115,777 research outputs found
Genomic characterisation of Eμ-Myc mouse lymphomas identifies Bcor as a Myc co-operative tumour-suppressor gene
The Eμ-Myc mouse is an extensively used model of MYC driven malignancy; however to date there has only been partial characterization of MYC co-operative mutations leading to spontaneous lymphomagenesis. Here we sequence spontaneously arising Eμ-Myc lymphomas to define transgene architecture, somatic mutations, and structural alterations. We identify frequent disruptive mutations in the PRC1-like component and BCL6-corepressor gene Bcor. Moreover, we find unexpected concomitant multigenic lesions involving Cdkn2a loss and other cancer genes including Nras, Kras and Bcor. These findings challenge the assumed two-hit model of Eμ-Myc lymphoma and demonstrate a functional in vivo role for Bcor in suppressing tumorigenesis.We acknowledge the following
funding agencies: Leukaemia Foundation of Australia, Arrow Bone Marrow Transplant
Foundation, National Health and Medical Research Council Australia, Cancer Council
Victoria, Victorian Cancer Agency, Australian Cancer Research Foundation, Peter
MacCallum Cancer Centre Foundation, National Institutes of Health
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
Mammography screening for early detection of breast lesions currently suffers
from high amounts of false positive findings, which result in unnecessary
invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many
of these false-positive findings prior to biopsy. Current approaches estimate
tissue properties by means of quantitative parameters taken from generative,
biophysical models fit to the q-space encoded signal under certain assumptions
regarding noise and spatial homogeneity. This process is prone to fitting
instability and partial information loss due to model simplicity. We reveal
unexplored potentials of the signal by integrating all data processing
components into a convolutional neural network (CNN) architecture that is
designed to propagate clinical target information down to the raw input images.
This approach enables simultaneous and target-specific optimization of image
normalization, signal exploitation, global representation learning and
classification. Using a multicentric data set of 222 patients, we demonstrate
that our approach significantly improves clinical decision making with respect
to the current state of the art.Comment: Accepted conference paper at MICCAI 201
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
While deep learning methods are increasingly being applied to tasks such as
computer-aided diagnosis, these models are difficult to interpret, do not
incorporate prior domain knowledge, and are often considered as a "black-box."
The lack of model interpretability hinders them from being fully understood by
target users such as radiologists. In this paper, we present a novel
interpretable deep hierarchical semantic convolutional neural network (HSCNN)
to predict whether a given pulmonary nodule observed on a computed tomography
(CT) scan is malignant. Our network provides two levels of output: 1) low-level
radiologist semantic features, and 2) a high-level malignancy prediction score.
The low-level semantic outputs quantify the diagnostic features used by
radiologists and serve to explain how the model interprets the images in an
expert-driven manner. The information from these low-level tasks, along with
the representations learned by the convolutional layers, are then combined and
used to infer the high-level task of predicting nodule malignancy. This unified
architecture is trained by optimizing a global loss function including both
low- and high-level tasks, thereby learning all the parameters within a joint
framework. Our experimental results using the Lung Image Database Consortium
(LIDC) show that the proposed method not only produces interpretable lung
cancer predictions but also achieves significantly better results compared to
common 3D CNN approaches
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