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基于 CT 的影像组学评分鉴别 1 级和 2 级无功能胰腺神经内分泌肿瘤。

CT-Based Radiomics Score for Distinguishing Between Grade 1 and Grade 2 Nonfunctioning Pancreatic Neuroendocrine Tumors.

机构信息

Department of Radiology, Changhai Hospital, The Navy Military Medical University, 168 Changhai Rd, Shanghai 200433, China.

Department of Pathology, Changhai Hospital, The Navy Military Medical University, Shanghai, China.

出版信息

AJR Am J Roentgenol. 2020 Oct;215(4):852-863. doi: 10.2214/AJR.19.22123. Epub 2020 Jul 22.

Abstract

The objective of our study was to explore the relationship between a CT-based radiomics score and grade of nonfunctioning pancreatic neuroendocrine tumors (PNETs) and to evaluate the ability of a calculated CT radiomics score to distinguish between grade 1 and grade 2 nonfunctioning PNETs. This retrospective study assessed 102 patients with surgically resected, pathologically confirmed nonfunctioning PNETs who underwent MDCT from January 2014 to December 2017. Radiomic methods were used to extract features from portal venous phase CT scans, and the least absolute shrinkage and selection operator (LASSO) method was used to select the features. Multivariate logistic regression models were used to analyze the association between the CT radiomics score and nonfunctioning PNET grades. The performance of the CT radiomics score was assessed on the basis of its discriminative ability and clinical usefulness. The CT radiomics score, which consisted of four selected features, was significantly associated with nonfunctioning PNET grades. Every 1-point increase in radiomics score was associated with a 57% increased risk of grade 2 disease. The score also showed high accuracy (AUC = 0.86 for all PNETs; AUC = 0.81 for PNETs ≤ 2 cm). The best cutoff point for maximal sensitivity and specificity was a CT radiomics score of -0.134. Decision curve analysis showed that the CT radiomics score is clinically useful. The CT radiomics score shows a significant association with the grade of nonfunctioning PNETs and provides a potentially valuable noninvasive tool for distinguishing between different grades of nonfunctioning PNET, especially among patients with tumors 2 cm or smaller.

摘要

我们的研究目的是探讨基于 CT 的放射组学评分与无功能性胰腺神经内分泌肿瘤(PNET)分级之间的关系,并评估计算出的 CT 放射组学评分区分 1 级和 2 级无功能性 PNET 的能力。本回顾性研究纳入了 2014 年 1 月至 2017 年 12 月期间经 MDCT 检查且经手术切除、病理证实为无功能性 PNET 的 102 例患者。使用放射组学方法从门静脉期 CT 扫描中提取特征,使用最小绝对收缩和选择算子(LASSO)方法选择特征。使用多变量逻辑回归模型分析 CT 放射组学评分与无功能性 PNET 分级之间的关联。基于其区分能力和临床实用性评估 CT 放射组学评分的性能。由四个选定特征组成的 CT 放射组学评分与无功能性 PNET 分级显著相关。放射组学评分每增加 1 分,2 级疾病的风险增加 57%。该评分还具有较高的准确性(所有 PNET 的 AUC = 0.86;直径≤2cm 的 PNET 的 AUC = 0.81)。最大敏感性和特异性的最佳截断点为 CT 放射组学评分-0.134。决策曲线分析表明 CT 放射组学评分具有临床实用性。CT 放射组学评分与无功能性 PNET 的分级显著相关,为区分不同分级的无功能性 PNET 提供了一种有价值的非侵入性工具,尤其是在直径 2cm 或更小的肿瘤患者中。

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