Radiomic phenotype features predict pathological response in non-small cell lung cancer.
Radiomic phenotype features predict pathological response in non-small cell lung cancer.
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DOI：
10.1016/j.radonc.2016.04.004
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Aerts HJ
中科院分区：
文献类型：
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作者：
Coroller TP;Agrawal V;Narayan V;Hou Y;Grossmann P;Lee SW;Mak RH;Aerts HJ
放射组学可以通过应用先进的成像特征算法非侵入性地量化肿瘤表型特征。在这项研究中，我们评估了治疗前放射组学数据是否能够预测局部晚期非小细胞肺癌（NSCLC）患者新辅助放化疗后的病理反应。本研究纳入了127例NSCLC患者。评价了基于稳定性和方差选择的15个放射组学特征预测病理反应的能力。使用曲线下面积（AUC）评价预测能力。常规成像特征（肿瘤体积和直径）用于比较。7个特征预测病理大体残留疾病（AUC > 0.6，p值< 0.05），1个预测病理完全反应（AUC = 0.63，p值= 0.01）。常规成像特征无预测性（范围AUC = 0.51-0.59，p值> 0.05）。对新辅助放化疗反应不佳的肿瘤更可能呈现更圆的形状（球形，AUC = 0.63，p值= 0.009）和异质纹理（LoG 5 mm 3D-GLCM熵，AUC = 0.61，p值= 0.03）。我们确定了病理反应的预测性放射组学特征，尽管没有常规特征具有显著的预测性。这项研究表明，放射组学可以提供有价值的临床信息，并比传统的成像功能更好。
Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC). 127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance were evaluated for its power to predict pathological response. Predictive power was evaluated using area under the curve (AUC). Conventional imaging features (tumor volume and diameter) were used for comparison. Seven features were predictive for pathologic gross residual disease (AUC > 0.6, p-value < 0.05), and one for pathologic complete response (AUC = 0.63, p-value = 0.01). No conventional imaging features were predictive (range AUC = 0.51–0.59, p-value > 0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present rounder shape (spherical disproportionality, AUC = 0.63, p-value = 0.009) and heterogeneous texture (LoG 5 mm 3D - GLCM entropy, AUC = 0.61, p-value = 0.03). We identified predictive radiomic features for pathological response, although no conventional features were significantly predictive. This study demonstrates that radiomics can provide valuable clinical information, and performed better than conventional imaging features.