Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer.
Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer.
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DOI：
10.3390/cancers10080249
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发表时间：
2018-07-28
期刊：
影响因子：
5.2
通讯作者：
Niazi T
中科院分区：
文献类型：
--
作者：
Chaddad A;Kucharczyk MJ;Niazi T
背景：新的放射组学特征使得能够从常规MRI图像序列中提取生物数据。本研究的目的是建立一个新的模型，基于联合强度矩阵（JIM），预测前列腺癌（PCa）患者的Gleason评分（GS）。研究方法：回顾性数据集包括99名前列腺癌患者的诊断成像数据，提取自癌症成像档案（TCIA）的T2加权（T2-WI）和表观扩散系数（ADC）图像。从报告的肿瘤位置提取来自JIM和灰度共生矩阵（GLCM）的放射组学特征。Kruskal-Wallis检验和斯皮尔曼等级相关性确定了与GS相关的特征。实施随机森林分类器模型以识别JIM和GLCM放射性组学特征的最佳性能特征以预测GS。结果如下：五个源自JIM的特性：对比度、同质性、差异方差、相异度和反差异是GS的独立预测因子（p < 0.05）。联合JIM和GLCM分析提供了最佳的曲线下面积，GS ≤ 6的值为78.40%，GS = 3 + 4的值为82.35%，GS ≥ 4 + 3的值为64.76%。结论：这项回顾性研究通过纳入标准诊断MRI图像的JIM数据，产生了一种新的GS预测模型。
Background: Novel radiomic features are enabling the extraction of biological data from routine sequences of MRI images. This study’s purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients. Methods: A retrospective dataset comprised of the diagnostic imaging data of 99 PCa patients was used, extracted from The Cancer Imaging Archive’s (TCIA) T2-Weighted (T2-WI) and apparent diffusion coefficient (ADC) images. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Kruskal-Wallis test and Spearman’s rank correlation identified features related to the GS. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS. Results: Five JIM-derived features: contrast, homogeneity, difference variance, dissimilarity, and inverse difference were independent predictors of GS (p < 0.05). Combined JIM and GLCM analysis provided the best performing area-under-the-curve, with values of 78.40% for GS ≤ 6, 82.35% for GS = 3 + 4, and 64.76% for GS ≥ 4 + 3. Conclusion: This retrospective study produced a novel predictive model for GS by the incorporation of JIM data from standard diagnostic MRI images.
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影响因子：
3.7
作者：
Heidegger, Isabel;Skradski, Viktor;Bektic, Jasmin
通讯作者：
Bektic, Jasmin
影响因子：
45.3
作者：
D'Amico, AV;Moul, J;Chen, MH
通讯作者：
Chen, MH
DOI：
10.1073/pnas.1505935112
发表时间：
2015-11-17
影响因子：
11.1
作者：
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通讯作者：
Deasy, Joseph O.
影响因子：
10.6
作者：
Madabhushi, A;Feldman, MD;Chute, D
通讯作者：
Chute, D
影响因子：
1.8
作者：
Archer, Kelfie J.;Kirnes, Ryan V.
通讯作者：
Kirnes, Ryan V.