A new computer model may be able to forecast cervical cancer recurrence in an individual based on how the tumor changes in size after the first rounds of radiation therapy, a team of researchers recently found.
The team, led by Jian Z. Wang, PhD, director of the Radiation Response Modeling Program at the James Cancer Hospital and Solove Research Institute of Ohio State University, studied 80 women with cervical cancer who were diagnosed with tumors ranging from as small as a marble to as large as a grapefruit.
Each woman was treated with a course of radiation therapy. After several weeks of treatment, Wang and his colleagues measured the size of each shrinking tumor with an MRI scanner.
With this data, the scientists used a mathematical formula to calculate 2 important factors that affect each tumor’s response to radiotherapy—the fraction of cancerous cells that survived each of the daily radiation treatments, and how long each woman’s body took to flush out the destroyed cells. These numbers can predict how likely it is for the cancer to return years later.
The team found that if radiation treatment killed at least 70% of a tumor, a woman had a 30% better chance of avoiding recurrence than someone with a more resistant tumor. Furthermore, women whose bodies took more than 22 days to clear out the dead cells were almost 50% more likely to see their tumors return.
These results suggest that not all cervical cancers are the same. Patients should be grouped based on whether their tumors are radioresistant or radiosensitive, said Dr Wang.
The findings were presented at the 51st Annual Meeting of the American Association of Physicists in Medicine in Anaheim in July. Nina A. Mayr, professor of radiation oncology at the Ohio State University James Cancer Hospital and Solove Research Center, indicated that this approach might pay dividends for other kinds of cancer. “Using similar techniques, ongoing projects from our group are tackling other cancer sites, such as lung and prostate cancer.”