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Conference Paper: The role of pre-treatment 18F-FDG PET texture features in the prediction of nodal involvement and tumor characterization in cervical cancer
Title | The role of pre-treatment 18F-FDG PET texture features in the prediction of nodal involvement and tumor characterization in cervical cancer |
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Authors | |
Issue Date | 2019 |
Publisher | Elsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ijrobp |
Citation | Proceedings of the American Society for Radiation Oncology (ASTRO) 61st Annual Meeting, Chicago, IL, USA, 15-18 September 2019. In International Journal of Radiation Oncology, 2019, v. 105 n. 1, Suppl., p. E320, abstract no. 2731 How to Cite? |
Abstract | Purpose/Objective(s): Radiomics is gaining attention in oncology given the increased availability of information from radiological images. Quantitative analysis of texture features has been evolving. However, in order to determine the added value of the texture features, their performance should be compared with conventional indices, such as Standardized Uptake Value (SUV), Metabolic Tumor Volume (MTV), and Total Lesion Glycolysis (TLG). This study aimed to analyze the association
between the texture features and nodal involvement, and the correlations between the selected texture features and conventional metabolic indices.
Materials/Methods: 18F-FDG PET scans were analyzed retrospectively, 31 texture features and 5 histogram indices were extracted from the baseline images of 85 cervical cancer patients. Volumes of interest were first manually contoured, then segmented by fixed threshold set to 40% of the maximum SUV in the lesion. Texture features were computed with absolute resampling method. The patients were dichotomized by nodal involvement and further subdivided into pelvic and para-aortic involvement. The area under the curve (AUC) of receiver operating characteristics was used to evaluate the discrimination performance of the features and AUC>0.7 was considered moderate-good. Correlation between texture
features was assessed by Spearman’s Rank Correlation.
Results: GLRLM_RLNU, GLZLM_GLNU, GLZLM_ZLNU, and NGLDM_Coarseness were identified to have AUC > 0.7 (p<0.01) in the node positive group and in subgroup with pelvic node positive. Among
which, GLRLM_RLNU and NGLDM_Coarseness were highly correlated with MTV and TLG (mean absolute correlation rs Z 0.92, p<0.01) and resulted in similar prediction performances. GLZLM_ZLNU was highly correlated with SUVmax (rs Z 0.907, p<0.01). GLZLM_GLNU was highly correlated with TLG (rs Z 0.913, p<0.01).
Conclusion: The texture features that had the potential to predict nodal involvement were highly correlated with MTV, TLG, and SUVmax. The correlation should be accounted for when evaluating the results from texture analysis. |
Description | Poster Viewing Q&A Session |
Persistent Identifier | http://hdl.handle.net/10722/285345 |
ISSN | 2023 Impact Factor: 6.4 2023 SCImago Journal Rankings: 1.992 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chan, KC | - |
dc.contributor.author | Lee, EYP | - |
dc.date.accessioned | 2020-08-18T03:52:36Z | - |
dc.date.available | 2020-08-18T03:52:36Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of the American Society for Radiation Oncology (ASTRO) 61st Annual Meeting, Chicago, IL, USA, 15-18 September 2019. In International Journal of Radiation Oncology, 2019, v. 105 n. 1, Suppl., p. E320, abstract no. 2731 | - |
dc.identifier.issn | 0360-3016 | - |
dc.identifier.uri | http://hdl.handle.net/10722/285345 | - |
dc.description | Poster Viewing Q&A Session | - |
dc.description.abstract | Purpose/Objective(s): Radiomics is gaining attention in oncology given the increased availability of information from radiological images. Quantitative analysis of texture features has been evolving. However, in order to determine the added value of the texture features, their performance should be compared with conventional indices, such as Standardized Uptake Value (SUV), Metabolic Tumor Volume (MTV), and Total Lesion Glycolysis (TLG). This study aimed to analyze the association between the texture features and nodal involvement, and the correlations between the selected texture features and conventional metabolic indices. Materials/Methods: 18F-FDG PET scans were analyzed retrospectively, 31 texture features and 5 histogram indices were extracted from the baseline images of 85 cervical cancer patients. Volumes of interest were first manually contoured, then segmented by fixed threshold set to 40% of the maximum SUV in the lesion. Texture features were computed with absolute resampling method. The patients were dichotomized by nodal involvement and further subdivided into pelvic and para-aortic involvement. The area under the curve (AUC) of receiver operating characteristics was used to evaluate the discrimination performance of the features and AUC>0.7 was considered moderate-good. Correlation between texture features was assessed by Spearman’s Rank Correlation. Results: GLRLM_RLNU, GLZLM_GLNU, GLZLM_ZLNU, and NGLDM_Coarseness were identified to have AUC > 0.7 (p<0.01) in the node positive group and in subgroup with pelvic node positive. Among which, GLRLM_RLNU and NGLDM_Coarseness were highly correlated with MTV and TLG (mean absolute correlation rs Z 0.92, p<0.01) and resulted in similar prediction performances. GLZLM_ZLNU was highly correlated with SUVmax (rs Z 0.907, p<0.01). GLZLM_GLNU was highly correlated with TLG (rs Z 0.913, p<0.01). Conclusion: The texture features that had the potential to predict nodal involvement were highly correlated with MTV, TLG, and SUVmax. The correlation should be accounted for when evaluating the results from texture analysis. | - |
dc.language | eng | - |
dc.publisher | Elsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/ijrobp | - |
dc.relation.ispartof | International Journal of Radiation Oncology - Biology - Physics | - |
dc.relation.ispartof | Proceedings of the American Society for Radiation Oncology (ASTRO) Annual Meeting 2019 | - |
dc.title | The role of pre-treatment 18F-FDG PET texture features in the prediction of nodal involvement and tumor characterization in cervical cancer | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lee, EYP: eyplee77@hku.hk | - |
dc.identifier.authority | Lee, EYP=rp01456 | - |
dc.identifier.doi | 10.1016/j.ijrobp.2019.06.1797 | - |
dc.identifier.hkuros | 312776 | - |
dc.identifier.volume | 105 | - |
dc.identifier.issue | 1, Suppl. | - |
dc.identifier.spage | E320, abstract no. 2731 | - |
dc.identifier.epage | E320, abstract no. 2731 | - |
dc.identifier.isi | WOS:000485671501018 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0360-3016 | - |