Radiomics‐based machine learning for the diagnosis of lymph node metastases in patients with head and neck cancer: Systematic review

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 C...

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Published in:Head & neck 2023-02, Vol.45 (2), p.482-491
Main 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
Format: Article
Language:English
Subjects:
Head & neck cancer
head and neck
Head and neck carcinoma
Head and Neck Neoplasms - diagnostic imaging
Head and Neck Neoplasms - pathology
Humans
Learning algorithms
Lymph nodes
Lymph Nodes - diagnostic imaging
Lymph Nodes - pathology
Lymphatic Metastasis - pathology
lymphnodes
Machine Learning
Metastases
Radiomics
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Summary: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.
ISSN:1043-3074
1097-0347
DOI:10.1002/hed.27239