利用扩展范围 b 因子扩散加权成像评估前列腺组织特征化的拟合模型。
Evaluation of fitting models for prostate tissue characterization using extended-range b-factor diffusion-weighted imaging.
机构信息
Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
出版信息
Magn Reson Med. 2018 Apr;79(4):2346-2358. doi: 10.1002/mrm.26831. Epub 2017 Jul 17.
PURPOSE
To compare the fitting and tissue discrimination performance of biexponential, kurtosis, stretched exponential, and gamma distribution models for high b-factor diffusion-weighted images in prostate cancer.
METHODS
Diffusion-weighted images with 15 b-factors ranging from b = 0 to 3500 s/mm were obtained in 62 prostate cancer patients. Pixel-wise signal decay fits for each model were evaluated with the Akaike Information Criterion (AIC). Parameter values for each model were determined within normal prostate and the index lesion. Their potential to differentiate normal from cancerous tissue was investigated through receiver operating characteristic analysis and comparison with Gleason score.
RESULTS
The biexponential slow diffusion fraction f , the apparent kurtosis diffusion coefficient ADC , and the excess kurtosis factor K differ significantly among normal peripheral zone (PZ), normal transition zone (TZ), tumor PZ, and tumor TZ. Biexponential and gamma distribution models result in the lowest AIC, indicating a superior fit. Maximum areas under the curve (AUCs) of all models ranged from 0.93 to 0.96 for the PZ and from 0.95 to 0.97 for the TZ. Similar AUCs also result from the apparent diffusion coefficient (ADC) of a monoexponential fit to a b-factor sub-range up to 1250 s/mm . For kurtosis and stretched exponential models, single parameters yield the highest AUCs, whereas for the biexponential and gamma distribution models, linear combinations of parameters produce the highest AUCs. Parameters with high AUC show a trend in differentiating low from high Gleason score, whereas parameters with low AUC show no such ability.
CONCLUSION
All models, including a monoexponential fit to a lower-b sub-range, achieve similar AUCs for discrimination of normal and cancer tissue. The biexponential model, which is favored statistically, also appears to provide insight into disease-related microstructural changes. Magn Reson Med 79:2346-2358, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
目的
比较双指数、峰度、拉伸指数和伽马分布模型在前列腺癌高 b 因子扩散加权图像中的拟合和组织分辨性能。
方法
在 62 例前列腺癌患者中获得了 15 个 b 值（b=0 至 3500 s/mm）的扩散加权图像。通过 Akaike 信息准则（AIC）评估每个模型的像素信号衰减拟合。在正常前列腺和病灶内确定每个模型的参数值。通过接受者操作特征分析并与 Gleason 评分进行比较，研究它们区分正常组织和癌组织的能力。
结果
在正常外周带（PZ）、正常移行带（TZ）、肿瘤 PZ 和肿瘤 TZ 中，双指数慢扩散分数 f 、表观峰度扩散系数 ADC 和超峰度因子 K 差异显著。双指数和伽马分布模型的 AIC 最低，表明拟合效果更好。所有模型在 PZ 的最大曲线下面积（AUC）范围为 0.93 至 0.96，在 TZ 的 AUC 范围为 0.95 至 0.97。对于单指数拟合到 1250 s/mm 以下 b 值的亚范围，表观扩散系数（ADC）也会产生相似的 AUC。对于峰度和拉伸指数模型，单个参数产生最高的 AUC，而对于双指数和伽马分布模型，参数的线性组合产生最高的 AUC。具有高 AUC 的参数显示出区分低 Gleason 评分和高 Gleason 评分的趋势，而 AUC 较低的参数则没有这种能力。
结论
包括对低 b 值子范围进行单指数拟合在内的所有模型在区分正常组织和癌组织方面都能达到相似的 AUC。在统计学上受到青睐的双指数模型似乎也能提供与疾病相关的微观结构变化的见解。磁共振医学 79：2346-2358，2018。© 2017 国际磁共振学会。
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