Artificial intelligence in CT and MR imaging for oncological Applications Review
| Authors: | Paudyal, R.; Shah, A. D.; Akin, O.; Do, R. K. G.; Konar, A. S.; Hatzoglou, V.; Mahmood, U.; Lee, N.; Wong, R. J.; Banerjee, S.; Shin, J.; Veeraraghavan, H.; Shukla-Dave, A. |
| Review Title: | Artificial intelligence in CT and MR imaging for oncological Applications |
| Abstract: | Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients. © 2023 by the authors. |
| Keywords: | artificial intelligence; computed tomography; diffusion-weighted magnetic resonance imaging; cancer; deep learning; radiomics |
| Journal Title: | Cancers |
| Volume: | 15 |
| Issue: | 9 |
| ISSN: | 2072-6694 |
| Publisher: | MDPI |
| Date Published: | 2023-05-01 |
| Start Page: | 2573 |
| Language: | English |
| DOI: | 10.3390/cancers15092573 |
| PROVIDER: | scopus |
| PMCID: | PMC10177423 |
| PUBMED: | 37174039 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK author: Amita Shukla-Dave -- Source: Scopus |
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