Radiomics‐based machine learning for the diagnosis of lymph node metastases in patients with head and neck cancer: Systematic review. Issue 2 (8th November 2022)

• Record Type: Journal Article
• Authors: Giannitto, Caterina; Mercante, Giuseppe; Ammirabile, Angela; Cerri, Luca; De Giorgi, Teresa; Lofino, Ludovica; Vatteroni, Giulia; Casiraghi, Elena; Marra, Silvia; Esposito, Andrea Alessandro; De Virgilio, Armando; Costantino, Andrea; Ferreli, Fabio; Savevski, Victor; Spriano, Giuseppe; Balzarini, Luca
• Is Part Of: Head & neck. Volume 45:Issue 2(2023)
• Page Start: 482
• Page End: 491
• Publication Date: 2022-11-08
• Subjects: head and neck -- lymphnodes -- machine learning -- metastases -- radiomics; Head -- Diseases -- Periodicals; Neck -- Diseases -- Periodicals; Head -- Periodicals; Neck -- Periodicals; Face -- Periodicals; 617.51059
• DOI: 10.1002/hed.27239
• Languages: English
• ISSNs: 1043-3074

Abstract

Abstract: 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.