基于CT放射组学的胃肠道间质瘤D842V突变鉴定:一项多中心研究

IF 3.5 2区 医学 Q2 ONCOLOGY
Zhenhui Xie, Qingwei Zhang, Ranying Zhang, Yuxuan Zhao, Wang Zhang, Yang Song, Dexin Yu, Jiang Lin, Xiaobo Li, Shiteng Suo, Yan Zhou
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引用次数: 0

摘要

背景:胃肠道间质瘤(gist)是最常见的胃肠道间质肿瘤。最近出现的酪氨酸激酶抑制剂(TKIs)显著改善了GIST患者的预后。然而,对TKI治疗的反应可能因特定基因突变而异。D842V是血小板源性生长因子受体α外显子18中最常见的突变,对伊马替尼和舒尼替尼没有反应。基于静脉期对比增强计算机断层扫描(CECT)的放射组学特征显示出在无创预测GIST基因型方面的潜力。本研究试图确定放射组学特征是否可以帮助区分gist与D842V突变。方法:共纳入来自三个独立中心的872例经病理证实的GIST患者,并提供CECT数据,分为训练组(n = 487)和外部验证组(n = 385)。收集患者的临床特征,包括年龄、性别、肿瘤大小和部位。分析静脉期CECT最大轴向图像的放射组学特征,经特征选择后,共选择2个放射组学特征。随机森林模型只训练了非放射组学特征(非放射组学模型)和同时训练了非放射组学和放射组学特征(组合模型)。结果:联合模型的平均精度(0.250比0.102,p = 0.039)和F1评分(0.253比0.155,p = 0.012)均优于非放射组学模型。ROC-AUC (0.728 vs. 0.737, p = 0.836)和几何平均(0.737 vs. 0.681, p = 0.352)差异无统计学意义。结论:本研究证明了基于静脉期CECT图像的放射组学特征识别gist中D842V突变的潜力。我们的模型可能为无法进行序列变异检测的患者提供一种替代方法来指导TKI治疗,潜在地改善GIST患者的治疗效果,特别是在资源有限的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of D842V mutation in gastrointestinal stromal tumors based on CT radiomics: a multi-center study.

Background: Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Recent advent of tyrosine kinase inhibitors (TKIs) has significantly improved the prognosis of GIST patients. However, responses to TKI therapy can vary depending on the specific gene mutation. D842V, which is the most common mutation in platelet-derived growth factor receptor alpha exon 18, shows no response to imatinib and sunitinib. Radiomics features based on venous-phase contrast-enhanced computed tomography (CECT) have shown potential in non-invasive prediction of GIST genotypes. This study sought to determine whether radiomics features could help distinguish GISTs with D842V mutations.

Methods: A total of 872 pathologically confirmed GIST patients with CECT data available from three independent centers were included and divided into the training cohort ( n = 487 ) and the external validation cohort ( n = 385 ). Clinical features including age, sex, tumor size and location were collected. Radiomics features on the largest axial image of venous-phase CECT were analyzed and a total of two radiomics features were selected after feature selection. Random forest models trained on non-radiomics features only (the non-radiomics model) and on both non-radiomics and radiomics features (the combined model) were compared.

Results: The combined model showed better average precision (0.250 vs. 0.102, p = 0.039) and F1 score (0.253 vs. 0.155, p = 0.012) than the non-radiomics model. There was no significant difference in ROC-AUC (0.728 vs. 0.737, p = 0.836) and geometric mean (0.737 vs. 0.681, p = 0.352).

Conclusions: This study demonstrated the potential of radiomics features based on venous-phase CECT images to identify D842V mutation in GISTs. Our model may provide an alternative approach to guide TKI therapy for patients inaccessible to sequence variant testing, potentially improving treatment outcomes for GIST patients especially in resource-limited settings.

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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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