Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
NMR Biomed. 2023 Jan;36(1):e4824. doi: 10.1002/nbm.4824. Epub 2022 Sep 24.
The purpose of this study was to evaluate the value of amide proton transfer-weighted (APTw) MRI radiomic features for the differentiation of tumor recurrence from treatment effect in malignant gliomas. Eighty-six patients who had suspected tumor recurrence after completion of chemoradiation or radiotherapy, and who had APTw-MRI data acquired at 3 T, were retrospectively analyzed. Using a fluid-attenuated inversion recovery (FLAIR) image-based mask, radiomics analysis was applied to the processed APTw and structural MR images. A chi-square automatic interaction detector decision tree was used for classification analysis. Models with and without APTw features were built using the same strategy. Tenfold cross-validation was applied to obtain the overall classification performance of each model. Sixty patients were confirmed as having tumor recurrence, and the remainder were confirmed as having treatment effect, at median time points of 190 and 171 days after therapy, respectively. There were 525 radiomic features extracted from each of the processed APTw and structural MR images. Based on these, the APTw-based model yielded the highest accuracy (86.0%) for the differentiation of tumor recurrence from treatment effect, compared with 74.4%, 76.7%, 83.7%, and 76.7% for T w, T w, FLAIR, and Gd-T w, respectively. Model classification accuracy was 82.6% when using the combined structural MR images (T w, T w, FLAIR, Gd-T w), and increased to 89.5% when using these structural plus APTw images. The corresponding sensitivity and specificity were 85.0% and 76.9% for the combination of structural MR images, and 85.0% and 100% after adding APTw image features. Adding APTw-based radiomic features increased MRI accuracy in the assessment of the treatment response in post-treatment malignant gliomas.
本研究旨在评估酰胺质子转移加权（APTw）MRI 放射组学特征在区分恶性胶质瘤肿瘤复发与治疗效果中的价值。回顾性分析了 86 例完成放化疗或放疗后怀疑肿瘤复发且在 3T 下获得 APTw-MRI 数据的患者。使用基于液体衰减反转恢复（FLAIR）图像的掩模，对处理后的 APTw 和结构 MRI 图像进行放射组学分析。使用卡方自动交互检测决策树进行分类分析。使用相同的策略构建包含和不包含 APTw 特征的模型。采用 10 倍交叉验证获得每个模型的总体分类性能。60 例患者在治疗后 190 天和 171 天的中位数时间点被证实为肿瘤复发，其余患者被证实为治疗效果。从处理后的 APTw 和结构 MRI 图像中分别提取了 525 个放射组学特征。在此基础上，APTw 模型在区分肿瘤复发与治疗效果方面的准确率最高（86.0%），而 T w、T w、FLAIR 和 Gd-T w 的准确率分别为 74.4%、76.7%、83.7%和 76.7%。当使用联合结构 MRI 图像（T w、T w、FLAIR、Gd-T w）时，模型分类准确率为 82.6%，当使用这些结构加 APTw 图像时，准确率提高到 89.5%。联合结构 MRI 图像的敏感性和特异性分别为 85.0%和 76.9%，加入 APTw 图像特征后分别为 85.0%和 100%。在评估治疗后恶性胶质瘤的治疗反应时，添加基于 APTw 的放射组学特征可提高 MRI 准确性。
