Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI

• Published in: Cancers Vol. 13; no. 11; p. 2522
• Main Authors: Caruso, Damiano, Polici, Michela, Zerunian, Marta, Pucciarelli, Francesco, Guido, Gisella, Polidori, Tiziano, Landolfi, Federica, Nicolai, Matteo, Lucertini, Elena, Tarallo, Mariarita, Bracci, Benedetta, Nacci, Ilaria, Rucci, Carlotta, Iannicelli, Elsa, Laghi, Andrea
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 21.05.2021; MDPI
• Subjects: Artificial intelligence; Bias; Biomarkers; Biopsy; Cancer; Cluster analysis; Computed tomography; Decision making; Diagnosis; Feature selection; Image processing; Magnetic resonance imaging; Medical imaging; Medical prognosis; Neural networks; Patients; Precision medicine; Predictions; Principal components analysis; Prognosis; Radiomics; Reproducibility; Review; Scanners; Segmentation; Standardization
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers13112522

Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.