Artificial intelligence for HPV status prediction based on disease-specific images in head and neck cancer: A systematic review and meta-analysis.

Accurate early detection of the human papillomavirus (HPV) status in head and neck cancer (HNC) is crucial to identify at-risk populations, stratify patients, personalized treatment options, and predict prognosis. Artificial intelligence (AI) is an emerging tool to dissect imaging features. This systematic review and meta-analysis aimed to evaluate the performance of AI to predict the HPV positivity through the HPV-associated diseased images in HNC patients. A systematic literature search was conducted in databases including Ovid-MEDLINE, Embase, and Web of Science Core Collection for studies continuously published from inception up to October 30, 2022. Search strategies included keywords such as "artificial intelligence," "head and neck cancer," "HPV," and "sensitivity & specificity." Duplicates, articles without HPV predictions, letters, scientific reports, conference abstracts, or reviews were excluded. Binary diagnostic data were then extracted to generate contingency tables and then used to calculate the pooled sensitivity (SE), specificity (SP), area under the curve (AUC), and their 95% confidence interval (CI). A random-effects model was used for meta-analysis, four subgroup analyses were further explored. Totally, 22 original studies were included in the systematic review, 15 of which were eligible to generate 33 contingency tables for meta-analysis. The pooled SE and SP for all studies were 79% (95% CI: 75-82%) and 74% (95% CI: 69-78%) respectively, with an AUC of 0.83 (95% CI: 0.79-0.86). When only selecting one contingency table with the highest accuracy from each study, our analysis revealed a pooled SE of 79% (95% CI: 75-83%), SP of 75% (95% CI: 69-79%), and an AUC of 0.84 (95% CI: 0.81-0.87). The respective heterogeneities were moderate (I 2 for SE and SP were 51.70% and 51.01%) and only low (35.99% and 21.44%). This evidence-based study showed an acceptable and promising performance for AI algorithms to predict HPV status in HNC but was not comparable to the routine p16 immunohistochemistry. The exploitation and optimization of AI algorithms warrant further research. Compared with previous studies, future studies anticipate to make progress in the selection of databases, improvement of international reporting guidelines, and application of high-quality deep learning algorithms.