Agosti Edoardo, Mangili Marcello, Panciani Pier Paolo, Ugga Lorenzo, Rampinelli Vittorio, Ravanelli Marco, Fiorindi Alessandro, Fontanella Marco Maria
Neurosurgery Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy.
Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", 80131 Naples, Italy.
J Clin Med. 2025 Sep 18;14(18):6595. doi: 10.3390/jcm14186595.
: Radiomics offers quantitative, high-dimensional data from conventional imaging and holds promise for improving diagnosis and treatment of pituitary adenomas (PAs). This systematic review aimed to synthesize current clinical applications of radiomics in PAs, focusing on diagnostic, predictive, and prognostic modeling. : This review followed the PRISMA 2020 guidelines. A systematic search was performed in PubMed, Scopus, and Web of Science on 10 January 2024, and updated on 5 March 2024, using predefined keywords and MeSH terms. Studies were included if they evaluated radiomics-based models using MRI for diagnosis, classification, consistency, invasiveness, treatment response, or recurrence in human PA populations. Data extraction included study design, sample size, MRI sequences, feature types, machine learning algorithms, and model performance metrics. Study quality was assessed via the Newcastle-Ottawa Scale. Descriptive statistics summarized study characteristics; no meta-analysis was performed due to heterogeneity. : Out of 341 identified articles, 49 studies met inclusion criteria, encompassing a total of more than 9350 patients. The majority were retrospective (43 studies, 88%). MRI sequences used included T2-weighted imaging (35 studies, 71%), contrast-enhanced T1WI (34 studies, 69%), and T1WI (21 studies, 43%). PyRadiomics was the most common feature extraction tool (20 studies, 41%). Machine learning was employed in 43 studies (88%), predominantly support vector machines (16 studies, 33%), random forests (9 studies, 18%), and logistic regression (9 studies, 18%). Deep learning methods were applied in 17 studies (35%). Regarding diagnostic performance, 22 studies (45%) reported an (AUC) ≥0.85 in test datasets. External validation was performed in only 6 studies (12%). Radiomics applications included histological subtype prediction (14 studies, 29%), surgical outcome prediction (13 studies, 27%), invasiveness assessment (7 studies, 15%), tumor consistency evaluation (8 studies, 16%), and response to medical or radiotherapy treatments (3 studies, 6%). One study (2%) addressed automated segmentation and volumetry. : Radiomics enables high-performance, noninvasive prediction of PA subtypes, consistency, invasiveness, treatment response, and recurrence, with 22 studies (45%) reporting AUC ≥0.85. Despite promising results, clinical translation remains limited by methodological heterogeneity, low external validation (6 studies, 12%), and lack of standardization.
放射组学可从传统影像中提供定量的高维数据，有望改善垂体腺瘤（PA）的诊断和治疗。本系统评价旨在综合放射组学在PA中的当前临床应用，重点关注诊断、预测和预后模型。：本评价遵循PRISMA 2020指南。于2024年1月10日在PubMed、Scopus和Web of Science上进行了系统检索，并于2024年3月5日更新，使用了预定义的关键词和医学主题词。如果研究评估了基于放射组学的模型在人类PA群体中用于诊断、分类、一致性、侵袭性、治疗反应或复发的情况，则纳入研究。数据提取包括研究设计、样本量、MRI序列、特征类型机器学习算法和模型性能指标。通过纽卡斯尔-渥太华量表评估研究质量。描述性统计总结了研究特征；由于异质性，未进行荟萃分析。：在341篇已识别的文章中，49项研究符合纳入标准，共纳入9350多名患者。大多数研究为回顾性研究（43项研究，88%）。使用的MRI序列包括T2加权成像（35项研究，71%）、对比增强T1WI（34项研究，69%）和T1WI（21项研究，43%）。PyRadiomics是最常用的特征提取工具（20项研究，41%）。43项研究（88%）采用了机器学习，主要是支持向量机（16项研究，33%）、随机森林（9项研究，18%）和逻辑回归（9项研究，18%）。17项研究（35%）应用了深度学习方法。关于诊断性能，22项研究（45%）报告测试数据集的曲线下面积（AUC）≥0.85。仅6项研究（12%）进行了外部验证。放射组学应用包括组织学亚型预测（14项研究，29%）、手术结果预测（13项研究，27%）、侵袭性评估（7项研究，15%）、肿瘤一致性评估（8项研究，16%）以及对药物或放射治疗的反应（3项研究，6%）。一项研究（2%）涉及自动分割和体积测量。：放射组学能够对PA亚型、一致性、侵袭性、治疗反应和复发进行高性能、无创预测，22项研究（45%）报告AUC≥0.85。尽管取得了有前景的结果，但临床转化仍受到方法学异质性、低外部验证（6项研究，12%）和缺乏标准化的限制。
