Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Abstract

Background Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology “imaging biomarker”, “radiomics”, and “artificial intelligence” are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. Methods and Results This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. Conclusion Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. Key Points: Citation Format

The potential of predictive and prognostic breast MRI (P2-bMRI)

“…Generally artificial intelligence may address a wide range of clinical use cases including predictive/prognostic tasks [ 14 , 15 ]. The status of radiomics and artificial intelligence in breast imaging extends beyond the aim of this article and has been reviewed previously [ 14 , 15 , 19 , 69 ]. There is no doubt that these methods offer a great advantage for P2-bMRI [ 14 , 15 , 17 – 19 ].…”

“…Just like in diagnostic MRI itself, the spectrum of tools available for P2-bMRI is broad as well; it ranges from semantic criteria to advanced post-processing techniques, such as artificial intelligence, including radiomics data analysis [14][15][16][17][18][19]. Generally artificial intelligence may address a wide range of clinical use cases including predictive/prognostic tasks [14,15].…”

“…Such imaging biomarkers may ultimately support the personalisation of breast cancer treatment [13]. P2-bMRI applies a wide spectrum of tools to achieve this general objective: standard and advanced multiparametric pulse sequences, structured reporting criteria (for instance BI-RADS descriptors), artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis), and deep learning that have shown compelling potential for this purpose [14][15][16][17][18][19].…”

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

“…AI-driven methods have gained prominence in diagnosis (as exemplified by Computer-Aided Detection (CADe) systems and Computer-Aided Diagnosis (CADx) systems) for tuberculosis [ 5 , 6 ], lung cancer [ 7 , 8 ] and metastatic disease to the brain[ 9 ] and also been applied to multiple other areas of clinical need [ 4 ] including notably infectious diseases as described in the context of COVID-19 [ 10 – 12 ], internal medicine[ 13 ], diabetic retinopathy [ 14 ]. In oncology, AI methods are being explored in most cancer types including prominently in lung [ 8 , 15 ], breast [ 16 , 17 ], prostate [ 18 ], central nervous system cancers [ 13 , 19 – 26 ], and other malignancies [ 27 ]. There is ongoing progress in employing AI methods towards imaging for oncology treatment e.g., generating radiation therapy volumes [ 28 ], assessing treatment response [ 29 ], and understanding and communicating prognosis [ 19 , 30 ].…”

“…To examine the variability in technical approaches to imaging analysis, a review and definition of the imaging analysis workflow are necessary ( Figure 1 ). A number of publications have now reported on the workflow involved in harnessing quantitative data embedded in images for eventual analysis in a variety of clinical settings, in the context of the COVID-19 pandemic, and in multiple oncologic settings that are imaging driven [ 2 , 11 , 16 , 25 , 34 , 35 ]. All aspects and terms involved in the image analysis workflow continue to evolve and grow in complexity ( Figure 2 ).…”