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Article: MRI texture features differentiate clinicopathological characteristics of cervical carcinoma

TitleMRI texture features differentiate clinicopathological characteristics of cervical carcinoma
Authors
KeywordsMagnetic resonance imaging
Squamous cell carcinoma
Adenocarcinoma
Area under the curve
Entropy
Issue Date2020
PublisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00330/index.htm
Citation
European Radiology, 2020, v. 30 n. 10, p. 5384-5391 How to Cite?
AbstractObjectives: To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). Methods: Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. Results: Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. Conclusions: Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. Key Points: • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.
Persistent Identifierhttp://hdl.handle.net/10722/285277
ISSN
2019 Impact Factor: 4.101
2015 SCImago Journal Rankings: 2.064
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, M-
dc.contributor.authorPerucho, JAU-
dc.contributor.authorTse, KY-
dc.contributor.authorChu, MMY-
dc.contributor.authorIp, P-
dc.contributor.authorLee, EYP-
dc.date.accessioned2020-08-18T03:51:57Z-
dc.date.available2020-08-18T03:51:57Z-
dc.date.issued2020-
dc.identifier.citationEuropean Radiology, 2020, v. 30 n. 10, p. 5384-5391-
dc.identifier.issn0938-7994-
dc.identifier.urihttp://hdl.handle.net/10722/285277-
dc.description.abstractObjectives: To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). Methods: Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. Results: Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. Conclusions: Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. Key Points: • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00330/index.htm-
dc.relation.ispartofEuropean Radiology-
dc.subjectMagnetic resonance imaging-
dc.subjectSquamous cell carcinoma-
dc.subjectAdenocarcinoma-
dc.subjectArea under the curve-
dc.subjectEntropy-
dc.titleMRI texture features differentiate clinicopathological characteristics of cervical carcinoma-
dc.typeArticle-
dc.identifier.emailPerucho, JAU: peruchoj@hku.hk-
dc.identifier.emailTse, KY: tseky@hku.hk-
dc.identifier.emailChu, MMY: chumy@hku.hk-
dc.identifier.emailIp, P: philipip@hku.hk-
dc.identifier.emailLee, EYP: eyplee77@hku.hk-
dc.identifier.authorityTse, KY=rp02391-
dc.identifier.authorityIp, P=rp01890-
dc.identifier.authorityLee, EYP=rp01456-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s00330-020-06913-7-
dc.identifier.pmid32382845-
dc.identifier.scopuseid_2-s2.0-85084316109-
dc.identifier.hkuros312774-
dc.identifier.volume30-
dc.identifier.issue10-
dc.identifier.spage5384-
dc.identifier.epage5391-
dc.identifier.isiWOS:000531511700003-
dc.publisher.placeGermany-

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