Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Abstract Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-n...

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Autores principales:	Yingjie Xv, Fajin Lv, Haoming Guo, Xiang Zhou, Hao Tan, Mingzhao Xiao, Yineng Zheng
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Machine learning Tomography (X-ray computed) Clear cell renal cell carcinoma WHO/ISUP grading Prediction model Medical physics. Medical radiology. Nuclear medicine R895-920
Acceso en línea:	https://doaj.org/article/f4995fb21ba840a19dd8aef54220e3fe
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spelling	oai:doaj.org-article:f4995fb21ba840a19dd8aef54220e3fe2021-11-21T12:07:05ZMachine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study10.1186/s13244-021-01107-11869-4101https://doaj.org/article/f4995fb21ba840a19dd8aef54220e3fe2021-11-01T00:00:00Zhttps://doi.org/10.1186/s13244-021-01107-1https://doaj.org/toc/1869-4101Abstract Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.Yingjie XvFajin LvHaoming GuoXiang ZhouHao TanMingzhao XiaoYineng ZhengSpringerOpenarticleMachine learningTomography (X-ray computed)Clear cell renal cell carcinomaWHO/ISUP gradingPrediction modelMedical physics. Medical radiology. Nuclear medicineR895-920ENInsights into Imaging, Vol 12, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Tomography (X-ray computed) Clear cell renal cell carcinoma WHO/ISUP grading Prediction model Medical physics. Medical radiology. Nuclear medicine R895-920
spellingShingle	Machine learning Tomography (X-ray computed) Clear cell renal cell carcinoma WHO/ISUP grading Prediction model Medical physics. Medical radiology. Nuclear medicine R895-920 Yingjie Xv Fajin Lv Haoming Guo Xiang Zhou Hao Tan Mingzhao Xiao Yineng Zheng Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study
description	Abstract Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.
format	article
author	Yingjie Xv Fajin Lv Haoming Guo Xiang Zhou Hao Tan Mingzhao Xiao Yineng Zheng
author_facet	Yingjie Xv Fajin Lv Haoming Guo Xiang Zhou Hao Tan Mingzhao Xiao Yineng Zheng
author_sort	Yingjie Xv
title	Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study
title_short	Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study
title_full	Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study
title_fullStr	Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study
title_full_unstemmed	Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study
title_sort	machine learning-based ct radiomics approach for predicting who/isup nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/f4995fb21ba840a19dd8aef54220e3fe
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Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

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