Primary brain tumors including gliomas continue to pose significant management challenges to clinicians. While the presentation, the pathology, and the clinical course of these lesions are variable, the initial investigations are usually similar. Patients who are suspected to have a brain tumor will be assessed with computed tomography (CT) and magnetic resonance imaging (MRI). The imaging findings are used by neurosurgeons to determine the feasibility of surgical resection and plan such an undertaking. Imaging studies are also an indispensable tool in tracking tumor progression or its response to treatment. As these imaging studies are non-invasive, relatively cheap and accessible to patients, there have been many efforts over the past two decades to increase the amount of clinically-relevant information that can be extracted from brain imaging. Most recently, artificial intelligence (AI) techniques have been employed to segment and characterize brain tumors, as well as to detect progression or treatment-response. However, the clinical utility of such endeavours remains limited due to challenges in data collection and annotation, model training, and the reliability of AI-generated information. We provide a review of recent advances in addressing the above challenges. First, to overcome the challenge of data paucity, different image imputation and synthesis techniques along with annotation collection efforts are summarized. Next, various training strategies are presented to meet multiple desiderata, such as model performance, generalization ability, data privacy protection, and learning with sparse annotations. Finally, standardized performance evaluation and model interpretability methods have been reviewed. We believe that these technical approaches will facilitate the development of a fully-functional AI tool in the clinical care of patients with gliomas.

中文翻译：

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神经胶质瘤成像中的人工智能：挑战与进步。

包括胶质瘤在内的原发性脑肿瘤继续给临床医生带来重大的管理挑战。虽然这些病变的表现、病理学和临床过程是可变的，但最初的调查通常是相似的。怀疑患有脑肿瘤的患者将接受计算机断层扫描 (CT) 和磁共振成像 (MRI) 的评估。神经外科医生使用成像结果来确定手术切除的可行性并计划这样的工作。影像学研究也是追踪肿瘤进展或其对治疗反应的不可或缺的工具。由于这些影像学研究是非侵入性的、相对便宜且可供患者使用，因此在过去的二十年中已经做出了许多努力来增加可以从脑成像中提取的临床相关信息的数量。最近，人工智能 (AI) 技术已被用于分割和表征脑肿瘤，以及检测进展或治疗反应。然而，由于数据收集和注释、模型训练以及人工智能生成信息的可靠性方面的挑战，此类努力的临床效用仍然有限。我们回顾了应对上述挑战的最新进展。首先，为了克服数据匮乏的挑战，总结了不同的图像插补和合成技术以及注释收集工作。接下来，提出了各种训练策略来满足多个需求，例如模型性能、泛化能力、数据隐私保护和稀疏注释学习。最后，已经审查了标准化的绩效评估和模型可解释性方法。我们相信，这些技术方法将促进在神经胶质瘤患者的临床护理中开发功能齐全的人工智能工具。