Ardavan Saeedi

The Prognostic Value of BRAF Mutation in Colorectal Cancer and Melanoma: A Systematic Review and Meta-Analysis

Background Mutation of BRAF is a predominant event in cancers with poor prognosis such as melanoma and colorectal cancer. BRAF mutation leads to a constitutive activation of mitogen activated protein kinase pathway which is essential for cell proliferation and tumor progression. Despite tremendous efforts made to target BRAF for cancer treatment, the correlation between BRAF mutation and patient survival is still a matter of controversy. Methods/Principal Findings Clinical studies on the correlation between BRAF mutation and patient survival were retrieved from MEDLINE and EMBASE databases between June 2002 and December 2011. One hundred twenty relevant full text studies were categorized based on study design and cancer type. Publication bias was evaluated for each category and pooled hazard ratio (HR) with 95% confidence interval (CI) was calculated using random or fixed effect meta-analysis based on the percentage of heterogeneity. Twenty six studies on colorectal cancer (11,773 patients) and four studies on melanoma (674 patients) were included in our final meta-analysis. The average prevalence of BRAF mutation was 9.6% in colorectal cancer, and 47.8% in melanoma reports. We found that BRAF mutation increases the risk of mortality in colorectal cancer patients for more than two times; HR = 2.25 (95% CI, 1.82–2.83). In addition, we revealed that BRAF mutation also increases the risk of mortality in melanoma patients by 1.7 times (95% CI, 1.37–2.12). Conclusions We revealed that BRAF mutation is an absolute risk factor for patient survival in colorectal cancer and melanoma.

Nonparametric Spherical Topic Modeling with Word Embeddings.

Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere. Such a representation is well-suited for directional data. We use a Hierarchical Dirichlet Process for our base topic model and propose an efficient inference algorithm based on Stochastic Variational Inference. This model enables us to naturally exploit the semantic structures of word embeddings while flexibly discovering the number of topics. Experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence on two different text corpora while offering efficient inference.

Inferring Disease Status by Non-parametric Probabilistic Embedding

Computing similarity between all pairs of patients in a dataset enables us to group the subjects into disease subtypes and infer their disease status. However, robust and efficient computation of pairwise similarity is a challenging task for large-scale medical image datasets. We specifically target diseases where multiple subtypes of pathology present simultaneously, rendering the definition of the similarity a difficult task. To define pairwise patient similarity, we characterize each subject by a probability distribution that generates its local image descriptors. We adopt a notion of affinity between probability distributions which lends itself to similarity between subjects. Instead of approximating the distributions by a parametric family, we propose to compute the affinity measure indirectly using an approximate nearest neighbor estimator. Computing pairwise similarities enables us to embed the entire patient population into a lower dimensional manifold, mapping each subject from high-dimensional image space to an informative low-dimensional representation. We validate our method on a large-scale lung CT scan study and demonstrate the state-of-the-art prediction on an important physiologic measure of airflow (the forced expiratory volume in one second, FEV1) in addition to a 5-category clinical rating (so-called GOLD score).