AI Can Predict Risk of Recurrence for Women with Common Breast Cancer
Originally published by Pharmacy Times
A study from Gustave Roussy and the startup Owkin shows that deep learning analysis in digitized pathology slides can help classify patients with localized breast cancer between high- and low-risk of metastatic relapse in the next 5 years using artificial intelligence (AI).
This could help in therapeutic decision making and avoid unnecessary chemotherapy affecting the personal, professional, and social lives for low-risk women, according to the researchers. They added that this is one of the first proofs of concept illustrating the power of an AI model for identifying parameters associated with relapse that the human brain could not detect.
The RACE AI study was conducted among a cohort of 1400 patients managed at Gustave-Roussy between 2005 and 2013 for localized hormone-sensitive (hormone receptor-positive, humean epidermal growth factor receptor 2-negative) breast cancer, and these women were treated with surgery, radiotherapy, hormone therapy, and sometimes chemotherapy to reduce the risk of distant relapse.
Gustave Roussy and Owkin proposed a new method to direct patients identified as being at high risk towards new innovative therapies and avoiding unnecessary chemotherapy for low-risk patients. An AI model was developed in the RACE AI that would assess the risk of relapse with an area under the curve of 81% to help the practitioner determine the benefit/risk balance of chemotherapy.
The calculation is based on the patient’s clinical data combined with the analysis of stained and digitized histological slides of the tumor. The slides contain rich information for the management of cancer, making it unnecessary to develop a new technique or to equip a specific technical platform, according to the study.
A slide scanner is the only important equipment that can be found in most laboratories, as it digitized the morphological information present on the slide, according to the researchers.
This study opens up next steps for using the model on an independent cohort of patients treated outside of Gustave Roussy, and if the results are confirmed through reliable information to clinicians, this AI tool will prove to be a valuable aid to therapeutic decisions, according to the study.