proposed algorithms.
Fully convolutional neural networks like U-Net have been the state-of-the-art
methods in medical image segmentation. Practically, a network is highly
specialized and trained separately for each ...segmentation task. Instead of a
collection of multiple models, it is highly desirable to learn a universal data
representation for different tasks, ideally a single model with the addition of
a minimal number of parameters steered to each task. Inspired by the recent
success of multi-domain learning in image classification, for the first time we
explore a promising universal architecture that handles multiple medical
segmentation tasks and is extendable for new tasks, regardless of different
organs and imaging modalities. Our 3D Universal U-Net (3D U$^2$-Net) is built
upon separable convolution, assuming that {\it images from different domains
have domain-specific spatial correlations which can be probed with channel-wise
convolution while also share cross-channel correlations which can be modeled
with pointwise convolution}. We evaluate the 3D U$^2$-Net on five organ
segmentation datasets. Experimental results show that this universal network is
capable of competing with traditional models in terms of segmentation accuracy,
while requiring only about $1\%$ of the parameters. Additionally, we observe
that the architecture can be easily and effectively adapted to a new domain
without sacrificing performance in the domains used to learn the shared
parameterization of the universal network. We put the code of 3D U$^2$-Net into
public domain. \url{https://github.com/huangmozhilv/u2net_torch/}
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