概化理论
医学
淋巴结
头颈部癌
系统回顾
透明度(行为)
头颈部
放射科
医学物理学
无线电技术
科克伦图书馆
荟萃分析
肿瘤科
梅德林
内科学
放射治疗
外科
计算机科学
统计
法学
计算机安全
数学
政治学
作者
Caterina Giannitto,Giuseppe Mercante,Angela Ammirabile,Luca Cerri,Teresa De Giorgi,Ludovica Lofino,Giulia Vatteroni,E. Casiraghi,Silvia Marra,Andrea Esposito,Armando De Virgilio,Andrea Costantino,Fabio Ferreli,Victor Savevski,Giuseppe Spriano,Luca Balzarini
出处
期刊:Head & neck
[Wiley]
日期:2022-11-08
卷期号:45 (2): 482-491
被引量:6
摘要
Machine learning (ML) is increasingly used to detect lymph node (LN) metastases in head and neck (H&N) carcinoma. We systematically reviewed the literature on radiomic-based ML for the detection of pathological LNs in H&N cancer. A systematic review was conducted in PubMed, EMBASE, and the Cochrane Library. Baseline study characteristics and methodological quality items (modeling, performance evaluation, clinical utility, and transparency items) were extracted and evaluated. The qualitative synthesis is presented using descriptive statistics. Seven studies were included in this study. Overall, the methodological quality items were generally favorable for modeling (57% of studies). The studies were mostly unsuccessful in terms of transparency (85.7%), evaluation of clinical utility (71.3%), and assessment of generalizability employing independent or external validation (72.5%). ML may be able to predict LN metastases in H&N cancer. Further studies are warranted to improve the generalizability assessment, clinical utility evaluation, and transparency items.
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