| Autor: |
Tabari A; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.; Harvard Medical School, Boston, MA 02115, USA., Chan SM; Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA., Omar OMF; Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA., Iqbal SI; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.; Harvard Medical School, Boston, MA 02115, USA., Gee MS; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.; Harvard Medical School, Boston, MA 02115, USA., Daye D; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.; Harvard Medical School, Boston, MA 02115, USA. |
| Abstrakt: |
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities. |
