Johns Hopkins creates web tool to predict cancer recurrence

For women with early-stage breast cancer, deciding whether or not to undergo expensive molecular imaging tests can be a difficult decision. While the tests can assess risk of recurrence, they may not be necessary at all—prompting scientists at the Johns Hopkins Kimmel Cancer Center to create a web app that can aid in patients' decision-making.

Using tumor pathology data culled from more than 1,000 patients, the app can predict which patients' risk category had they undergone molecular imaging tests. The risk of recurrence informs clinicians of the necessity of treatments like chemotherapy or anti-horomone medication.

"While such tests can be informative, clinicians ideally should use them when there is a gray zone in which high-quality pathology measures alone do not give doctors all the information they need. Molecular tests should complement, not duplicate, information that is already available,” says Antonio Wolff, MD, Professor at Baltimore's Johns Hopkins University School of Medicine and member of the Kimmel Cancer Center.

Clinicians upload standard tumor pathology data such as Ki67 protein levels, the expression of the HER2 gene and the grade of the cancer.

Read more about the Breast Cancer Recurrence Score Estimator by following the link below: