New Content Collection in JCO Clinical Cancer Informatics Delves Into the Field of Mathematical Oncology
Mathematics, modeling, and simulation to study cancer are topics covered in a new special series in JCO Clinical Cancer Informatics (JCO CCI). On April 24, JCO CCI published its fourth special collection of articles, “Mathematical Oncology,” which showcases the current state of the field and new applications for modeling and simulation—whether it’s translating patient-specific data into a treatment plan for personalized medicine; using game theory to optimize neoadjuvant combination therapies; or creating cell-based models that allow the analysis of high-dimensional single-cell sequencing data.
In their introduction to the collection, Guest Editors Russell Rockne, PhD, and Jacob G. Scott, MD, DPhil, noted the evolution of the field over the past few decades from theoretical work in mathematical oncology and biology—which provided a foundation for making predictions about cancer progression—to work that now uses real-world scenarios and data.
“Mathematical models can now, more than ever, be directly applied to real scenarios and readily tested using large amounts of biological and clinical data,” they write. “In many ways, the era of big data has enabled an era of big theory to meet both the conceptual and practical challenges of analyzing and, to a limited extent, understanding, big data.”
The invited articles in the collection focus on three areas within the field of mathematics oncology, Dr. Rockne and Dr. Scott observed: “(1) Modeling the relationship between cancer therapy and the immune system; (2) optimizing personalized medicine through clinical imaging and predictive mathematical modeling; and (3) connecting individual cells to tumor behavior.”
The invited articles from a multidisciplinary group of authors showcase the way in which mathematics is playing an increasing role in oncology. As Dr. Rockne and Dr. Scott explain, “Mathematical oncology, which has historically been a subdiscipline of mathematics, has begun to migrate toward being a subdiscipline of oncology itself. Its practitioners, including the authors whose papers are highlighted herein, come from disparate fields, including engineering, physics, and computer science, yet all share the common goal of bringing their computational and theoretical tools to bear on the fight against cancer.”
This meeting of big data and clinical work is exactly the type of effort that JCO CCI is dedicated to sharing. The guest editors note that just as JCO CCI is focused on the intersection of informatics and the clinician—with its focus on research into the “tools, knowledge, and infrastructure required to translate vast amounts of data into the clinic and improve our treatment and understanding of cancer”—so, too, is the field of mathematical oncology. This discipline “has served as a bridge between the data, the biologist, and the practicing clinician,” they stated. “In this way, knowledge-based predictive mathematical modeling is used to fill in gaps in sparse data; guide and train machine learning algorithms; provide actionable interpretations of complex data sets; and make predictions of cancer progression and response to therapy on a patient-specific basis.”
All JCO CCI articles are discoverable at ascopubs.org/cci and on PubMed, MEDLINE, and the Emerging Sources Citation Index. ■
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