Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.

Zhou, H; Mao, H; Dong, D; Fang, M; Gu, D; Liu, X; Xu, M; Yang, S; Zou, J; Yin, R; Zheng, H; Tian, J; Pan, C; Fang, X

Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.

Zhou, H Mao, H Dong, D Fang, M Gu, D Liu, X Xu, M

Yang, S Zou, J Yin, R Zheng, H Tian, J Pan, C Fang, X

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Annals of surgical oncology, 2020, 27, (10), pp. 4057-4065
Issue Date:: 2020-10

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Field	Value	Language
dc.contributor.author	Zhou, H
dc.contributor.author	Mao, H
dc.contributor.author	Dong, D
dc.contributor.author	Fang, M
dc.contributor.author	Gu, D
dc.contributor.author	Liu, X
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.contributor.author	Yang, S
dc.contributor.author	Zou, J
dc.contributor.author	Yin, R
dc.contributor.author	Zheng, H
dc.contributor.author	Tian, J
dc.contributor.author	Pan, C
dc.contributor.author	Fang, X
dc.date.accessioned	2021-05-26T01:40:33Z
dc.date.available	2021-05-26T01:40:33Z
dc.date.issued	2020-10
dc.identifier.citation	Annals of surgical oncology, 2020, 27, (10), pp. 4057-4065
dc.identifier.issn	1068-9265
dc.identifier.issn	1534-4681
dc.identifier.uri	http://hdl.handle.net/10453/149227
dc.description.abstract	<h4>Background and purpose</h4>Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC.<h4>Methods</h4>Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models.<h4>Results</h4>The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data).<h4>Conclusion</h4>The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Annals of surgical oncology
dc.relation.isbasedon	10.1245/s10434-020-08255-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1112 Oncology and Carcinogenesis
dc.subject.classification	Oncology & Carcinogenesis
dc.subject.mesh	Carcinoma, Renal Cell
dc.subject.mesh	Diagnosis, Differential
dc.subject.mesh	Humans
dc.subject.mesh	Kidney Neoplasms
dc.subject.mesh	ROC Curve
dc.subject.mesh	Tomography, X-Ray Computed
dc.subject.mesh	Humans
dc.subject.mesh	Carcinoma, Renal Cell
dc.subject.mesh	Kidney Neoplasms
dc.subject.mesh	Diagnosis, Differential
dc.subject.mesh	Tomography, X-Ray Computed
dc.subject.mesh	ROC Curve
dc.subject.mesh	Carcinoma, Renal Cell
dc.subject.mesh	Diagnosis, Differential
dc.subject.mesh	Humans
dc.subject.mesh	Kidney Neoplasms
dc.subject.mesh	ROC Curve
dc.subject.mesh	Tomography, X-Ray Computed
dc.title	Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.
dc.type	Journal Article
utslib.citation.volume	27
utslib.location.activity	United States
utslib.for	1112 Oncology and Carcinogenesis
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-26T01:40:31Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	27
utslib.citation.issue	10

Abstract:

Background and purpose

Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC.

Methods

Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models.

Results

The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data).

Conclusion

The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/149227