Curing More Cervical Cancer Patients

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Cervical cancer is highly curable when caught early. But in a third of cases, the tumor responds poorly to treatment or recurs later, when cure is much less likely.

Quicker identification of non-responding tumors appears to be possible using a new mathematical model developed by scientists at the Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute.

The model uses information from magnetic resonance imaging (MRI) scans taken before and during treatment to monitor changes in tumor size. That information is plugged into the model to predict whether a particular case is responding well to therapy. If not, the patient can be changed to a more aggressive or experimental treatment midway through therapy, something not possible now.

The study, reported in the journal Cancer Research, uses MRI scans and outcome information from 80 cervical cancer patients receiving a standard course of radiation treatment designed to cure their cancer.

"The model enables us to better interpret clinical data and predict therapy outcomes for individual patients," says principal investigator Jian Z. Wang, assistant professor of radiation medicine and a radiation physicist at the OSUCCC-James.

"The outcome predictions presented in this paper were solely based on changes in tumor volume as derived from MRI scans, which can be easily accessed even in community hospitals," Wang says. "The model is very robust and can provide a prediction accuracy of 90 percent for local tumor control and recurrence".

A strength of the new model, says first author Zhibin Huang, is its use of MRI data to estimate three factors that play key roles in tumor shrinkage and that vary from patient to patient - the proportion of tumor cells that survive radiation exposure, the speed at which the body removes dead cells from the tumor, and the growth rate of surviving tumor cells.

The model is applicable to all cervical cancer patients, and the researchers are in the process of developing a model that can be applied to other cancer sites, Wang says.

Co-author Dr. Nina A. Mayr, professor of radiation medicine at Ohio State, notes that the size of cervical tumors is currently estimated by touch, or palpation, which is often imprecise. Furthermore, shrinkage of a tumor may not be apparent until months after treatment has ended.

Other clinical factors currently used to predict a tumor's response to treatment include the tumor's stage, whether it has invaded nearby lymph nodes and its microscopic appearance.

"Our kinetic model helps us understand the underlying biological mechanisms of the rather complicated living tissue that is a tumor," Wang says. "It enables us to better interpret clinical data and predict therapy outcomes, which is critical for identifying the most effective treatment for personalized medicine".

This study was supported by a grant from the National Cancer Institute.


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