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Current image\u2010based HPV prediction methods are hindered by high computational demands or suboptimal performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To address these issues, we propose a methodology that employs a Siamese Neural Network architecture, integrating multi\u2010modality off\u2010the\u2010shelf features\u2014handcrafted features and 3D deep features\u2014to enhance the representation of information. We assessed the incremental benefit of combining 3D deep features from various networks and introduced manufacturer normalization. Our method was also designed for computational efficiency, utilizing transfer learning and allowing for model execution on a single\u2010CPU platform. A substantial dataset comprising 1453 valid samples was used as internal validation, a separate independent dataset for external validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our proposed model achieved superior performance compared to other methods, with an average area under the receiver operating characteristic curve (AUC) of 0.791 [95% (confidence interval, CI), 0.781\u20130.809], an average recall of 0.827 [95% CI, 0.798\u20130.858], and an average accuracy of 0.741 [95% CI, 0.730\u20130.752], indicating promise for clinical application. In the external validation, proposed method attained an AUC of 0.581 [95% CI, 0.560\u20130.603] and same network architecture with pure deep features achieved an AUC of 0.700 [95% CI, 0.682\u20130.717]. An ablation study confirmed the effectiveness of incorporating manufacturer normalization and the synergistic effect of combining different feature sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Overall, our proposed model not only outperforms existing counterparts for HPV status prediction but is also computationally accessible for use on a single\u2010CPU platform, which reduces resource requirements and enhances clinical usability.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.70061","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T22:55:52Z","timestamp":1741733752000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human papillomavirus (HPV) prediction for oropharyngeal cancer based on CT by using off\u2010the\u2010shelf features: A dual\u2010dataset study"],"prefix":"10.1002","volume":"26","author":[{"given":"Junhua","family":"Chen","sequence":"first","affiliation":[{"name":"School of Medicine Shanghai University  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Medical Engineering Department Shandong Provincial Hospital Affiliated to Shandong First Medical University  Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Chen","sequence":"additional","affiliation":[{"name":"The Fourth People's Hospital of Jiangshan City  Quzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Banghua","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Medicine Shanghai University  Shanghai China"},{"name":"School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering Shanghai University  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_9_2_1","DOI":"10.1177\/01945998221098011"},{"doi-asserted-by":"publisher","key":"e_1_2_9_3_1","DOI":"10.1016\/S1470-2045(23)00013-X"},{"doi-asserted-by":"publisher","key":"e_1_2_9_4_1","DOI":"10.1002\/ijc.32804"},{"doi-asserted-by":"publisher","key":"e_1_2_9_5_1","DOI":"10.1001\/jama.2023.13174"},{"doi-asserted-by":"publisher","key":"e_1_2_9_6_1","DOI":"10.1158\/1078-0432.CCR-11-0388"},{"doi-asserted-by":"publisher","key":"e_1_2_9_7_1","DOI":"10.1002\/jmv.29080"},{"doi-asserted-by":"publisher","key":"e_1_2_9_8_1","DOI":"10.1038\/nature14539"},{"doi-asserted-by":"publisher","key":"e_1_2_9_9_1","DOI":"10.1038\/ncomms5006"},{"key":"e_1_2_9_10_1","first-page":"826","article-title":"Understanding sources of variation to improve the reproducibility of radiomics","volume":"11","author":"Zhao B","year":"2021","journal-title":"Front Oncol"},{"doi-asserted-by":"publisher","key":"e_1_2_9_11_1","DOI":"10.1148\/radiol.2016152234"},{"doi-asserted-by":"publisher","key":"e_1_2_9_12_1","DOI":"10.3390\/cancers13092269"},{"unstructured":"ChattopadhyayP BalajiY HoffmanJ.Learning to balance specificity and invariance for in and out of domain generalization.Computer Vision\u2013ECCV2020: 16th European Conference Glasgow UK August 23\u201328 2020 Proceedings Part IX 16.Springer International Publishing;2020.","key":"e_1_2_9_13_1"},{"doi-asserted-by":"publisher","key":"e_1_2_9_14_1","DOI":"10.3390\/cancers13040786"},{"doi-asserted-by":"publisher","key":"e_1_2_9_15_1","DOI":"10.1038\/s41746-023-00901-z"},{"doi-asserted-by":"publisher","key":"e_1_2_9_16_1","DOI":"10.1088\/1361-6560\/acba74"},{"doi-asserted-by":"publisher","key":"e_1_2_9_17_1","DOI":"10.1080\/02656736.2021.1910736"},{"doi-asserted-by":"publisher","key":"e_1_2_9_18_1","DOI":"10.1097\/MD.0000000000027270"},{"doi-asserted-by":"publisher","key":"e_1_2_9_19_1","DOI":"10.1016\/j.oraloncology.2021.105239"},{"doi-asserted-by":"publisher","key":"e_1_2_9_20_1","DOI":"10.1016\/S2589-7500(23)00046-8"},{"doi-asserted-by":"publisher","key":"e_1_2_9_21_1","DOI":"10.1002\/mp.16430"},{"doi-asserted-by":"publisher","key":"e_1_2_9_22_1","DOI":"10.1161\/CIRCULATIONAHA.114.014508"},{"doi-asserted-by":"publisher","key":"e_1_2_9_23_1","DOI":"10.1038\/s41598-024-65240-9"},{"unstructured":"ChenJ.Image based HPV Prediction for Oropharyngeal Cancer. 5 May2024. 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