Abstract
Background: Radiomics and deep learning have been widely investigated in the quantitative analysis of medical images. Deep learning radiomics (DLR), combining the strengths of both methods, is increasingly used in head and neck cancer (HNC). This systematic review was aimed at evaluating existing studies and assessing the potential application of DLR in HNC prognosis. Materials and methods: The PubMed, Embase, Scopus, Web of Science, and Cochrane databases were searched for articles published in the past 10 years with the keywords “radiomics,” “deep learning,” and “head and neck cancer” (and synonyms). Two independent reviewers searched, screened, and reviewed the English literature. The methodological quality of each article was evaluated with the Radiomics Quality Score (RQS). Data from the studies were extracted and collected in tables. A systematic review of radiomics prognostic prediction models for HNC incorporating deep learning techniques is presented. Result: A total of eight studies, published in 2012–2022, with a varying number of patients (59–707 cases), were included. Each study used deep learning; three studies performed automatic segmentation of regions of interest (ROI), and the Dice score range for automatic segmentation was 0.75–0.81. Four studies involved extraction of deep learning features, one study combined different modality features, and two studies performed predictive model building. The range of the area under the curve (AUC) was 0.84–0.96, the range of the concordance index (C-index) was 0.72–0.82, and the range of model accuracy (ACC) was 0.72–0.96. The median total RQS for these studies was 13 (10–15), corresponding to a percentage of 36.11% (27.78%–41.67). Low scores were due to a lack of prospective design, cost-effectiveness analysis, detection and discussion of biologically relevant factors, and external validation. Conclusion: DLR has potential to improve model performance in HNC prognosis.
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