Kevin Scibilia, Doctoral Student of Integrated Mathematical Oncology at Moffitt Cancer Center, shared a post on LinkedIn about a paper he co-authored with colleagues published in Cancer Research:
“MathOnco on the cover of Cancer Research! Modeling is emerging as a promising tool for understanding and treating cancer, and is now influencing numerous clinical decisions and trials. Find out how and why in our latest article.”
Quoting AACR Journals’ post:
“Read the December 15 issue of Cancer Research.
This issue includes five new research articles in the Cancer Research special series on Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI:
- Spatially Discontinuous Mutation Topographies in Ductal Carcinoma In Situ Reveal Noncompetitive Growth Dynamics
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology
- Mathematical Modeling Quantifies ‘Just-Right’ APC Inactivation for Colorectal Cancer Initiation
- Artificial Intelligence and Multimodality Data Integration Decipher Tertiary Lymphoid Structure Maturity in Gastric Cancer
- Large-Scale T-cell Receptor Repertoire Profiling Unveils Tumor-Specific Signals for Diagnosing Indeterminate Pulmonary Nodules.”
Heiko Enderling, Professor of Radiation Oncology at MD Anderson Cancer Center, shared Kevin Scibilia’s post, adding:
“Very nice to see Mathematical Oncology on the cover of Cancer Research. Shoutout to friends and collaborators at Moffitt Cancer Center and congratulations on their very insightful review on clinical translation of mathematical and computational models in oncology.”
Title: Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making
Authors: Kevin R. Scibilia, Kit Gallagher, M.A. Masud, Mark Robertson-Tessi, Chandler D. Gatenbee, Jeffrey West, Paul Llamas, Sandhya Prabhakaran, Jill Gallaher, and Alexander R.A. Anderson.
You can read the Full Article in Cancer Research.
More posts on Mathematical Oncology on OncoDaily.
