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Abstract：
BACKGROUND: Radiomics, an evolving paradigm in medical imaging, involves the quantitative analysis of tumor features and demonstrates promise in predicting treatment responses and outcomes. This study aims to investigate the predictive capacity of radiomics for genetic alterations in non-small cell lung cancer (NSCLC).
METHODS: This exploratory, observational study integrated radiomic perspectives using computed tomography (CT) and genomic perspectives through next-generation sequencing (NGS) applied to liquid biopsies. Associations between radiomic features and genetic mutations were established using the Area Under the Receiver Operating Characteristic curve (AUC-ROC). Machine learning techniques, including Support Vector Machine (SVM) classification, aim to predict genetic mutations based on radiomic features. The prognostic impact of selected gene variants was assessed using Kaplan-Meier curves and Log-rank tests.
RESULTS: Sixty-six patients underwent screening, with fifty-seven being comprehensively characterized radiomically and genomically. Predominantly males (68.4%), adenocarcinoma was the prevalent histological type (73.7%). Disease staging is distributed across I/II (38.6%), III (31.6%), and IV (29.8%). Significant correlations were identified with mutations of ROS1 p.Thr145Pro (shape_Sphericity), ROS1 p.Arg167Gln (glszm_ZoneEntropy, firstorder_TotalEnergy), ROS1 p.Asp2213Asn (glszm_GrayLevelVariance, firstorder_RootMeanSquared), and ALK p.Asp1529Glu (glcm_Imc1). Patients with the ROS1 p.Thr145Pro variant demonstrated markedly shorter median survival compared to the wild-type group (9.7 months vs. not reached, p = 0.0143; HR: 5.35; 95% CI: 1.39-20.48).
CONCLUSIONS: The exploration of the intersection between radiomics and cancer genetics in NSCLC is not only feasible but also holds the potential to improve genetic predictions and enhance prognostic accuracy.
METHODS: This exploratory, observational study integrated radiomic perspectives using computed tomography (CT) and genomic perspectives through next-generation sequencing (NGS) applied to liquid biopsies. Associations between radiomic features and genetic mutations were established using the Area Under the Receiver Operating Characteristic curve (AUC-ROC). Machine learning techniques, including Support Vector Machine (SVM) classification, aim to predict genetic mutations based on radiomic features. The prognostic impact of selected gene variants was assessed using Kaplan-Meier curves and Log-rank tests.
RESULTS: Sixty-six patients underwent screening, with fifty-seven being comprehensively characterized radiomically and genomically. Predominantly males (68.4%), adenocarcinoma was the prevalent histological type (73.7%). Disease staging is distributed across I/II (38.6%), III (31.6%), and IV (29.8%). Significant correlations were identified with mutations of ROS1 p.Thr145Pro (shape_Sphericity), ROS1 p.Arg167Gln (glszm_ZoneEntropy, firstorder_TotalEnergy), ROS1 p.Asp2213Asn (glszm_GrayLevelVariance, firstorder_RootMeanSquared), and ALK p.Asp1529Glu (glcm_Imc1). Patients with the ROS1 p.Thr145Pro variant demonstrated markedly shorter median survival compared to the wild-type group (9.7 months vs. not reached, p = 0.0143; HR: 5.35; 95% CI: 1.39-20.48).
CONCLUSIONS: The exploration of the intersection between radiomics and cancer genetics in NSCLC is not only feasible but also holds the potential to improve genetic predictions and enhance prognostic accuracy.
摘要：
背景：影像组学，医学成像中不断发展的范式，涉及肿瘤特征的定量分析，并在预测治疗反应和结果方面显示出希望。本研究旨在探讨影像组学对非小细胞肺癌（NSCLC）遗传改变的预测能力。
方法：这是探索性的，观察性研究整合了使用计算机断层扫描（CT）的放射学观点和通过应用于液体活检的下一代测序（NGS）的基因组观点。使用接受者工作特征曲线下面积(AUC-ROC)建立放射学特征与遗传突变之间的关联。机器学习技术,包括支持向量机(SVM)分类，目的是根据放射学特征预测基因突变。使用Kaplan-Meier曲线和Log-rank检验评估所选基因变体的预后影响。
结果：66名患者接受了筛查，57个被全面的放射学和基因学特征。以男性为主（68.4%），腺癌是常见的组织学类型(73.7%)。疾病分期分布在I/II（38.6％），III（31.6%），和IV（29.8%）。与ROS1p.Thr145Pro(shape_Sphericity)的突变显著相关，ROS1p.Arg167Gln(glszm_ZoneEntropy,firstorder_TotalEnergy)，ROS1p.Asp2213Asn(glszm_灰度方差，firstorder_RootMeanSquared)，和ALKp.Asp1529Glu（glcm_Imc1）。与野生型组相比，ROS1p.Thr145Pro变体的患者中位生存期明显缩短（9.7个月vs.没有到达,p=0.0143；HR：5.35；95%CI：1.39-20.48）。
结论：探索非小细胞肺癌的影像组学与癌症遗传学之间的交叉不仅是可行的，而且具有改善遗传预测和提高预后准确性的潜力。
方法：这是探索性的，观察性研究整合了使用计算机断层扫描（CT）的放射学观点和通过应用于液体活检的下一代测序（NGS）的基因组观点。使用接受者工作特征曲线下面积(AUC-ROC)建立放射学特征与遗传突变之间的关联。机器学习技术,包括支持向量机(SVM)分类，目的是根据放射学特征预测基因突变。使用Kaplan-Meier曲线和Log-rank检验评估所选基因变体的预后影响。
结果：66名患者接受了筛查，57个被全面的放射学和基因学特征。以男性为主（68.4%），腺癌是常见的组织学类型(73.7%)。疾病分期分布在I/II（38.6％），III（31.6%），和IV（29.8%）。与ROS1p.Thr145Pro(shape_Sphericity)的突变显著相关，ROS1p.Arg167Gln(glszm_ZoneEntropy,firstorder_TotalEnergy)，ROS1p.Asp2213Asn(glszm_灰度方差，firstorder_RootMeanSquared)，和ALKp.Asp1529Glu（glcm_Imc1）。与野生型组相比，ROS1p.Thr145Pro变体的患者中位生存期明显缩短（9.7个月vs.没有到达,p=0.0143；HR：5.35；95%CI：1.39-20.48）。
结论：探索非小细胞肺癌的影像组学与癌症遗传学之间的交叉不仅是可行的，而且具有改善遗传预测和提高预后准确性的潜力。
