Predicting future morphological changes of lesions from radiotracer uptake in 18F-FDG-PET images.
Predicting future morphological changes of lesions from radiotracer uptake in 18F-FDG-PET images.
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DOI：
10.1371/journal.pone.0057105
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Mollura DJ
中科院分区：
文献类型：
--
作者：
Bagci U;Yao J;Miller-Jaster K;Chen X;Mollura DJ
我们引入了一种新的计算框架，使自动识别的纹理和形状特征的病变18F-FDG-PET图像通过基于图形的图像分割方法。所提出的框架预测未来的病变形态变化具有很高的准确性。所提出的方法与传统的定性和半定量方法相比具有几个优点，这是由于其在（i）检测、（ii）分割和（iii）特征提取的每个步骤中的完全定量性质和高精度。为了评估我们提出的计算框架，30名患者在两个不同的时间点接受了2次18F-FDG-PET扫描（共60次扫描）。本分析包括转移性乳头状肾细胞癌、小脑成血管细胞瘤、非小细胞肺癌、神经纤维瘤、淋巴瘤样肉芽肿病、肺肿瘤、神经内分泌肿瘤、软组织胸部肿块、非坏死性肉芽肿性炎症、具有乳头状和囊性特征的肾细胞癌、弥漫性大B细胞淋巴瘤、转移性腺泡状软组织肉瘤和小细胞肺癌。该分割算法能够自动检测和分割出患者扫描图像中的放射性示踪剂积聚。利用所提出的自适应特征提取框架提取轮廓区域的形状和纹理特征，并利用标准化摄取值（SUV）对摄取区域进行定量分析。分割结果表明，我们提出的分割算法具有平均骰子相似系数为85.75± 1.75%。我们发现，在68个提取的成像特征中，有28个与SUVmax有很好的相关性（p<0.05），并且与单一强度特征（如SUVmax）相比，一些纹理特征（如熵和最大概率）在预测放射性示踪剂摄取区域的纵向形态变化方面具有上级优势。我们还发现，将纹理特征与SUV测量相结合显著提高了形态变化的预测准确性（斯皮尔曼相关系数= 0.8715，p<2 e-16）。
We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on 18F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 18F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75±1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUVmax (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUVmax. We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).
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3.8
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