深度学习算法在乳腺 MRI 中非肿块样强化中的应用：与不同级别放射科医生解读的比较。
Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists' interpretations at various levels.
机构信息
Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi Hirokoji, Kamigyoku, Kyoto, 602-8566, Japan.
出版信息
Jpn J Radiol. 2023 Oct;41(10):1094-1103. doi: 10.1007/s11604-023-01435-w. Epub 2023 Apr 18.
PURPOSE
To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (ResNet50) neural network constructed from different segmentations for distinguishing malignant and benign non-mass enhancement (NME) on breast magnetic resonance imaging (MRI) and conduct a comparison with radiologists with various levels of experience.
MATERIALS AND METHODS
A total of 84 consecutive patients with 86 lesions (51 malignant, 35 benign) presenting NME on breast MRI were analyzed. Three radiologists with different levels of experience evaluated all examinations, based on the Breast Imaging-Reporting and Data System (BI-RADS) lexicon and categorization. For the deep learning method, one expert radiologist performed lesion annotation manually using the early phase of dynamic contrast-enhanced (DCE) MRI. Two segmentation methods were applied: a precise segmentation was carefully set to include only the enhancing area, and a rough segmentation covered the whole enhancing region, including the intervenient non-enhancing area. ResNet50 was implemented using the DCE MRI input. The diagnostic performance of the radiologists' readings and deep learning were then compared using receiver operating curve analysis.
RESULTS
The ResNet50 model from precise segmentation achieved diagnostic accuracy equivalent [area under the curve (AUC) = 0.91, 95% confidence interval (CI) 0.90, 0.93] to that of a highly experienced radiologist (AUC = 0.89, 95% CI 0.81, 0.96; p = 0.45). Even the model from rough segmentation showed diagnostic performance equivalent to a board-certified radiologist (AUC = 0.80, 95% CI 0.78, 0.82 vs. AUC = 0.79, 95% CI 0.70, 0.89, respectively). Both ResNet50 models from the precise and rough segmentation exceeded the diagnostic accuracy of a radiology resident (AUC = 0.64, 95% CI 0.52, 0.76).
CONCLUSION
These findings suggest that the deep learning model from ResNet50 has the potential to ensure accuracy in the diagnosis of NME on breast MRI.
目的
评估使用不同分割方法构建的残差网络 50（ResNet50）神经网络进行深度学习，以区分乳腺磁共振成像（MRI）上的恶性和良性非肿块强化（NME）的诊断性能，并与不同经验水平的放射科医生进行比较。
材料和方法
共分析了 84 例 86 个病灶（51 个恶性，35 个良性）的连续患者，这些患者在乳腺 MRI 上呈现 NME。三位具有不同经验水平的放射科医生根据乳腺影像报告和数据系统（BI-RADS）词汇和分类对所有检查进行评估。对于深度学习方法，一位专家放射科医生使用早期动态对比增强（DCE）MRI 手动进行病变标注。应用了两种分割方法：精细分割仔细设置以仅包括增强区域，而粗略分割则覆盖整个增强区域，包括中间无增强区域。使用 DCE MRI 输入实现 ResNet50。然后使用受试者工作特征曲线分析比较放射科医生阅读和深度学习的诊断性能。
结果
精确分割的 ResNet50 模型的诊断准确性与经验丰富的放射科医生相当（曲线下面积（AUC）=0.91，95%置信区间（CI）0.90，0.93；p=0.45）。即使是粗略分割的模型也表现出与 board-certified 放射科医生相当的诊断性能（AUC=0.80，95%CI 0.78，0.82 vs. AUC=0.79，95%CI 0.70，0.89，分别）。精确和粗略分割的两种 ResNet50 模型均超过了放射科住院医师的诊断准确性（AUC=0.64，95%CI 0.52，0.76）。
结论
这些发现表明，ResNet50 的深度学习模型有可能确保乳腺 MRI 上 NME 的诊断准确性。
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