Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, 410008, People's Republic of China.
Sci Rep. 2023 Feb 24;13(1):3216. doi: 10.1038/s41598-022-27213-8.
Non-invasive prediction for KIT/PDGFRA status in GIST is a challenging problem. This study aims to evaluate whether CT based sarcopenia could differentiate KIT/PDGFRA wild-type gastrointestinal stromal tumor (wt-GIST) from the mutant-type GIST (mu-GIST), and to evaluate genetic features of GIST. A total of 174 patients with GIST (wt-GIST = 52) were retrospectively identified between January 2011 to October 2019. A sarcopenia nomogram was constructed by multivariate logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Genomic data was obtained from our own specimens and also from the open databases cBioPortal. Data was analyzed by R version 3.6.1 and clusterProfiler ( http://cbioportal.org/msk-impact ). There were significantly higher incidence (75.0% vs. 48.4%) and more severe sarcopenia in patients with wt-GIST than in patients with mu-GIST. Multivariate logistic regression analysis showed that sarcopenia score (fitted based on age, gender and skeletal muscle index), and muscle fat index were independent predictors for higher risk of wt-GIST (P < 0.05 for both the training and validation cohorts). Our sarcopenia nomogram achieved a promising efficiency with an AUC of 0.879 for the training cohort, and 0.9099 for the validation cohort with a satisfying consistency in the calibration curve. Favorable clinical usefulness was observed using decision curve analysis. The additional gene sequencing analysis based on both our data and the external data demonstrated aberrant signal pathways being closely associated with sarcopenia in the wt-GIST. Our study supported the use of CT-based assessment of sarcopenia in differentiating the wt-GIST from the mu-GIST preoperatively.
在 GIST 中，对 KIT/PDGFRA 状态进行非侵入性预测是一个具有挑战性的问题。本研究旨在评估基于 CT 的肌肉减少症是否可以区分 KIT/PDGFRA 野生型胃肠道间质瘤（wt-GIST）和突变型 GIST（mu-GIST），并评估 GIST 的遗传特征。共回顾性分析了 2011 年 1 月至 2019 年 10 月间 174 例 GIST 患者（wt-GIST=52 例）。采用多变量逻辑回归构建肌肉减少症列线图。通过判别、校准曲线和决策曲线评估列线图的性能。基因组数据来自我们自己的标本，也来自开放数据库 cBioPortal。数据分析采用 R 版本 3.6.1 和 clusterProfiler（http://cbioportal.org/msk-impact）。与 mu-GIST 患者相比，wt-GIST 患者的发病率（75.0% vs. 48.4%）更高，肌肉减少症更严重。多变量逻辑回归分析显示，肌肉减少症评分（根据年龄、性别和骨骼肌指数拟合）和肌肉脂肪指数是 wt-GIST 风险较高的独立预测因素（训练和验证队列均 P<0.05）。我们的肌肉减少症列线图在训练队列中的 AUC 为 0.879，验证队列中的 AUC 为 0.9099，校准曲线一致性良好，具有良好的效率。决策曲线分析显示其具有良好的临床实用性。基于我们的数据和外部数据的额外基因测序分析表明，异常信号通路与 wt-GIST 中的肌肉减少症密切相关。本研究支持术前使用基于 CT 的肌肉减少症评估来区分 wt-GIST 和 mu-GIST。
