Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105366
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Health Technology and Informatics | - |
dc.contributor | Research Institute for Smart Ageing | - |
dc.contributor | Department of Biomedical Engineering | - |
dc.contributor | Mainland Development Office | - |
dc.creator | Dong, Y | - |
dc.creator | Zhang, J | - |
dc.creator | Lam, S | - |
dc.creator | Zhang, X | - |
dc.creator | Liu, A | - |
dc.creator | Teng, X | - |
dc.creator | Han, X | - |
dc.creator | Cao, J | - |
dc.creator | Li, H | - |
dc.creator | Lee, FK | - |
dc.creator | Yip, CW | - |
dc.creator | Au, K | - |
dc.creator | Zhang, Y | - |
dc.creator | Cai, J | - |
dc.date.accessioned | 2024-04-12T06:51:58Z | - |
dc.date.available | 2024-04-12T06:51:58Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105366 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Dong Y, Zhang J, Lam S, Zhang X, Liu A, Teng X, Han X, Cao J, Li H, Lee FK, et al. Multimodal Data Integration to Predict Severe Acute Oral Mucositis of Nasopharyngeal Carcinoma Patients Following Radiation Therapy. Cancers. 2023; 15(7):2032 is available at https://doi.org/10.3390/cancers15072032. | en_US |
dc.subject | Acute mucositis | en_US |
dc.subject | Dosiomics | en_US |
dc.subject | Multimodal data integration | en_US |
dc.subject | Nasopharyngeal carcinoma | en_US |
dc.subject | Radiomics | en_US |
dc.title | Multimodal data integration to predict severe acute oral mucositis of nasopharyngeal carcinoma patients following radiation therapy | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 7 | - |
dc.identifier.doi | 10.3390/cancers15072032 | - |
dcterms.abstract | (1) Background: Acute oral mucositis is the most common side effect for nasopharyngeal carcinoma patients receiving radiotherapy. Improper or delayed intervention to severe AOM could degrade the quality of life or survival for NPC patients. An effective prediction method for severe AOM is needed for the individualized management of NPC patients in the era of personalized medicine. | - |
dcterms.abstract | (2) Methods: A total of 242 biopsy-proven NPC patients were retrospectively recruited in this study. Radiomics features were extracted from contrast-enhanced CT (CECT), contrast-enhanced T1-weighted (cT1WI), and T2-weighted (T2WI) images in the primary tumor and tumor-related area. Dosiomics features were extracted from 2D or 3D dose-volume histograms (DVH). Multiple models were established with single and integrated data. The dataset was randomized into training and test sets at a ratio of 7:3 with 10-fold cross-validation. | - |
dcterms.abstract | (3) Results: The best-performing model using Gaussian Naive Bayes (GNB) (mean validation AUC = 0.81 ± 0.10) was established with integrated radiomics and dosiomics data. The GNB radiomics and dosiomics models yielded mean validation AUC of 0.6 ± 0.20 and 0.69 ± 0.14, respectively. | - |
dcterms.abstract | (4) Conclusions: Integrating radiomics and dosiomics data from the primary tumor area could generate the best-performing model for severe AOM prediction. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Cancers, Apr. 2023, v. 15, no. 7, 2032 | - |
dcterms.isPartOf | Cancers | - |
dcterms.issued | 2023-04 | - |
dc.identifier.scopus | 2-s2.0-85152939339 | - |
dc.identifier.eissn | 2072-6694 | - |
dc.identifier.artn | 2032 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Shenzhen Basic Research Program; Shenzhen-Hong Kong-Macau S&T Program (Category C); Mainland-Hong Kong Joint Funding Scheme; Project of Strategic Importance Fund; Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
cancers-15-02032.pdf | 4.3 MB | Adobe PDF | View/Open |
Page views
15
Citations as of Jul 7, 2024
Downloads
3
Citations as of Jul 7, 2024
SCOPUSTM
Citations
3
Citations as of Jul 4, 2024
WEB OF SCIENCETM
Citations
3
Citations as of Jul 4, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.