Two faculty members from the Washington University Department of Radiation Oncology at Siteman Cancer Center have received honors for recent publications.
Deshan Yang, PhD, an associate professor in the Medical Physics Division, has been recognized for his article, A method to detect landmark pairs accurately between intra-patient volumetric medical images, which has been selected as one of four publications to be highlighted in the Editors’ Choice column in the November 2017 issue of Medical Physics Scitation. Manuscripts are selected for this honor based on their potential impact on the scientific community, as well as projected reader interest in the subject.
Dr. Yang designed an image processing method to accurately detect large quantities of landmark pairs in pairs of volumetric medical images. Landmark detection and pair matching were implemented in a Gaussian pyramid multiresolution scheme, and a novel landmark pair matching algorithm called Multi-Resolution Inverse-Consistent Guided Matching (MRICGM) was developed to allow accurate landmark pairs matching. The detected landmark pairs are used to quantitatively evaluate deformable image registration (DIR) methods.
This work marks a key step forward in researchers’ ability to quantitatively verify deformable image registration (DIR), which is an important technique for many radiation therapy applications, including adaptive radiotherapy. Dr. Yang plans to use his findings to assist planned future research into automatic and quantitative evaluations of DIR.
Jin Zhang, PhD, an Instructor in the Cancer Biology Division, has an article featured in the 2017 IMIA Yearbook of Medical Informatics, published by the International Medical Informatics Association (IMIA). The article, INTEGRATE: gene fusion discovery using whole genome and transcriptome data, was previously published in Genome Research, and was chosen as one of the four best articles in the ‘Bioinformatics and Translational Informatics’ subfield of medical informatics literature published in the past year.
In the article, Dr. Zhang discusses his development of a state-of-the-art gene fusion discovery algorithm that integrates whole genome and transcriptome sequencing data, demonstrating both high sensitivity and accuracy to detect novel causative mutations. Gene fusions are prevalent somatic aberrations in cancer genomes, the detection of which can serve as specific diagnostic markers, prognostic indicators, and therapeutic targets. Mono-modal data tools suffer from variability between fusion callers and from a poor sensitivity and specificity of fusion detection. Tools such as Dr. Zhang’s algorithm are especially useful to researchers, as it can assist in the exploration of the translational impact of gene fusions in cancer. Building on this recent success, Dr. Zhang has since discovered novel gene fusion biomarkers in breast cancer, liver cancer, leukemia, and other disease areas, and has also developed the first gene fusion neo-antigen discovery tool.