Manikis Georgios C, Marias Kostas, Lambregts Doenja M J, Nikiforaki Katerina, van Heeswijk Miriam M, Bakers Frans C H, Beets-Tan Regina G H, Papanikolaou Nikolaos
Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational Biomedicine Lab, Heraklion, Greece.
Department of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands.
PLoS One. 2017 Sep 1;12(9):e0184197. doi: 10.1371/journal.pone.0184197. eCollection 2017.
The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer.
Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio.
All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area.
No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.
本研究旨在比较四种扩散模型（包括单指数和双指数高斯模型以及非高斯模型）在直肠癌扩散加权成像中的性能。
19例直肠腺癌患者在放化疗前接受了直肠MRI检查，包括在1.5T扫描仪上进行的7个b值扩散序列（0、25、50、100、500、1000和2000 s/mm²）。将四种不同的扩散模型（包括单指数和双指数高斯模型（MG和BG）以及非高斯模型（MNG和BNG））应用于整个感兴趣的肿瘤体积。采用两种不同的统计标准来评估它们的拟合性能，包括调整后的R²和均方根误差（RMSE）。为了确定哪种模型能更好地表征直肠癌，模型选择依赖于赤池信息准则（AIC）和F比率。
所有候选模型均具有良好的拟合性能，其中两个最复杂的模型BG和BNG表现出最佳的拟合性能。然而，两种模型选择标准均表明MG模型的性能优于其他任何模型。特别是，基于AIC权重和F比率的基于像素的分析表明，在平均面积分别为53%和33%的情况下，最简单的MG模型能更好地描述肿瘤区域。根据F比率，非高斯行为在平均面积为37%的区域中得到体现，而使用AIC权重时为7%。然而，四个模型各自最佳拟合的像素分布表明，MG模型在所有患者和整个肿瘤区域中并非都比其他模型表现更好。
本文评估的单一扩散模型均无法准确描述直肠肿瘤。这些发现可能可以基于肿瘤异质性增加来解释，其中血管丰富的区域可能更适合双指数模型，而坏死区域大多遵循单指数行为。
