PDF(Pubmed)
Abstract：
BACKGROUND: 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI.
METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant.
RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist.
CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.
METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant.
RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist.
CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.
摘要：
背景：低级别胶质瘤中的1p/19q共缺失（LGG，世界卫生组织II级和III级）在临床决策中具有重要意义。我们的目标是使用影像组学分析来预测基于酰胺质子转移加权（APTw）的LGG中的1p/19q共缺失，弥散加权成像(DWI),和常规MRI。
方法：这项回顾性研究包括90例经组织病理学诊断为LGG的患者。我们通过从APTw中提取8454基于MRI的特征进行了影像组学分析，DWI和常规MR图像，并应用最小绝对收缩和选择运算符（LASSO）算法来选择影像组学签名。使用针对每个患者加权的所选特征的值的线性组合来生成放射组学评分(Rad评分)。三位神经放射学家,包括一位有经验的神经放射学家和两位住院医师,独立评估了LGG的MR特征,并对肿瘤是否存在1p/19q共缺失或1p/19q完整状态进行了预测.然后基于该分析中确定的重要变量构建临床模型。还构建了包含Rad评分和临床因素的组合模型。通过接收器工作特性曲线分析验证了预测性能，德隆分析和决策曲线分析P<0.05有统计学意义。
结果：影像组学模型和组合模型在训练集和测试集上均表现出优异的性能，曲线下面积（AUC）分别为0.948和0.966，以及0.909和0.896。这些结果超过了临床模型的性能，在训练集和测试集上实现了0.760和0.766的AUC，分别。在进行了德隆分析之后，3名神经放射科医师的临床模型在预测性能方面没有显著差异.在训练集中，影像组学和组合模型的表现均优于所有神经放射科医师.在测试集中，这些模型显示出比神经放射学家更高的AUC，随着影像组学模型的表现明显优于住院医师B和C，但与有经验的神经放射学家没有显著差异。
结论：我们的结果表明，我们的算法可以无创预测LGG的1p/19q共缺失状态。影像组学模型的预测性能与经验丰富的神经放射学家相当，显著优于住院医师的诊断准确性，从而提供了促进LGG的非侵入性1p/19q共缺失预测的潜力。
方法：这项回顾性研究包括90例经组织病理学诊断为LGG的患者。我们通过从APTw中提取8454基于MRI的特征进行了影像组学分析，DWI和常规MR图像，并应用最小绝对收缩和选择运算符（LASSO）算法来选择影像组学签名。使用针对每个患者加权的所选特征的值的线性组合来生成放射组学评分(Rad评分)。三位神经放射学家,包括一位有经验的神经放射学家和两位住院医师,独立评估了LGG的MR特征,并对肿瘤是否存在1p/19q共缺失或1p/19q完整状态进行了预测.然后基于该分析中确定的重要变量构建临床模型。还构建了包含Rad评分和临床因素的组合模型。通过接收器工作特性曲线分析验证了预测性能，德隆分析和决策曲线分析P<0.05有统计学意义。
结果：影像组学模型和组合模型在训练集和测试集上均表现出优异的性能，曲线下面积（AUC）分别为0.948和0.966，以及0.909和0.896。这些结果超过了临床模型的性能，在训练集和测试集上实现了0.760和0.766的AUC，分别。在进行了德隆分析之后，3名神经放射科医师的临床模型在预测性能方面没有显著差异.在训练集中，影像组学和组合模型的表现均优于所有神经放射科医师.在测试集中，这些模型显示出比神经放射学家更高的AUC，随着影像组学模型的表现明显优于住院医师B和C，但与有经验的神经放射学家没有显著差异。
结论：我们的结果表明，我们的算法可以无创预测LGG的1p/19q共缺失状态。影像组学模型的预测性能与经验丰富的神经放射学家相当，显著优于住院医师的诊断准确性，从而提供了促进LGG的非侵入性1p/19q共缺失预测的潜力。
