Caryn Geady, James J Bannon, Shagheyegh Reza, Laura Madanat-Harjuoja, Denise Reinke, Scott Schuetze, Brian Crompton, Andrew Hope, Benjamin Haibe-Kains
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引用次数: 0
摘要
放射组学提供了一种非侵入性的肿瘤表征方法,但其在转移性癌症中的应用受到肿瘤间异质性的限制-同一患者内不同病变放射组学表型的变异性。我们介绍了测量患者内部放射变异性(MIRV),这是一种使用标准护理成像量化异质性的新度量。应用于SARC021试验中的397例转移性软组织肉瘤患者,利用病变之间的两两欧几里得距离和余弦不相似度从预处理CT扫描中计算MIRV。欧几里得距离捕获放射特征的绝对差异,而余弦不相似性评估独立于大小的特征模式的变化。较高的MIRV与肿瘤特异性反应分类和体积反应的较大变异性相关,与基线肿瘤体积无关。在一个有液体活检数据的亚组中,MIRV显示出与ctDNA阳性的中度关联,表明与分子异质性有关。虽然在整个队列中,MIRV并不能预测总生存率,但在平滑肌肉瘤患者中,较高的MIRV与较差的生存率显著相关(n = 165, p = 0.007, FDR = 0.06)。这些发现确立了MIRV作为肿瘤间异质性的生物标志物,具有预测混合治疗反应和指导转移性STS个体化治疗的潜力。未来的研究应评估其在其他肿瘤类型和治疗环境中的相关性。
Measured intrapatient radiomic variability as a predictor of treatment response in multi-metastatic soft tissue sarcoma patients.
Radiomics offers a non-invasive approach to tumor characterization, yet its application in metastatic cancers is limited by intertumor heterogeneity-variability in radiomic phenotypes across lesions within the same patient. We introduce Measured Intrapatient Radiomic Variability (MIRV), a novel metric quantifying heterogeneity using standard-of-care imaging. Applied to 397 metastatic soft-tissue sarcoma patients from the SARC021 trial, MIRV was calculated from pretreatment CT scans using pairwise Euclidean distance and cosine dissimilarity between lesions. Euclidean distance captures absolute differences in radiomic features, while cosine dissimilarity assesses variation in feature patterns independent of magnitude. Higher MIRV correlated with greater variability in tumor-specific response classification and volumetric response, independent of baseline tumor volume. In a subset with liquid biopsy data, MIRV showed a moderate association with ctDNA positivity, suggesting links to molecular heterogeneity. While MIRV was not prognostic for overall survival in the full cohort, higher MIRV was significantly associated with worse survival in leiomyosarcoma patients (n = 165, p = 0.007, FDR = 0.06). These findings establish MIRV as a biomarker for intertumor heterogeneity, with potential to predict mixed treatment responses and guide personalized therapy in metastatic STS. Future studies should assess its relevance across other tumor types and therapeutic settings.
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