Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics

Abstract

Motivation: Breast cancer is the leading cause of cancer deaths among women today. Survival rates in developing countries are around 50%-60% due to late detection. A personalized, accurate risk scoring method can help in targeting the right population for follow-up tests and enables early detection of breast abnormalities. Most of the available risk assessment tools use generic and weakly correlated features like age, weight, height etc. While a personalized risk scoring from screening modalities such as mammography and ultrasound could be helpful, these tests are limited to very few metropolitan hospitals in developing countries due to high capital cost, operational expenses and interpretation expertise needed for a large screening population.

Methods: We propose and analyze a new personalized risk framework called Thermalytix Risk Score (TRS) to identify a high-risk target population for regular screening and enable early stage breast cancer detection at scale. This technique uses Artificial Intelligence (AI) over thermal images to automatically generate a breast health risk score. This risk score is mainly derived from two sub-scores namely, vascular score and hotspot score. A hotspot score signifies the abnormality seen from irregular asymmetric heat patterns seen on the skin surface, whereas vascular score predicts the presence of asymmetric vascular activity. These scores are generated using machine learning algorithms over medically interpretable parameters that describes the metabolic activity inside the breast tissue and indicate the presence of a possible malignancy even in asymptomatic women.

Results: The proposed personalized risk score was tested on 769 subjects in four breast cancer screening facilities. The subjects' age ranged from 18 to 82 years with a median of around 45 years. Out of the 769 subjects, 185 subjects were diagnosed with a breast malignancy by an expert radiologist after mammography, ultrasound and/or histopathology. Our personalized AI based risk score achieved an area under the receiver-operator curve (AUC) of 0.89 when compared to an age normalized risk score that showed an AUC of 0.68. We also found that if the computed risk score is used to place individuals into four risk groups, the likelihood of malignancy also increases monotonically with the risk grouping level.

Conclusion: The proposed AI based personalized risk score uses breast thermal image patterns for risk computation and compares favorably to other generic risk estimation approaches. The proposed risk framework solution is automated, affordable, non-invasive, non-contact and radiation free and works for a wide age range of women from 18 to 82 years, including young women with dense breasts. The proposed score might be further used to assign subjects into one of the four risk groups and provide guidance on the periodicity of screening needed. In addition, the automatically annotated thermal images localizes the potential abnormal regions and might empower the physician to create a better personalized care.

Keywords: Artificial intelligence; Breast cancer; Machine learning; Risk assessment; Thermalytix; Thermography.