Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer
Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer
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DOI：
10.1007/s11547-017-0838-3
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发表时间：
2018-04-01
影响因子：
8.9
通讯作者：
Valentini, Vincenzo
中科院分区：
文献类型：
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作者：
Cusumano, Davide;Dinapoli, Nicola;Valentini, Vincenzo
本研究的目的是提出一种方法来调查肿瘤异质性，并评估其预测局部进展期直肠癌（LARC）放化疗（CRT）后病理完全缓解（pCR）的能力。这种方法包括对肿瘤的像素强度进行标准化，并使用基于强度的阈值来识别不同的子区域。这些亚群的空间组织进行了量化使用分形维数（FD）。该方法在放射组学工作流程中实施，并应用于LARC患者的198个T2加权治疗前磁共振（MR）图像。三种类型的特征提取的总肿瘤体积（GTV）：形态，统计和分形特征。使用Wilcoxon检验进行特征选择，并计算逻辑回归模型以预测CRT后的pCR概率。考虑到在两个机构治疗的患者，该模型进行了阐述：罗马的“Agostino Gemelli”基金会（173例，训练集）和马斯特里赫特的大学医学中心（25例，验证集）。结果表明，亚群的分形参数在预测pCR中具有最高的性能。所阐述的预测模型的曲线下面积（AUC）等于0.77 +/- 0.07。验证集证实了模型可靠性（AUC = 0.79 +/- 0.09）。这项研究表明，分形分析可以在放射组学中发挥重要作用，提供有价值的信息，不仅对GTV的结构，而且其内部亚群。
The aim of this study was to propose a methodology to investigate the tumour heterogeneity and evaluate its ability to predict pathologically complete response (pCR) after chemo-radiotherapy (CRT) in locally advanced rectal cancer (LARC). This approach consisted in normalising the pixel intensities of the tumour and identifying the different sub-regions using an intensity-based thresholding. The spatial organisation of these subpopulations was quantified using the fractal dimension (FD). This approach was implemented in a radiomic workflow and applied to 198 T2-weighted pre-treatment magnetic resonance (MR) images of LARC patients. Three types of features were extracted from the gross tumour volume (GTV): morphological, statistical and fractal features. Feature selection was performed using the Wilcoxon test and a logistic regression model was calculated to predict the pCR probability after CRT. The model was elaborated considering the patients treated in two institutions: Fondazione Policlinico Universitario "Agostino Gemelli" of Rome (173 cases, training set) and University Medical Centre of Maastricht (25 cases, validation set). The results obtained showed that the fractal parameters of the subpopulations have the highest performance in predicting pCR. The predictive model elaborated had an area under the curve (AUC) equal to 0.77 +/- 0.07. The model reliability was confirmed by the validation set (AUC = 0.79 +/- 0.09). This study suggests that the fractal analysis can play an important role in radiomics, providing valuable information not only about the GTV structure, but also about its inner subpopulations.