Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection

Abstract To investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological ch...

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Autores principales:	Yong Zhu, Yingfan Mao, Jun Chen, Yudong Qiu, Yue Guan, Zhongqiu Wang, Jian He
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/10da6ecf2bd54cca96eabf2fe6957277
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spelling	oai:doaj.org-article:10da6ecf2bd54cca96eabf2fe69572772021-12-02T18:33:55ZRadiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection10.1038/s41598-021-97796-12045-2322https://doaj.org/article/10da6ecf2bd54cca96eabf2fe69572772021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97796-1https://doaj.org/toc/2045-2322Abstract To investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840–0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637–0.828) and 0.844 (95% CI 0.751–0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.Yong ZhuYingfan MaoJun ChenYudong QiuYue GuanZhongqiu WangJian HeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yong Zhu Yingfan Mao Jun Chen Yudong Qiu Yue Guan Zhongqiu Wang Jian He Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection
description	Abstract To investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840–0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637–0.828) and 0.844 (95% CI 0.751–0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.
format	article
author	Yong Zhu Yingfan Mao Jun Chen Yudong Qiu Yue Guan Zhongqiu Wang Jian He
author_facet	Yong Zhu Yingfan Mao Jun Chen Yudong Qiu Yue Guan Zhongqiu Wang Jian He
author_sort	Yong Zhu
title	Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection
title_short	Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection
title_full	Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection
title_fullStr	Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection
title_full_unstemmed	Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection
title_sort	radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/10da6ecf2bd54cca96eabf2fe6957277
work_keys_str_mv	AT yongzhu radiomicsbasedmodelforpredictingearlyrecurrenceofintrahepaticmassformingcholangiocarcinomaaftercurativetumorresection AT yingfanmao radiomicsbasedmodelforpredictingearlyrecurrenceofintrahepaticmassformingcholangiocarcinomaaftercurativetumorresection AT junchen radiomicsbasedmodelforpredictingearlyrecurrenceofintrahepaticmassformingcholangiocarcinomaaftercurativetumorresection AT yudongqiu radiomicsbasedmodelforpredictingearlyrecurrenceofintrahepaticmassformingcholangiocarcinomaaftercurativetumorresection AT yueguan radiomicsbasedmodelforpredictingearlyrecurrenceofintrahepaticmassformingcholangiocarcinomaaftercurativetumorresection AT zhongqiuwang radiomicsbasedmodelforpredictingearlyrecurrenceofintrahepaticmassformingcholangiocarcinomaaftercurativetumorresection AT jianhe radiomicsbasedmodelforpredictingearlyrecurrenceofintrahepaticmassformingcholangiocarcinomaaftercurativetumorresection
_version_	1718377903355330560

Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection

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