How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Abstract

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical “how-to” guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis

“…The various radiomics features used are shape features, first-order histogram-based features, second-order texture features, and higher-order transform-based features. 38 Fusion of deep features and radiomic features can improve the efficiency in BC detection and classification.…”

Radiomics in Breast Imaging: Future Development

“…Recent studies suggested that the application of radiomics in mammography could produce significant results in the diagnosis of breast cancer. In 2019, in the study by Li et al [ 48 ], the extraction of quantitative features from the analysis of the mammograms of 182 patients proved to be better than simple qualitative analysis of the lesion [ 49 ]. Furthermore, from the study by Tagliafico et al [ 50 ], also conducted in 2019, on 40 patients with high breast density, it was shown that the use of radiomics in tomosynthesis was useful in differentiating malignant findings from normal breast tissue [ 49 ].…”

Benchmarking radiomic features using CT simulated liver lesions

“…Radiomics, a burgeoning field within medical imaging, involves the extraction and analysis of a vast array of quantitative features from medical images [1][2][3][4][5][6]. These features encompass a wide range of information, capturing subtle and complex patterns that may not be discernible to the human eye alone [6][7][8][9][10][11].…”

“…These features encompass a wide range of information, capturing subtle and complex patterns that may not be discernible to the human eye alone [6][7][8][9][10][11]. Radiomics has garnered significant attention due to its potential to provide valuable insights for personalized medicine, treatment response prediction, and disease diagnosis [3,4,8,9,12]. However, the inherent variability in image acquisition protocols, equipment, and analysis techniques has led to a pressing need for standardized methodologies and benchmarking in radiomics research [13,14].…”