Background/Objectives: Radiomics, the high-throughput extraction of quantitative features from medical imaging, offers a promising method for identifying laryngeal cancer imaging biomarkers. We aim to systematically review the literature on radiomics in laryngeal squamous cell carcinoma, assessing applications in tumour staging, prognosis, recurrence prediction, and treatment response evaluation. PROSPERO ID: CRD420251117983.Methods: MEDLINE and EMBASE databases were searched in May 2025. Inclusion criteria: studies published between 1 January 2010 and 31 January 2024, extracted radiomic features from CT, PET/CT, or MRI, and analysed outcomes related to diagnosis, staging, survival, recurrence, or treatment response in laryngeal cancer. Exclusion criteria: case reports, abstracts, editorials, reviews, or conference proceedings, exclusive focus on preclinical or animal models, lack of a clear radiomics methodology, or did not include imaging-based feature extraction. Results were synthesised narratively by modelling objective, alongside formal assessment of methodological quality using the Radiomics Quality Score (RQS).Results: Twenty studies met the inclusion criteria, with most using CT-based radiomics. Seven incorporated PET/CT. Radiomic models demonstrated moderate-to-high accuracy across tasks including T-staging, thyroid cartilage invasion, survival prediction, and local failure. Key predictive features included first-order entropy, skewness, and texture metrics such as size zone non-uniformity and GLCM correlation. Methodological variability, limited external validation, and small samples were frequent limitations.Conclusions: Radiomics holds strong promise as a non-invasive biomarker for laryngeal cancer. However, methodological heterogeneity identified through formal quality assessment indicates that improved standardisation, reproducibility, and multicentre validation are required before widespread clinical implementation.
背景/目标：影像组学作为从医学影像中高通量提取定量特征的技术，为识别喉癌影像生物标志物提供了一种前景广阔的方法。本研究旨在系统综述喉鳞状细胞癌影像组学的相关文献，评估其在肿瘤分期、预后、复发预测及治疗反应评估中的应用。PROSPERO注册号：CRD420251117983。
方法：于2025年5月检索MEDLINE和EMBASE数据库。纳入标准：2010年1月1日至2024年1月31日期间发表的研究，从CT、PET/CT或MRI中提取影像组学特征，并分析与喉癌诊断、分期、生存、复发或治疗反应相关的结局。排除标准：病例报告、摘要、社论、综述或会议论文，仅关注临床前或动物模型，缺乏明确的影像组学方法，或未包含基于影像的特征提取。结果按建模目标进行叙述性综合，同时采用影像组学质量评分（RQS）对方法学质量进行正式评估。
结果：共20项研究符合纳入标准，其中多数采用基于CT的影像组学，7项研究结合了PET/CT。影像组学模型在T分期、甲状腺软骨侵犯、生存预测及局部失败等任务中表现出中等到较高的准确性。关键预测特征包括一阶熵、偏度以及纹理指标（如区域大小不均匀性和灰度共生矩阵相关性）。方法学异质性、有限的外部验证及小样本量是常见的局限性。
结论：影像组学作为喉癌的非侵入性生物标志物具有巨大潜力。然而，通过正式质量评估发现的方法学异质性表明，在广泛临床应用前仍需改进标准化程度、可重复性及多中心验证。
肿瘤(癌症)患者之家