Publications by authors named "J Deasy"
Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images.
Purpose: We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA.
Methods: TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF).
Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies.
View Article and Find Full Text PDFArticle Synopsis
- Recent advancements in sequencing tech have improved how we analyze genomic and proteomic data, but we still lack efficient computational tools for large-scale data analysis.
- To fill this gap, a new bioinformatics tool called Ollivier-Ricci curvature-omics (ORCO) has been developed, which incorporates gene interactions and omic data into a biological network.
- ORCO calculates Ollivier-Ricci curvature (ORC) values to assess network robustness and gene signaling changes, and it's an open-source Python package available on GitHub for public use.
Article Synopsis
- * Researchers analyzed the MMRF CoMMpass dataset and found that WEE1 expression levels can effectively distinguish between high-risk and low-risk patients, showing a significant difference in progression-free survival (PFS).
- * The findings suggest that WEE1 expression is an independent prognostic factor for MM and may lead to new therapeutic strategies by exploring the causes of abnormal WEE1 expression.
Importance: Given high rates of locoregional control after definitive management of head and neck squamous cell carcinoma (HNSCC), better methods are needed to project distant metastasis (DM) risk. Tumor hypoxia on 18F-fluoromisonidazole (FMISO) positron emission tomography (PET) is associated with locoregional failure, but data demonstrating an association with DM are limited.
Objective: To determine whether tumor hypoxia on FMISO PET is associated with DM risk after chemoradiotherapy (CRT) for HNSCC.