基于梯度回波和自旋回波对比增强 MRI 减少深度学习脑转移瘤检测中的假阳性：多中心诊断队列的验证。
Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort.
机构信息
Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-Ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea.
出版信息
Eur Radiol. 2024 May;34(5):2873-2884. doi: 10.1007/s00330-023-10318-7. Epub 2023 Oct 28.
OBJECTIVES
To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced DL) and evaluate it in a clinical cohort in comparison with human readers and DL using gradient-echo-based imaging only (GRE DL).
MATERIALS AND METHODS
DL detection was developed using data from 200 patients with BM (training set) and tested in 62 (internal) and 48 (external) consecutive patients who underwent stereotactic radiosurgery and diagnostic dual-enhanced imaging (dual-enhanced DL) and later guide GRE imaging (GRE DL). The detection sensitivity and positive predictive value (PPV) were compared between two DLs. Two neuroradiologists independently analyzed BM and reference standards for BM were separately drawn by another neuroradiologist. The relative differences (RDs) from the reference standard BM numbers were compared between the DLs and neuroradiologists.
RESULTS
Sensitivity was similar between GRE DL (93%, 95% confidence interval [CI]: 90-96%) and dual-enhanced DL (92% [89-94%]). The PPV of the dual-enhanced DL was higher (89% [86-92%], p < .001) than that of GRE DL (76%, [72-80%]). GRE DL significantly overestimated the number of metastases (false positives; RD: 0.05, 95% CI: 0.00-0.58) compared with neuroradiologists (RD: 0.00, 95% CI: - 0.28, 0.15, p < .001), whereas dual-enhanced DL (RD: 0.00, 95% CI: 0.00-0.15) did not show a statistically significant difference from neuroradiologists (RD: 0.00, 95% CI: - 0.20-0.10, p = .913).
CONCLUSION
The dual-enhanced DL showed improved detection of BM and reduced overestimation compared with GRE DL, achieving similar performance to neuroradiologists.
CLINICAL RELEVANCE STATEMENT
The use of deep learning-based brain metastasis detection with turbo spin-echo imaging reduces false positive detections, aiding in the guidance of stereotactic radiosurgery when gradient-echo imaging alone is employed.
KEY POINTS
•Deep learning for brain metastasis detection improved by using both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced deep learning). •Dual-enhanced deep learning increased true positive detections and reduced overestimation. •Dual-enhanced deep learning achieved similar performance to neuroradiologists for brain metastasis counts.
目的
开发一种深度学习（DL）方法，用于检测脑转移瘤（BM），该方法结合梯度和涡轮自旋回波对比增强 MRI（双重增强 DL），并与仅基于梯度回波的成像（GRE DL）的人类读者和 DL 进行比较。
材料和方法
使用 200 例 BM 患者的数据（训练集）开发 DL 检测，并在 62 例（内部）和 48 例（外部）连续患者中进行测试，这些患者接受立体定向放射外科手术和诊断性双重增强成像（双重增强 DL），随后指导 GRE 成像（GRE DL）。比较两种 DL 之间的检测灵敏度和阳性预测值（PPV）。两名神经放射科医生独立分析 BM，并由另一名神经放射科医生分别绘制 BM 的参考标准。比较 DL 和神经放射科医生之间与参考标准 BM 数量的相对差异（RD）。
结果
GRE DL（93%，95%置信区间[CI]：90-96%）和双重增强 DL（92%[89-94%]）之间的灵敏度相似。双重增强 DL 的 PPV 更高（89%[86-92%]，p<.001），而 GRE DL 的 PPV 更低（76%[72-80%]）。与神经放射科医生相比，GRE DL 显著高估了转移瘤的数量（假阳性；RD：0.05，95%CI：0.00-0.58）（RD：0.00，95%CI：-0.28，0.15，p<.001），而双重增强 DL（RD：0.00，95%CI：0.00-0.15）与神经放射科医生相比无统计学差异（RD：0.00，95%CI：-0.20-0.10，p=.913）。
结论
与 GRE DL 相比，双重增强 DL 显示出对 BM 的检测改善和高估减少，其性能与神经放射科医生相似。
临床相关性声明
当仅使用梯度回波成像时，基于深度学习的脑转移瘤检测中使用涡轮自旋回波成像可减少假阳性检测，有助于立体定向放射外科手术的指导。
要点
•通过使用梯度和涡轮自旋回波对比增强 MRI（双重增强深度学习），脑转移瘤检测的深度学习得到改善。•双重增强深度学习增加了真正的阳性检出率并减少了高估。•双重增强深度学习的脑转移瘤计数与神经放射科医生的表现相似。
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