Radiomics signature based on CECT for non-invasive prediction of response to anti-PD-1 therapy in patients with hepatocellular carcinoma

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成果类型：

期刊论文

作者：

Cui, H.;Zeng, L.;Li, R.;Li, Q.;Hong, C.;...

通讯作者：

Liu, L.;Xiao, L;Zou, X.

作者机构：

[Liu, L.; Xiao, L.; Liu, L; Cui, H.] Southern Med Univ, Nanfang Hosp, Big Data Ctr, Guangzhou 510515, Peoples R China.

[Li, R.; Li, Q.; Hong, C.; Zou, X.; Liu, L.; Xiao, L.; Liu, L; Zeng, L.; Cui, H.] Southern Med Univ, Nanfang Hosp, Dept Infect Dis, Guangzhou 510515, Peoples R China.

[Zhu, H.] Univ South China, Affiliated Hosp 1, Hengyang Med Sch, Dept Med Oncol, Hengyang 421001, Peoples R China.

[Chen, L.] Southern Med Univ, Nanfang Hosp, Dept Med Qual Management, Guangzhou 510515, Peoples R China.

通讯机构：

[Zou, X; Xiao, L ; Liu, L] S

Southern Med Univ, Nanfang Hosp, Big Data Ctr, Guangzhou 510515, Peoples R China.

Southern Med Univ, Nanfang Hosp, Dept Infect Dis, Guangzhou 510515, Peoples R China.

语种：

英文

期刊：

Japanese Journal of Clinical Radiology

ISSN：

0009-9252

年：

2023

卷：

期：

页码：

e37-e44

DOI：

10.1016/j.crad.2022.09.113

基金类别：

This work was supported by the National Nature Science Foundation of China (grant nos. 81773008, 81972897, 82002549), the Clinical Research Startup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education (LC2019ZD003), China Postdoctoral Science Foundation (grant no. 2021M701629), Guangzhou Science and Technology Project (grant no. 202201011183), Guangdong Province College Students' Innovative Entrepreneurial Training Program (grant no. S202212121108) of China and Guangdong Natural Science Foundation (Grant No. 2019A1515110629).

机构署名：

本校为其他机构

院系归属：

医学院

摘要：

PURPOSE: This study aimed to develop a radiomics signature (RS) based on contrast -enhanced computed tomography (CECT) and evaluate its potential predictive value in hepa-tocellular carcinoma (HCC) patients receiving anti-PD-1 therapy. METHOD: CECT scans of 76 HCC patients who received anti-PD-1 therapy were obtained in this study (training group = 53 and validation group = 23). The least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics features of primary and metastatic lesions and establish a RS to predict lesion-level response. Then, a nomogram co...

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Radiomics signature based on CECT for non-invasive prediction of response to anti-PD-1 therapy in patients with hepatocellular carcinoma

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