Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study

ObjectiveThis study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).MethodsThe clinical data and ultrasonic images of 226 patients from three hosp...

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Autores principales:	Shanshan Ren, Qian Li, Shunhua Liu, Qinghua Qi, Shaobo Duan, Bing Mao, Xin Li, Yuejin Wu, Lianzhong Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	hepatocellular carcinoma intrahepatic cholangiocarcinoma machine learning radiomics ultrasonography Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/37bb448346bb48ef81d482008a0a6929
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spelling	oai:doaj.org-article:37bb448346bb48ef81d482008a0a69292021-11-05T13:37:39ZClinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study2234-943X10.3389/fonc.2021.749137https://doaj.org/article/37bb448346bb48ef81d482008a0a69292021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.749137/fullhttps://doaj.org/toc/2234-943XObjectiveThis study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).MethodsThe clinical data and ultrasonic images of 226 patients from three hospitals were retrospectively collected and divided into training set (n = 149), test set (n = 38), and independent validation set (n = 39). Manual segmentation of tumor lesion was performed with ITK-SNAP, the ultrasomics features were extracted by the pyradiomics, and ultrasomics signatures were generated using variance filtering and lasso regression. The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine (SVM). The performance of the three models was evaluated by the area under curve (AUC), sensitivity, specificity, and accuracy.ResultsThe ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC (p < 0.05). The combined model had a better performance than either the clinical model or the ultrasomics model. In addition to stability, the combined model also had a stronger generalization ability (p < 0.05). The AUC (along with 95% CI), sensitivity, specificity, and accuracy of the combined model on the test set and the independent validation set were 0.936 (0.806–0.989), 0.900, 0.857, 0.868, and 0.874 (0.733–0.961), 0.889, 0.867, and 0.872, respectively.ConclusionThe ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC. The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.Shanshan RenShanshan RenQian LiShunhua LiuQinghua QiShaobo DuanBing MaoXin LiYuejin WuLianzhong ZhangLianzhong ZhangFrontiers Media S.A.articlehepatocellular carcinomaintrahepatic cholangiocarcinomamachine learningradiomicsultrasonographyNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hepatocellular carcinoma intrahepatic cholangiocarcinoma machine learning radiomics ultrasonography Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	hepatocellular carcinoma intrahepatic cholangiocarcinoma machine learning radiomics ultrasonography Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Shanshan Ren Shanshan Ren Qian Li Shunhua Liu Qinghua Qi Shaobo Duan Bing Mao Xin Li Yuejin Wu Lianzhong Zhang Lianzhong Zhang Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study
description	ObjectiveThis study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).MethodsThe clinical data and ultrasonic images of 226 patients from three hospitals were retrospectively collected and divided into training set (n = 149), test set (n = 38), and independent validation set (n = 39). Manual segmentation of tumor lesion was performed with ITK-SNAP, the ultrasomics features were extracted by the pyradiomics, and ultrasomics signatures were generated using variance filtering and lasso regression. The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine (SVM). The performance of the three models was evaluated by the area under curve (AUC), sensitivity, specificity, and accuracy.ResultsThe ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC (p < 0.05). The combined model had a better performance than either the clinical model or the ultrasomics model. In addition to stability, the combined model also had a stronger generalization ability (p < 0.05). The AUC (along with 95% CI), sensitivity, specificity, and accuracy of the combined model on the test set and the independent validation set were 0.936 (0.806–0.989), 0.900, 0.857, 0.868, and 0.874 (0.733–0.961), 0.889, 0.867, and 0.872, respectively.ConclusionThe ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC. The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.
format	article
author	Shanshan Ren Shanshan Ren Qian Li Shunhua Liu Qinghua Qi Shaobo Duan Bing Mao Xin Li Yuejin Wu Lianzhong Zhang Lianzhong Zhang
author_facet	Shanshan Ren Shanshan Ren Qian Li Shunhua Liu Qinghua Qi Shaobo Duan Bing Mao Xin Li Yuejin Wu Lianzhong Zhang Lianzhong Zhang
author_sort	Shanshan Ren
title	Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study
title_short	Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study
title_full	Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study
title_fullStr	Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study
title_full_unstemmed	Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study
title_sort	clinical value of machine learning-based ultrasomics in preoperative differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma: a multicenter study
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/37bb448346bb48ef81d482008a0a6929
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Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study

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