Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Health and Social Sciences | en_US |
| dc.contributor.advisor | Cai, Jing (HTI) | en_US |
| dc.creator | Ho, Lok Man | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12252 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Treatment response in locally advanced hepatocellular carcinoma patients treated with sequential trans-arterial chemoembolization (TACE) and stereotactic body radiotherapy (SBRT) followed by immunotherapy | en_US |
| dcterms.abstract | Background and Objective: | en_US |
| dcterms.abstract | Liver cancer is one of the most common cancers globally, and hepatocellular carcinoma (HCC) is the most prevalent subtype. By combining Trans-Arterial Chemoembolization (TACE) and immunotherapy, stereotactic body radiation therapy (SBRT) may allow a better prognosis for advanced inoperable HCC patients. Radiomics is a new research area which extracts quantitative features information from radiological images and investigates their association with clinical outcomes, by using computational and mathematical strategies. The objective of this study was to investigate the correlation between multi-parametric features and the Modified Response Evaluation Criteria for Solid Tumours (mRECIST)-defined response rate of unresectable HCC patients treated by sequential TACE and SBRT followed by immunotherapy. | en_US |
| dcterms.abstract | Methods: | en_US |
| dcterms.abstract | In total, the data of 26 patients with locally advanced HCC were retrospectively analyzed. Radiomics features were extracted from every lesion for all patients, expanding the dataset to 42 lesions. A total of 105 radiomics features, including shape and size (n = 14), first-order (n = 18), and second-order (n = 73), were extracted from gross tumor volume (GTV) initially using baseline arterial phase (AP) and portal venous phase (PVP) T1W MR images. Delta phase (DeltaP) radiomics features were calculated by dividing features extracted from AP images by features extracted from PVP images. Dosimetric and clinical features were also extracted. A total of 348 features were extracted for each lesion. Univariate statistical tests and multivariate logistic regression analyses were performed for the classification of mRECIST-defined response rates at 3 and 6 months. Single and multiple feature-typed logistic regression were performed. The performance of each classification model was evaluated by the area under the receiver operator characteristic (ROC) curve (AUC). | en_US |
| dcterms.abstract | Results: | en_US |
| dcterms.abstract | Respondents were identified in 18 lesions (42.9%) and 28 lesions (66.7%) for 3- and 6- month assessment, respectively. For 3-month timepoint, clinical features alone achieved the best predictive performance (AUC = 0.830). Only slight increase in AUC when radiomics features were included (AUC = 0.833), and no statistically significant improvement was found. For 6-month timepoint, AP-based features showed significant association with the response rate and the AP-derived radiomics showed a satisfactory classification performance (AUC = 0.901). The AUCs increased slightly when additional types of parameters were added. However, no statistically significant difference was found between AP model and combined models. | en_US |
| dcterms.abstract | Conclusion: | en_US |
| dcterms.abstract | The pilot study successfully demonstrated the feasibility of multiple features in associating with mRECIST-defined response rate in unresectable HCC following the proposed treatment regimen. Radiomic features were found frequently to demonstrate significant association with response rate in different timepoints. However, a larger study cohort is needed the future to validate. | en_US |
| dcterms.extent | viii, 116 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2022 | en_US |
| dcterms.educationalLevel | DHSc | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Liver -- Cancer -- Radiotherapy | en_US |
| dcterms.LCSH | Liver -- Tumors -- Treatment | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6684.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 4.09 MB | Adobe PDF | View/Open |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/12252