Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/96471
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Health Technology and Informatics | - |
dc.creator | Chan, LWC | en_US |
dc.creator | Ding, T | en_US |
dc.creator | Shao, H | en_US |
dc.creator | Huang, M | en_US |
dc.creator | Hui, WFY | en_US |
dc.creator | Cho, WCS | en_US |
dc.creator | Wong, SCC | en_US |
dc.creator | Tong, KW | en_US |
dc.creator | Chiu, KWH | en_US |
dc.creator | Huang, L | en_US |
dc.creator | Zhou, H | en_US |
dc.date.accessioned | 2022-12-07T02:55:04Z | - |
dc.date.available | 2022-12-07T02:55:04Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/96471 | - |
dc.language.iso | en | en_US |
dc.publisher | Frontiers Research Foundation | en_US |
dc.rights | © 2022 Chan, Ding, Shao, Huang, Hui, Cho, Wong, Tong, Chiu, Huang and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | en_US |
dc.rights | The following publication Chan, L. W. C., Ding, T., Shao, H., Huang, M., Hui, W. F. Y., Cho, W. C. S., ... & Zhou, H. (2022). Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer. Frontiers in oncology, 12, 659096 is available at https://doi.org/10.3389/fonc.2022.659096. | en_US |
dc.subject | Adjuvant therapy (post-operative) | en_US |
dc.subject | Non-small cell lung cancer (NSCLC) | en_US |
dc.subject | Patient benefit | en_US |
dc.subject | Prediction model | en_US |
dc.subject | Radiomics | en_US |
dc.title | Augmented features synergize radiomics in post-operative survival prediction and adjuvant therapy recommendation for non-small cell lung cancer | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.doi | 10.3389/fonc.2022.659096 | en_US |
dcterms.abstract | Background: Owing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited. | - |
dcterms.abstract | Methods: Pre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People’s Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO). | - |
dcterms.abstract | Results: The Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar’s test p = 0.0003). | - |
dcterms.abstract | Conclusions: A Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Frontiers in oncology, Jan. 2022, v. 12, 659096 | en_US |
dcterms.isPartOf | Frontiers in oncology | en_US |
dcterms.issued | 2022-01 | - |
dc.identifier.scopus | 2-s2.0-85124581047 | - |
dc.identifier.eissn | 2234-943X | en_US |
dc.identifier.artn | 659096 | en_US |
dc.description.validate | 202212 bckw | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
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 | |
---|---|---|---|---|
fonc-12-659096.pdf | 2.45 MB | Adobe PDF | View/Open |
Page views
82
Last Week
2
2
Last month
Citations as of Oct 13, 2024
Downloads
40
Citations as of Oct 13, 2024
SCOPUSTM
Citations
1
Citations as of Oct 17, 2024
WEB OF SCIENCETM
Citations
1
Citations as of Oct 17, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.