Granata Vincenza, Fusco Roberta, Avallone Antonio, De Stefano Alfonso, Ottaiano Alessandro, Sbordone Carolina, Brunese Luca, Izzo Francesco, Petrillo Antonella
Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy.
Abdominal Oncology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80121 Naples, Italy.
Cancers (Basel). 2021 Jan 25;13(3):453. doi: 10.3390/cancers13030453.
: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. : 76 patients (36 women and 40 men; 59 years of mean age and 36-80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. : Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). : Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.
评估在肝转移瘤中,通过对比增强磁共振成像(CE-MRI)获得的RAS突变状态与影像组学衍生数据之间的关联。本回顾性研究纳入了76例患者(36名女性和40名男性;平均年龄59岁,年龄范围36 - 80岁)。计算基于病变形态的纹理指标和参数。进行了患者个体的单因素和多因素分析。考虑了Wilcoxon-Mann-Whitney U检验、受试者操作特征(ROC)分析以及带有特征选择方法的模式识别方法。纹理特征取得了显著结果,而形态学参数对RAS突变分类无显著结果。结果表明,采用单因素分析无法准确区分RAS突变状态。相反,考虑多因素分析和分类方法时,仅以纹理参数作为预测因子的KNN取得了最佳结果(训练集上的AUC为0.84,准确率为76.9%,灵敏度为90.0%,特异度为67.8%;外部验证队列上的准确率为87.5%,灵敏度为91.7%,特异度为83.3%)。通过CE-MRI获得并使用多因素分析和模式识别方法组合的纹理参数能够根据RAS突变状态对患者进行分层。