Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from18F-FDG PET/CT in the management of lymphoma patients.
淋巴瘤涵盖一系列免疫系统恶性肿瘤，其早期检测、治疗及预后评估因常与感染后/炎症性疾病表现相似而具有显著复杂性。淋巴瘤的异质性使得明确识别能够预测肿瘤生物学特征及选择最有效治疗策略的生物标志物面临挑战。尽管分子影像学技术，如正电子发射断层扫描/计算机断层扫描（PET/CT），特别是18F-FDG PET/CT，在淋巴瘤的诊断、预后判断及治疗反应评估中具有重要作用，但仍面临重大挑战。近年来，影像组学与人工智能技术已成为从医学图像中提取视觉评估难以识别的细微特征的重要工具。人工智能及其在医学/影像组学领域的快速发展和应用，为核医学领域开辟了新的机遇。影像组学与人工智能技术有望在淋巴瘤相关的多种临床场景中发挥作用。然而，仍需开展更广泛的前瞻性试验以验证其可靠性并规范应用标准。本综述旨在系统阐述当前关于人工智能与影像组学在淋巴瘤患者18F-FDG PET/CT影像中提取特征及应用的相关文献，为临床实践提供全面视角。
肿瘤(癌症)患者之家