CT-based liver peritumoural radiomics features predict hepatic metastases sources as gastrointestinal or non-gastrointestinal.

IF 1.8 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Chengshi Hou, Fang Wang, Martin Prince, Xin Yang, Wenjian Wang, Jing Ye, Lei Chen, Xianfu Luo
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引用次数: 0

Abstract

Objectives: To investigate the feasibility of radiomics models for predicting the source of hepatic metastases from gastrointestinal (GI) vs non-gastrointestinal (non-GI) primary tumours on contrast-enhanced CT (CECT).

Methods: Three hundred and forty-seven patients with liver metastases (180 from GI and 167 from non-GI) and abdominal CECT including arterial, portal venous, and delayed phases were divided into training (221) and validation (96) sets at a ratio of 7:3 and an independent testing set (30). Radiomics features were extracted from volumes of interest (VOIs) including tumoural (Vtc) and peritumoural (Vpt) regions on CECT. Optimal radiomics features were used in logistic regression models using receiver operating curve (ROC) analysis to evaluate the diagnostic efficiency.

Results: The best single-phase model was a venous phase peritumoural VOI with 11 features. Area under the curve (AUC), sensitivity, and specificity were 0.817, 0.740, and 0.761, respectively in the validation set. While the best arterial phase tumoural VOI gave an AUC of 0.677 in the validation set. For the combined models, peritumoural VOI in arterial and venous phases (15 features) achieved the best prediction performance with an AUC of 0.926 in the validation set and 0.884 in the testing set.

Conclusion: Liver peritumoural radiomics features extracted from CECT were able to identify the source of hepatic metastases as GI vs non-GI.

Advances in knowledge: Peritumoural radiomics features showed a correlation with source of liver metastases. The radiomics features from liver peritumoural arterial and venous phases CT were promising in differentiating the source of hepatic metastases from GI vs non-GI primary tumours.

基于ct的肝肿瘤周围放射组学特征预测肝转移源是胃肠道还是非胃肠道。
目的:探讨放射组学模型在增强CT(CECT)上预测胃肠道(GI)与非胃肠道(non-GI)原发性肿瘤肝转移来源的可行性。方法:347例肝转移患者(180例来自胃肠道,167例来自非胃肠道)和腹部CECT,包括动脉期、门静脉期和延迟期,按7:3的比例分为训练组(221例)和验证组(96例),独立测试组(30例)。放射组学特征从感兴趣的体积(VOIs)中提取,包括CECT上的肿瘤(Vtc)和肿瘤周围(Vpt)区域。最优放射组学特征用于logistic回归模型,采用受试者工作曲线(ROC)分析来评估诊断效率。结果:最佳的单相模型为静脉期瘤周VOI,有11个特征。验证集的曲线下面积(AUC)、灵敏度和特异度分别为0.817、0.740和0.761。而在验证集中,最佳动脉期肿瘤VOI的AUC为0.677。对于联合模型,瘤周动脉期和静脉期(15个特征)的VOI预测效果最好,验证集的AUC为0.926,测试集的AUC为0.884。结论:从CECT中提取的肝肿瘤周围放射组学特征能够识别肝转移的来源是胃肠道还是非胃肠道。知识进展:肿瘤周围放射组学特征显示与肝转移来源相关。肝肿瘤周围动脉和静脉期CT放射组学特征在鉴别胃肠道与非胃肠道原发肿瘤的肝转移来源方面很有希望。
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来源期刊
British Journal of Radiology
British Journal of Radiology 医学-核医学
CiteScore
5.30
自引率
3.80%
发文量
330
审稿时长
2-4 weeks
期刊介绍: BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences. Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896. Quick Facts: - 2015 Impact Factor – 1.840 - Receipt to first decision – average of 6 weeks - Acceptance to online publication – average of 3 weeks - ISSN: 0007-1285 - eISSN: 1748-880X Open Access option
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