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Heterogeneity in DCE-MRI parametric maps: a biomarker for treatment response?

机译：DCE-MRI参数图中的异质性：治疗反应的生物标志物？

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This study aims to quantify the heterogeneity of tumour enhancement in dynamic contrast-enhanced MRI (DCE-MRI) using texture analysis methods. The suitability of the coherence and the fractal dimension to monitor tumour response was evaluated in 18 patients with limb sarcomas imaged by DCE-MRI pre- and post-treatment. According to the histopathology, tumours were classified into responders and non-responders. Pharmacokinetic (K(trans)) and heuristic model-based parametric maps (slope, max enhancement, AUC) were computed from the DCE-MRI data. A substantial correlation was found between the pharmacokinetic and heuristic model-based parametric maps: rho = 0.56 for the slope, rho = 0.44 for maximum enhancement, and rho = 0.61 for AUC. From all four parametric maps, the enhancing fraction, and the heterogeneity features (i.e. coherence and fractal dimension) were determined. In terms of monitoring tumour response, using both pre- and post-treatment DCE-MRI, the enhancing fraction and the coherence showed significant differences between the response group and the non-response group (i.e. the highest sensitivity (91%) for K(trans), and the highest specificity (83%) for max enhancement). In terms of treatment prediction, using solely the pre-treatment DCE-MRI, the enhancing fraction and coherence discriminated between responders and non-responders. For prediction, the highest sensitivity (91%) was shared by K(trans), slope and max enhancement, and the highest specificity (71%) was achieved by K(trans). On average, tumours that responded showed a high enhancing fraction and high coherence on the pre-treatment scan. These results suggest that specific heterogeneity features, computed from both pharmacokinetic and heuristic model-based parametric maps, show potential as a biomarker for monitoring tumour response.
机译：这项研究旨在量化使用纹理分析方法的动态对比增强MRI（DCE-MRI）中肿瘤增强的异质性。通过DCE-MRI治疗前后，对18例肢体肉瘤患者的连贯性和分形维数进行监测，以监测肿瘤反应。根据组织病理学，将肿瘤分为反应者和非反应者。从DCE-MRI数据计算出药代动力学（K（trans））和基于启发式模型的参数图（斜率，最大增强，AUC）。在基于药代动力学和启发式模型的参数图之间发现了显着的相关性：斜率的rho = 0.56，最大增强值的rho = 0.44，AUC的rho = 0.61。从所有四个参数图确定增强分数和异质性特征（即相干性和分形维数）。在监测肿瘤反应方面，使用治疗前和治疗后DCE-MRI均显示，反应组和无反应组之间的增强分数和相干性显示出显着差异（即，对K（敏感性）最高（91％）反式）和最高的特异性（最大增强的特异性为83％）。就治疗预测而言，仅使用治疗前的DCE-MRI，可区分反应者和非反应者的增强率和相干性。为了进行预测，K（trans），斜率和最大增强值具有最高的敏感性（91％），而K（trans）则具有最高的特异性（71％）。平均而言，响应的肿瘤在治疗前扫描中显示出较高的增强率和较高的相干性。这些结果表明，从基于药代动力学和启发式模型的参数图计算得出的特定异质性特征显示出潜在的生物标志物，可用于监测肿瘤反应。

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