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We first extracted CT and dose information from intratumoral nasopharynx (GTV_nx) and lymph node (GTV_nd) regions. Then, the corresponding peritumoral regions (RING_3\u00a0mm and RING_5\u00a0mm) were also considered. Thus, the individual and combination of intratumoral and peritumoral regions were as follows: GTV_nx, GTV_nd, RING_3\u00a0mm_nx, RING_3\u00a0mm_nd, RING_5\u00a0mm_nx, RING_5\u00a0mm_nd, GTV_nxnd, RING_3\u00a0mm_nxnd, RING_5\u00a0mm_nxnd, GTV\u00a0+\u00a0RING_3\u00a0mm_nxnd, and GTV\u00a0+\u00a0RING_5\u00a0mm_nxnd. For each region, 11 models were built by combining five clinical parameters and 127 features from: (1) dose images alone; (2\u20137) fused dose and CT images via wavelet\u2010based fusion using CT weights of 0.2, 0.4, 0.6, and 0.8, gradient transfer fusion, and guided\u2010filtering\u2010based fusion (GFF); (8) fused matrices (sumMat); (9\u201310) fused features derived via feature averaging (avgFea) and feature concatenation (conFea); and finally, (11) CT images alone. The concordance index (C\u2010index) and Kaplan\u2013Meier curves with log\u2010rank test were used to assess model performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The fusion models\u2019 performance was better than single CT\/dose model on both internal and external validation. Models that combined the information from both GTV_nx and GTV_nd regions outperformed the single region model. For internal validation, GTV\u00a0+\u00a0RING_3\u00a0mm_nxnd GFF model achieved the highest C\u2010index both in recurrence\u2010free survival (RFS) and metastasis\u2010free survival (MFS) predictions (RFS: 0.822; MFS: 0.786). The highest C\u2010index in external validation set was achieved by RING_3\u00a0mm_nxnd model (RFS: 0.762; MFS: 0.719). The GTV\u00a0+\u00a0RING_3\u00a0mm_nxnd GFF model is able to significantly separate patients into high\u2010risk and low\u2010risk groups compared to dose\u2010only or CT\u2010only models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Fusion dosiomics model combining the primary tumor, the involved lymph node, and 3\u00a0mm peritumoral information outperformed single\u2010modality models for different outcome predictions, which is helpful for clinical decision\u2010making and the development of personalized treatment.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16044","type":"journal-article","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T12:49:42Z","timestamp":1667306982000},"page":"922-934","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Prognostic generalization of multi\u2010level CT\u2010dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal 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University Cancer Center, State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine  Guangzhou Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bailin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy The Second Affiliated Hospital of Guangzhou University of Chinese Medicine  Guangzhou Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy The Second Affiliated Hospital of Guangzhou University of Chinese Medicine  Guangzhou Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy The Second Affiliated Hospital of Guangzhou University of Chinese Medicine  Guangzhou Guangdong 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