Radiomics, deep learning and early diagnosis in oncology.
Radiomics, deep learning and early diagnosis in oncology.
复制标题
放射组学、深度学习和肿瘤学早期诊断。
DOI：
10.1042/etls20210218
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发表时间：
2021-12-21
影响因子：
3.8
通讯作者：
Wei P
中科院分区：
文献类型：
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作者：
Wei P
医学成像，包括 X 射线、计算机断层扫描 (CT) 和磁共振成像 (MRI)，在癌症的早期检测、诊断和治疗反应预测中发挥着关键作用。为了减轻放射科医生的任务并帮助解决具有挑战性的病例，计算机辅助诊断在过去十年中得到了迅速发展，早期由放射组学开创，最近在深度学习的推动下。在这篇小综述中，我以乳腺癌为例，回顾了医学成像及其定量模型（包括放射组学和深度学习）如何改进乳腺癌的早期检测和治疗反应预测。我还概述了放射组学和深度学习的共同点以及它们在建模过程、样本量要求和计算实现方面的差异。最后，我讨论了将深度学习模型和软件集成到临床实践中所面临的挑战和努力。
Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists’ task and help with challenging cases, computer-aided diagnosis has been developing rapidly in the past decade, pioneered by radiomics early on, and more recently, driven by deep learning. In this mini-review, I use breast cancer as an example and review how medical imaging and its quantitative modeling, including radiomics and deep learning, have improved the early detection and treatment response prediction of breast cancer. I also outline what radiomics and deep learning share in common and how they differ in terms of modeling procedure, sample size requirement, and computational implementation. Finally, I discuss the challenges and efforts entailed to integrate deep learning models and software in clinical practice.