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Conference Paper: Texture analysis of multiparametric MRI and association with tumour grading in cervical cancer

TitleTexture analysis of multiparametric MRI and association with tumour grading in cervical cancer
Authors
Issue Date2019
PublisherSpringerOpen. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/13244
Citation
European Congress of Radiology 25th Annual Meeting, Vienna, Austria, 27 February - 3 March 2019. In Insights into Imaging , 2019, v. 10 n. Suppl. 1, p. S223, article no. B-0178 How to Cite?
AbstractPurpose: To explore efficacy and generalizability of texture analysis of multiparametric magnetic resonance imaging (mpMRI) in discriminating histopathological characteristics of cervical cancers in a two-centre setting. Methods and Materials: 130 cervical cancer patients were retrospectively reviewed for pre-treatment diffusion-weighted (DWI) and standard T2-weighted (T2W) abdominopelvic MRI in two centres (centre 1 n=100; centre 2 n=30). Biopsies of each patient were acquired to determine histological subtype and tumour grading. Volumetric regions of interest (VOI) were placed to encompass the entirety of primary tumours on T2W and DWI parametric maps. 144 texture features were calculated using pyradiomics. Redundancy analysis was used for feature reduction while goodness-of-fit measures were used for feature selection. Logistic regression was used to build predictive models with centre 1 serving as the training set and centre 2 serving as an external testing set. Results: In the first centre 80 patients had squamous cell carcinoma (SCC) while 20 had adenocarcinoma (ACA), and 42 patients had well or moderately differentiated tumours (G1/G2) while 58 had poorly differentiated tumours (G3). In the second centre, 25 patients had SCC while 5 had ACA, and 14 had G1/G2 tumours while 16 had G3 tumours. Redundancy analysis demonstrated that only 15% of features were independent predictors. In discriminating histological grading, only one texture, T2Wkurtosis, achieved reasonable goodness-of-fit and moderate discriminative performance (internal accuracy: 0.64, external accuracy: 0.71). In discriminating histological subtype. Texture features had poor discriminative performance for tumour grading. Conclusion: T2Wkurtosis was moderately discriminative in differentiating tumour grading in cervical cancer.
DescriptionSS 216 - Multiparametric imaging for pelvic cancers - no. B-0178
v. 10 suppl. 2 has special title: ECR 2019 Book of Abstracts
Persistent Identifierhttp://hdl.handle.net/10722/275244
ISSN
2019 Impact Factor: 3.579
2015 SCImago Journal Rankings: 1.004

 

DC FieldValueLanguage
dc.contributor.authorPerucho, JAU-
dc.contributor.authorLee, EYP-
dc.contributor.authorDu, R-
dc.contributor.authorVardhanabhuti, V-
dc.contributor.authorChiu, WHK-
dc.contributor.authorWong, EMF-
dc.date.accessioned2019-09-10T02:38:36Z-
dc.date.available2019-09-10T02:38:36Z-
dc.date.issued2019-
dc.identifier.citationEuropean Congress of Radiology 25th Annual Meeting, Vienna, Austria, 27 February - 3 March 2019. In Insights into Imaging , 2019, v. 10 n. Suppl. 1, p. S223, article no. B-0178-
dc.identifier.issn1869-4101-
dc.identifier.urihttp://hdl.handle.net/10722/275244-
dc.descriptionSS 216 - Multiparametric imaging for pelvic cancers - no. B-0178-
dc.descriptionv. 10 suppl. 2 has special title: ECR 2019 Book of Abstracts-
dc.description.abstractPurpose: To explore efficacy and generalizability of texture analysis of multiparametric magnetic resonance imaging (mpMRI) in discriminating histopathological characteristics of cervical cancers in a two-centre setting. Methods and Materials: 130 cervical cancer patients were retrospectively reviewed for pre-treatment diffusion-weighted (DWI) and standard T2-weighted (T2W) abdominopelvic MRI in two centres (centre 1 n=100; centre 2 n=30). Biopsies of each patient were acquired to determine histological subtype and tumour grading. Volumetric regions of interest (VOI) were placed to encompass the entirety of primary tumours on T2W and DWI parametric maps. 144 texture features were calculated using pyradiomics. Redundancy analysis was used for feature reduction while goodness-of-fit measures were used for feature selection. Logistic regression was used to build predictive models with centre 1 serving as the training set and centre 2 serving as an external testing set. Results: In the first centre 80 patients had squamous cell carcinoma (SCC) while 20 had adenocarcinoma (ACA), and 42 patients had well or moderately differentiated tumours (G1/G2) while 58 had poorly differentiated tumours (G3). In the second centre, 25 patients had SCC while 5 had ACA, and 14 had G1/G2 tumours while 16 had G3 tumours. Redundancy analysis demonstrated that only 15% of features were independent predictors. In discriminating histological grading, only one texture, T2Wkurtosis, achieved reasonable goodness-of-fit and moderate discriminative performance (internal accuracy: 0.64, external accuracy: 0.71). In discriminating histological subtype. Texture features had poor discriminative performance for tumour grading. Conclusion: T2Wkurtosis was moderately discriminative in differentiating tumour grading in cervical cancer.-
dc.languageeng-
dc.publisherSpringerOpen. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/13244-
dc.relation.ispartofInsights into Imaging-
dc.relation.ispartofEuropean Congress of Radiology 25th Annual Meeting, 2019-
dc.titleTexture analysis of multiparametric MRI and association with tumour grading in cervical cancer-
dc.typeConference_Paper-
dc.identifier.emailLee, EYP: eyplee77@hku.hk-
dc.identifier.emailVardhanabhuti, V: varv@hku.hk-
dc.identifier.emailChiu, WHK: kwhchiu@hku.hk-
dc.identifier.authorityLee, EYP=rp01456-
dc.identifier.authorityVardhanabhuti, V=rp01900-
dc.identifier.authorityChiu, WHK=rp02074-
dc.identifier.hkuros303950-
dc.identifier.volume10-
dc.identifier.issueSuppl. 1-
dc.identifier.spageS223-
dc.identifier.epageS223-
dc.publisher.placeGermany-

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