Intrinsic imaging phenotypes of breast cancer tumor heterogeneity at primary diagnosis can predict 10-year recurrence, according to a study published in Clinical Cancer Research.
The independent and additional prognostic value afforded by this data indicates that radiomic phenotypes could present a non-invasive characterization of tumor heterogeneity to enhance personalized prognosis and treatment of patients with breast cancer.
“If we’re only taking out a little piece of a tissue from one part of a tumor, that does not give the full picture of a person’s disease and of his or her response to specific therapies,” principle investigator Despina Kontos, PhD, associate professor of radiology in the Perelman School of Medicine at the University of Pennsylvania, said in a press release. “We know that in a lot of instances, patients are over-treated, getting therapy that may not be beneficial. Or, conversely, patients who need more aggressive therapy may not end up receiving it. The method we currently have for choosing the appropriate treatment for patients with breast cancer is not perfect, so the more steps we can take toward more personalized treatment approaches, the better.”
In this study, using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), researchers retrospectively analyzed 95 women who had primary invasive cancer with at least 10-years of follow-up from a clinical trial. For each woman, a signal enhancement ratio (SER) map was generated and from it, researchers extracted 60 radiomic features of texture and morphology from which they identified intrinsic phenotypes of tumor heterogeneity. A sample of 163 women diagnosed with primary invasive breast cancer between 2002 and 2006 were used to validate phenotype reproducibility.
Three significant phenotypes of low, medium, and high heterogeneity were identified in the discovery cohort and reproduced in the validation cohort (P < .01). Statistically significant differences (P < .05) in recurrence free survival (RFS) were shown across phenotypes. Additionally, radiomic phenotypes demonstrated added prognostic value (c = .73) predicting RFS.
“Our study shows that imaging has the potential to capture the whole tumor’s behavior without doing a procedure that is invasive or limited by sampling error,” the study’s lead author Rhea Chitalia, a PhD candidate in the School of Engineering and Applied Science at the University of Pennsylvania, said in a press release. “Women who had more heterogeneous tumors tended to have a greater risk of tumor recurrence.”
Though imaging may not entirely replace the need for tumor biopsies, Kontos indicated that radiologic methods could improve the current standard of care by offering a more detailed profile of a patient’s disease and directing personalized treatment. Next steps include broadening the analysis to a larger patient cohort and further exploring which specific markers are more predictive of particular outcomes.
“We’ve just touched the tip of the iceberg,” Kontos explained. “Our results and the validation study give us confidence that there are many opportunities for these markers to be used in a prognostic and potentially a predictive setting.”
1. Chitalia RD, Rowland J, McDonal ES, et al. Imaging phenotypes of breast cancer heterogeneity in pre-operative breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) scans predict 10-year recurrence. Clinical Cancer Research. doi:10.1158/1078-0432.CCR-18-4067.
2. Penn Researchers Predict 10-Year Breast Cancer Recurrence with MRI Scans [news release]. Philadelphia, Pennsylvania. Published December 19, 2019. newswise.com/articles/penn-researchers-predict-10-year-breast-cancer-recurrence-with-mri-scans?sc=mwhr&xy=10024642. Accessed January 15, 2020.