Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods.
| Autor: | Mazaheri Y, Thakur SB, Bitencourt AG, Lo Gullo R; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States., Hötker AM; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland., Bates DDB; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States., Akin O; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States. |
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| Jazyk: | angličtina |
| Zdroj: | BJR open [BJR Open] 2022 Jun 22; Vol. 4 (1), pp. 20210072. Date of Electronic Publication: 2022 Jun 22 (Print Publication: 2022). |
| DOI: | 10.1259/bjro.20210072 |
| Abstrakt: | Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow. (© 2022 The Authors. Published by the British Institute of Radiology.) |
| Databáze: | MEDLINE |
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