Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach
Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach
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DOI：
10.1007/s00330-018-5368-4
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发表时间：
2018-09-01
影响因子：
5.9
通讯作者：
Lee, Seung-Koo
中科院分区：
文献类型：
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作者：
Suh, Hie Bum;Choi, Yoon Seong;Lee, Seung-Koo
为了评价基于磁共振（MR）放射组学的机器学习算法在区分原发性中枢神经系统淋巴瘤（PCNSL）和非坏死性非典型胶质母细胞瘤（GBM）方面的诊断性能，本回顾性研究纳入了2009年1月至2017年4月诊断的77例患者（54例PCNSL患者和23例非坏死性非典型GBM患者）。从多参数（对比后T1和T2加权和液体衰减反转恢复图像）和多区域（增强和非增强）肿瘤体积中提取了总共6，366个放射组学特征，包括形状、体积、一阶、纹理和小波变换特征。这些功能进行递归特征消除和随机森林（RF）分析嵌套交叉验证。放射组学机器学习分类器、表观扩散系数（ADC）和三名读者的诊断能力，他们根据传统MR序列对肿瘤进行独立分类，使用受试者工作特征（ROC）分析进行评估。比较放射组学分类器、ADC值和放射科医师的ROC曲线下面积（AUC），放射组学分类器的平均AUC为0.921（95%CI 0.825-0.990）。三名阅片员和ADC的AUC分别为0.707（95% CI 0.622-0.793）、0.759（95% CI 0.656-0.861）、0.695（95% CI 0.590-0.800）和0.684（95% CI 0.560 -0.809）。基于放射组学的分类器的AUC显著高于三个读取器和ADC（所有p < 0.001）。具有机器学习算法的大规模放射组学可用于区分PCNSL与非典型GBM，并产生比人类放射科医生和ADC值更好的诊断性能。学习算法放射组学可以帮助区分原发性中央PCNSL和GBM。aEuro cent这种方法比放射科医生的视觉分析产生更高的诊断准确性。aEuro cent放射组学可以加强放射科医生的诊断决策，常规的MRI序列是可用的。
To evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating primary central nervous system lymphoma (PCNSL) from non-necrotic atypical glioblastoma (GBM).Seventy-seven patients (54 individuals with PCNSL and 23 with non-necrotic atypical GBM), diagnosed from January 2009 to April 2017, were enrolled in this retrospective study. A total of 6,366 radiomics features, including shape, volume, first-order, texture, and wavelet-transformed features, were extracted from multi-parametric (post-contrast T1- and T2-weighted, and fluid attenuation inversion recovery images) and multiregional (enhanced and non-enhanced) tumour volumes. These features were subjected to recursive feature elimination and random forest (RF) analysis with nested cross-validation. The diagnostic abilities of a radiomics machine-learning classifier, apparent diffusion coefficient (ADC), and three readers, who independently classified the tumours based on conventional MR sequences, were evaluated using receiver operating characteristic (ROC) analysis. Areas under the ROC curves (AUC) of the radiomics classifier, ADC value, and the radiologists were compared.The mean AUC of the radiomics classifier was 0.921 (95 % CI 0.825-0.990). The AUCs of the three readers and ADC were 0.707 (95 % CI 0.622-0.793), 0.759 (95 %CI 0.656-0.861), 0.695 (95 % CI 0.590-0.800) and 0.684 (95 % CI0.560-0.809), respectively. The AUC of the radiomics-based classifier was significantly higher than those of the three readers and ADC (p < 0.001 for all).Large-scale radiomics with a machine-learning algorithm can be useful for differentiating PCNSL from atypical GBM, and yields a better diagnostic performance than human radiologists and ADC values.aEuro cent Machine-learning algorithm radiomics can help to differentiate primary central PCNSL from GBM.aEuro cent This approach yields a higher diagnostic accuracy than visual analysis by radiologists.aEuro cent Radiomics can strengthen radiologists' diagnostic decisions whenever conventional MRI sequences are available.