Liang Wanru, Yang Feiyang, Teng Peihong, Zhang Tianyang, Shen Weizhang
Department of Hematology and Oncology, The Second Hospital of Jilin University, Changchun, China.
College of Computer Science and Technology, Jilin University, Changchun, China.
Digit Health. 2025 Jul 28;11:20552076251362508. doi: 10.1177/20552076251362508. eCollection 2025 Jan-Dec.
Lymphoma is a prevalent malignant tumor within the hematological system, posing significant challenges to clinical practice due to its diverse subtypes, intricate radiological and metabolic manifestations. Lymphoma segmentation studies based on positron emission tomography/computed tomography (PET/CT), CT, and magnetic resonance imaging represent key strategies for addressing these challenges. This article reviews the advancements in lymphoma segmentation research utilizing deep learning methods, offering a comparative analysis with traditional approaches, and conducting an in-depth examination and summary of aspects such as dataset characteristics, backbone networks of models, adjustments to network structures based on research objectives, and model performance. The article also explores the potential and challenges of translating deep learning-based lymphoma segmentation research into clinical scenarios, with a focus on practical clinical applications. The future research priorities in lymphoma segmentation are identified as enhancing the models' clinical generalizability, integrating into clinical workflows, reducing computational demands, and expanding high-quality datasets. These efforts aim to facilitate the broad application of deep learning in the diagnosis and treatment monitoring of lymphoma.
淋巴瘤是血液系统中一种常见的恶性肿瘤，由于其亚型多样、放射学和代谢表现复杂，给临床实践带来了重大挑战。基于正电子发射断层扫描/计算机断层扫描（PET/CT）、CT和磁共振成像的淋巴瘤分割研究是应对这些挑战的关键策略。本文综述了利用深度学习方法进行淋巴瘤分割研究的进展，与传统方法进行了对比分析，并对数据集特征、模型骨干网络、基于研究目标的网络结构调整以及模型性能等方面进行了深入研究和总结。本文还探讨了将基于深度学习的淋巴瘤分割研究转化为临床场景的潜力和挑战，重点关注实际临床应用。淋巴瘤分割未来的研究重点被确定为提高模型的临床通用性、融入临床工作流程、降低计算需求以及扩大高质量数据集。这些努力旨在促进深度学习在淋巴瘤诊断和治疗监测中的广泛应用。
