Background: Positron emission tomography (PET) is a valuable tool for the assessment of lymphoma, while artificial intelligence (AI) holds promise as a reliable resource for the analysis of medical images. In this context, we systematically reviewed the applications of deep learning (DL) for the interpretation of lymphoma PET images.Methods: We searched PubMed until 11 September 2024 for studies developing DL models for the evaluation of PET images of patients with lymphoma. The risk of bias and applicability concerns were assessed using the prediction model risk of bias assessment tool (PROBAST). The articles included were categorized and presented based on the task performed by the proposed models. Our study was registered with the international prospective register of systematic reviews, PROSPERO, as CRD42024600026.Results: From 71 papers initially retrieved, 21 studies with a total of 9402 participants were ultimately included in our review. The proposed models achieved a promising performance in diverse medical tasks, namely, the detection and histological classification of lesions, the differential diagnosis of lymphoma from other conditions, the quantification of metabolic tumor volume, and the prediction of treatment response and survival with areas under the curve, F1-scores, andR2values of up to 0.963, 87.49%, and 0.94, respectively.Discussion: The primary limitations of several studies were the small number of participants and the absence of external validation. In conclusion, the interpretation of lymphoma PET images can reliably be aided by DL models, which are not designed to replace physicians but to assist them in managing large volumes of scans through rapid and accurate calculations, alleviate their workload, and provide them with decision support tools for precise care and improved outcomes.
背景：正电子发射断层扫描（PET）是评估淋巴瘤的重要工具，而人工智能（AI）有望成为医学影像分析的可靠资源。在此背景下，我们系统综述了深度学习（DL）在淋巴瘤PET图像解读中的应用。 方法：我们检索了截至2024年9月11日的PubMed数据库，查找开发用于评估淋巴瘤患者PET图像的深度学习模型的研究。使用预测模型偏倚风险评估工具（PROBAST）评估偏倚风险和适用性问题。纳入的文章根据所提出模型执行的任务进行分类和呈现。本研究已在系统综述国际前瞻性注册平台PROSPERO注册，注册号为CRD42024600026。 结果：从最初检索到的71篇论文中，最终有21项研究纳入本综述，共涉及9402名参与者。所提出的模型在多种医学任务中表现出良好性能，包括病灶检测与组织学分类、淋巴瘤与其他疾病的鉴别诊断、代谢肿瘤体积的量化，以及治疗反应和生存预测，其曲线下面积、F1分数和R²值分别高达0.963、87.49%和0.94。 讨论：多项研究的主要局限性在于参与者数量较少且缺乏外部验证。总之，深度学习模型可可靠地辅助淋巴瘤PET图像的解读，其设计目的并非取代医生，而是通过快速准确的计算协助医生处理大量扫描图像，减轻其工作负担，并为精准诊疗和改善预后提供决策支持工具。
肿瘤(癌症)患者之家