Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease

Abstract

In the last decade, the analysis of the medical images has evolved significantly, applications and tools capable to extract quantitative characteristics of the images beyond the discrimination capacity of the investigator’s eye being developed. The applications of this new research field, called radiomics, presented an exponential growth with direct implications in the diagnosis and prediction of response to therapy. Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype with a severe prognosis, despite the aggressive multimodal treatments applied according to the guidelines. Radiomics has already proven the ability to differentiate TNBC from fibroadenoma. Radiomics features extracted from digital mammography may also distinguish between TNBC and non-TNBC. Recent research has identified three distinct subtypes of TNBC using IRM breast images voxel-level radiomics features (size/shape related features, texture features, sharpness). The correlation of these TNBC subtypes with the clinical response to neoadjuvant therapy may lead to the identification of biomarkers in order to guide the clinical decision. Furthermore, the variation of some radiomics features in the neoadjuvant settings provides a tool for the rapid evaluation of treatment efficacy. The association of radiomics features with already identified biomarkers can generate complex predictive and prognostic models. Standardization of image acquisition and also of radiomics feature extraction is required to validate this method in clinical practice.

Understanding Breast Cancer Aggressiveness and Its Implications in Diagnosis and Treatment

“…Several pathways in the cancer cell or tumor environment are altered in BC [ 8 ]. These changes often cause the formation of metabolites in cancer cells that can be distinguished from the metabolites of normal cells [ 14 ]. Imaging techniques such as magnetic resonance imaging (MRI) can be used to study increased metabolite uptake by tumor cells, which could translate into earlier diagnosis and improved patient outcomes [ 15 ].…”

Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets

“…Quantitative imaging features that can be used to predict the response to treatment can expand the application of radiomics in routine clinical settings [ 11 ]. Braman et al [ 27 ] proposed that both intratumoral and peritumoral features can contribute to response predictions, and that peritumoral features cannot be replaced by tumoral features.…”

“…In recent years, the application of radiomics approaches, which are less invasive, has contributed to both cancer diagnosis [ 8 ] and response prediction [ 9 – 11 ]. However, most studies have focused on baseline predictions; data on the predictive ability of radiomic features mid-treatment are still lacking.…”

Adaptive radiotherapy for breast cancer

“…Therapeutic decisions need to take into account patients clinical information as well as complex biological and imaging features of the tumor. AI can facilitate development and implementation of models to predict tumor response [96] , [97] . This may allow individual treatment tailoring, including decisions on radiotherapy dose and volumes [98] .…”