Department of Radiology, Cangzhou Central Hospital, No. 16 Xinhua West Road, Cangzhou, 061000, China.
Department of Pathology, Cangzhou Central Hospital, Cangzhou, 061000, China.
Radiol Med. 2020 May;125(5):465-473. doi: 10.1007/s11547-020-01138-6. Epub 2020 Feb 11.
The pathological risk degree of gastrointestinal stromal tumors (GISTs) has become an issue of great concern. Computed tomography (CT) is beneficial for showing adjacent tissues in detail and determining metastasis or recurrence of GISTs, but its function is still limited. Radiomics has recently shown a great potential in aiding clinical decision-making. The purpose of our study is to develop and validate CT-based radiomics models for GIST risk stratification.
Three hundred and sixty-six patients clinically suspected of primary GISTs from January 2013 to February 2018 were retrospectively enrolled, among which data from 140 patients were eventually analyzed after exclusion. Data from patient CT images were partitioned based on the National Institutes of Health Consensus Classification, including tumor segmentation, radiomics feature extraction and selection. A radiomics model was then proposed and validated.
The radiomics signature demonstrated discriminative performance for advanced and nonadvanced GISTs with an area under the curve (AUC) of 0.935 [95% confidence interval (CI) 0.870-1.000] and an accuracy of 90.2% for validation cohort. The radiomics signature demonstrated favorable performance for the risk stratification of GISTs with an AUC of 0.809 (95% CI 0.777-0.841) and an accuracy of 67.5% for the validation cohort. Radiomics analysis could capture features of the four risk categories of GISTs. Meanwhile, this CT-based radiomics signature showed good diagnostic accuracy to distinguish between nonadvanced and advanced GISTs, as well as the four risk stratifications of GISTs.
Our findings highlight the potential of a quantitative radiomics analysis as a complementary tool to achieve an accurate diagnosis for GISTs.
胃肠道间质瘤（GIST）的病理危险程度已成为人们关注的焦点。计算机断层扫描（CT）有利于详细显示邻近组织，并确定 GIST 的转移或复发，但功能仍有限。放射组学最近在辅助临床决策方面显示出巨大的潜力。本研究旨在开发和验证基于 CT 的 GIST 风险分层放射组学模型。
回顾性纳入 2013 年 1 月至 2018 年 2 月临床疑似原发性 GIST 的 366 例患者，排除后最终分析了 140 例患者的数据。根据美国国立卫生研究院共识分类，对患者 CT 图像数据进行分区，包括肿瘤分割、放射组学特征提取和选择。然后提出并验证了一个放射组学模型。
放射组学特征对高级和非高级 GIST 具有区分性能，验证队列的曲线下面积（AUC）为 0.935[95%置信区间（CI）0.870-1.000]，准确率为 90.2%。放射组学特征对 GIST 的风险分层具有良好的性能，验证队列的 AUC 为 0.809（95%CI 0.777-0.841），准确率为 67.5%。放射组学分析可以捕捉 GIST 四个危险级别的特征。同时，这种基于 CT 的放射组学特征显示出良好的诊断准确性，可以区分非高级和高级 GIST，以及 GIST 的四个风险分层。
我们的研究结果强调了定量放射组学分析作为一种辅助工具，对 GIST 进行准确诊断的潜力。
