使用T2加权和扩散加权图像鉴别肾良性与恶性肿瘤：深度学习和影像组学模型与放射科医生评估的比较
Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists.
作者信息
Xu Qing, Zhu QingQiang, Liu Hao, Chang LuFan, Duan ShaoFeng, Dou WeiQiang, Li SaiYang, Ye Jing
机构信息
Department of Medical Imaging, Clinic Medical School, Yangzhou University, Northern Jiangsu Province Hospital, Yangzhou, China.
Yizhun Medical AI, Beijing, China.
出版信息
J Magn Reson Imaging. 2022 Apr;55(4):1251-1259. doi: 10.1002/jmri.27900. Epub 2021 Aug 30.
BACKGROUND
Differentiating benign from malignant renal tumors is important for selection of the most effective treatment.
PURPOSE
To develop magnetic resonance imaging (MRI)-based deep learning (DL) models for differentiation of benign and malignant renal tumors and to compare their discrimination performance with the performance of radiomics models and assessment by radiologists.
STUDY TYPE
Retrospective.
POPULATION
A total of 217 patients were randomly assigned to a training cohort (N = 173) or a testing cohort (N = 44).
FIELD STRENGTH/SEQUENCE: Diffusion-weighted imaging (DWI) and fast spin-echo sequence T2-weighted imaging (T2WI) at 3.0T.
ASSESSMENT
A radiologist manually labeled the region of interest (ROI) on each image. Three DL models using ResNet-18 architecture and three radiomics models using random forest were developed using T2WI alone, DWI alone, and a combination of the two image sets to discriminate between benign and malignant renal tumors. The diagnostic performance of two radiologists was assessed based on professional experience. We also compared the performance of each model and the radiologists.
STATISTICAL TESTS
The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of each model and the radiologists. P < 0.05 indicated statistical significance.
RESULTS
The AUC of the DL models based on T2WI, DWI, and the combination was 0.906, 0.846, and 0.925 in the testing cohorts, respectively. The AUC of the combination DL model was significantly better than that of the models based on individual sequences (0.925 > 0.906, 0.925 > 0.846). The AUC of the radiomics models based on T2WI, DWI, and the combination was 0.824, 0.742, and 0.826 in the testing cohorts, respectively. The AUC of two radiologists was 0.724 and 0.667 in the testing cohorts.
CONCLUSION
Thus, the MRI-based DL model is useful for differentiating benign from malignant renal tumors in clinic, and the DL model based on T2WI + DWI had the best performance.
LEVEL OF EVIDENCE
3 TECHNICAL EFFICACY STAGE: 2.
背景
区分肾肿瘤的良恶性对于选择最有效的治疗方法很重要。
目的
开发基于磁共振成像（MRI）的深度学习（DL）模型以区分肾肿瘤的良恶性，并将其判别性能与放射组学模型的性能以及放射科医生的评估结果进行比较。
研究类型
回顾性研究。
研究对象
总共217例患者被随机分配到训练队列（N = 173）或测试队列（N = 44）。
场强/序列：3.0T下的扩散加权成像（DWI）和快速自旋回波序列T2加权成像（T2WI）。
评估
一名放射科医生在每张图像上手动标记感兴趣区域（ROI）。使用单独的T2WI、单独的DWI以及两个图像集的组合，开发了三个使用ResNet-18架构的DL模型和三个使用随机森林的放射组学模型，以区分肾肿瘤的良恶性。根据专业经验评估两名放射科医生的诊断性能。我们还比较了每个模型和放射科医生的性能。
统计检验
采用受试者操作特征（ROC）曲线下面积（AUC）评估每个模型和放射科医生的性能。P < 0.05表示具有统计学意义。
结果
在测试队列中，基于T2WI、DWI以及两者组合的DL模型的AUC分别为0.906、0.846和0.925。组合DL模型的AUC显著优于基于单个序列的模型（0.925 > 0.906，0.925 > 0.846）。在测试队列中，基于T2WI、DWI以及两者组合的放射组学模型的AUC分别为0.824、0.742和0.826。两名放射科医生在测试队列中的AUC分别为0.724和0.667。
结论
因此，基于MRI的DL模型在临床上有助于区分肾肿瘤的良恶性，且基于T2WI + DWI的DL模型性能最佳。
证据水平
3 技术效能阶段：2。
Zotero 插件