Ammari Samy, Bône Alexandre, Balleyguier Corinne, Moulton Eric, Chouzenoux Émilie, Volk Andreas, Menu Yves, Bidault François, Nicolas François, Robert Philippe, Rohé Marc-Michel, Lassau Nathalie
Guerbet Research, Villepinte.
Center for Visual Computing, CentraleSupélec, Inria, Université Paris-Saclay, Gif-sur-Yvette, France.
Invest Radiol. 2022 Feb 1;57(2):99-107. doi: 10.1097/RLI.0000000000000811.
This monocentric retrospective study leveraged 200 multiparametric brain MRIs acquired between November 2019 and February 2020 at Gustave Roussy Cancer Campus (Villejuif, France). A total of 145 patients were included: 107 formed the training sample (55 ± 14 years, 58 women) and 38 the separate test sample (62 ± 12 years, 22 women). Patients had glioma, brain metastases, meningioma, or no enhancing lesion. T1, T2-FLAIR, diffusion-weighted imaging, low-dose, and standard-dose postcontrast T1 sequences were acquired. A deep network was trained to process the precontrast and low-dose sequences to predict "virtual" surrogate images for contrast-enhanced T1. Once trained, the deep learning method was evaluated on the test sample. The discrepancies between the predicted virtual images and the standard-dose MRIs were qualitatively and quantitatively evaluated using both automated voxel-wise metrics and a reader study, where 2 radiologists graded image qualities and marked all visible enhancing lesions.
The automated analysis of the test brain MRIs computed a structural similarity index of 87.1% ± 4.8% between the predicted virtual sequences and the reference contrast-enhanced T1 MRIs, a peak signal-to-noise ratio of 31.6 ± 2.0 dB, and an area under the curve of 96.4% ± 3.1%. At Youden's operating point, the voxel-wise sensitivity (SE) and specificity were 96.4% and 94.8%, respectively. The reader study found that virtual images were preferred to standard-dose MRI in terms of image quality (P = 0.008). A total of 91 reference lesions were identified in the 38 test T1 sequences enhanced with full dose of contrast agent. On average across readers, the brain lesion SE of the virtual images was 83% for lesions larger than 10 mm (n = 42), and the associated false detection rate was 0.08 lesion/patient. The corresponding positive predictive value of detected lesions was 92%, and the F1 score was 88%. Lesion detection performance, however, dropped when smaller lesions were included: average SE was 67% for lesions larger than 5 mm (n = 74), and 56% with all lesions included regardless of their size. The false detection rate remained below 0.50 lesion/patient in all cases, and the positive predictive value remained above 73%. The composite F1 score was 63% at worst.
The proposed deep learning method for virtual contrast-enhanced T1 brain MRI prediction showed very high quantitative performance when evaluated with standard voxel-wise metrics. The reader study demonstrated that, for lesions larger than 10 mm, good detection performance could be maintained despite a 4-fold division in contrast agent usage, unveiling a promising avenue for reducing the gadolinium exposure of returning patients. Small lesions proved, however, difficult to handle for the deep network, showing that full-dose injections remain essential for accurate first-line diagnosis in neuro-oncology.
本单中心回顾性研究利用了2019年11月至2020年2月在古斯塔夫·鲁西癌症中心（法国维勒瑞夫）采集的200例多参数脑磁共振成像（MRI）。共纳入145例患者：107例组成训练样本（年龄55±14岁，女性58例），38例组成单独的测试样本（年龄62±12岁，女性22例）。患者患有胶质瘤、脑转移瘤、脑膜瘤或无强化病变。采集了T1、T2液体衰减反转恢复序列（T2-FLAIR）、扩散加权成像、低剂量和标准剂量对比剂增强后的T1序列。训练一个深度网络来处理对比剂增强前和低剂量序列，以预测对比剂增强T1的“虚拟”替代图像。训练完成后，在测试样本上对深度学习方法进行评估。使用自动体素级指标和阅片者研究对预测的虚拟图像与标准剂量MRI之间的差异进行定性和定量评估，由2名放射科医生对图像质量进行分级并标记所有可见的强化病变。
对测试脑MRI进行的自动分析显示，预测的虚拟序列与参考对比剂增强T1 MRI之间的结构相似性指数为87.1%±4.8%，峰值信噪比为31.6±2.0 dB，曲线下面积为96.4%±3.1%。在约登指数的最佳工作点，体素级灵敏度（SE）和特异度分别为96.4%和94.8%。阅片者研究发现，在图像质量方面，虚拟图像优于标准剂量MRI（P = 0.008）。在38例用全剂量对比剂增强的测试T1序列中，共识别出91个参考病变。在所有阅片者中，对于直径大于10 mm的病变（n = 42），虚拟图像的脑病变SE平均为83%，相关的假检测率为0.08个病变/患者。检测到的病变的相应阳性预测值为92%，F1分数为88%。然而，当纳入较小病变时，病变检测性能下降：对于直径大于5 mm的病变（n = 74），平均SE为67%，对于所有病变（无论大小），平均SE为56%。在所有情况下，假检测率均低于0.50个病变/患者，阳性预测值均高于73%。最差情况下，综合F1分数为63%。
所提出的用于虚拟对比剂增强T1脑MRI预测的深度学习方法在用标准体素级指标评估时显示出非常高的定量性能。阅片者研究表明，对于直径大于10 mm的病变，尽管对比剂使用量减少了4倍，但仍可保持良好的检测性能，这为减少复诊患者的钆暴露揭示了一条有前景的途径。然而，小病变被证明难以被深度网络处理，这表明全剂量注射对于神经肿瘤学的准确一线诊断仍然至关重要。
