Xie Xiaoyang, Yang Lijuan, Zhao Fengjun, Wang Dong, Zhang Hui, He Xuelei, Cao Xin, Yi Huangjian, He Xiaowei, Hou Yuqing
Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, 710069, Shaanxi, China.
Department of Radiology, Xi'an Fourth Hospital, Xi'an, 710004, Shaanxi, China.
Eur Radiol. 2022 Oct;32(10):6922-6932. doi: 10.1007/s00330-022-08857-6. Epub 2022 Jun 8.
To evaluate the value of deep learning (DL) combining multimodal radiomics and clinical and imaging features for differentiating ocular adnexal lymphoma (OAL) from idiopathic orbital inflammation (IOI).
Eighty-nine patients with histopathologically confirmed OAL (n = 39) and IOI (n = 50) were divided into training and validation groups. Convolutional neural networks and multimodal fusion layers were used to extract multimodal radiomics features from the T1-weighted image (T1WI), T2-weighted image, and contrast-enhanced T1WI. These multimodal radiomics features were then combined with clinical and imaging features and used together to differentiate between OAL and IOI. The area under the curve (AUC) was used to evaluate DL models with different features under five-fold cross-validation. The Student t-test, chi-squared, or Fisher exact test was used for comparison of different groups.
In the validation group, the diagnostic AUC of the DL model using combined features was 0.953 (95% CI, 0.895-1.000), higher than that of the DL model using multimodal radiomics features (0.843, 95% CI, 0.786-0.898, p < 0.01) or clinical and imaging features only (0.882, 95% CI, 0.782-0.982, p = 0.13). The DL model built on multimodal radiomics features outperformed those built on most bimodalities and unimodalities (p < 0.05). In addition, the DL-based analysis with the orbital cone area (covering both the orbital mass and surrounding tissues) was superior to that with the region of interest (ROI) covering only the mass area, although the difference was not significant (p = 0.33).
DL-based analysis that combines multimodal radiomics features with clinical and imaging features may help to differentiate between OAL and IOI.
• It is difficult to differentiate OAL from IOI due to the overlap in clinical and imaging manifestations. • Radiomics has shown potential for noninvasive diagnosis of different orbital lymphoproliferative disorders. • DL-based analysis combining radiomics and imaging and clinical features may help the differentiation between OAL and IOI.
评估深度学习（DL）结合多模态影像组学以及临床和影像特征在鉴别眼附属器淋巴瘤（OAL）与特发性眼眶炎症（IOI）中的价值。
89例经组织病理学确诊的OAL患者（n = 39）和IOI患者（n = 50）被分为训练组和验证组。使用卷积神经网络和多模态融合层从T1加权图像（T1WI）、T2加权图像和对比增强T1WI中提取多模态影像组学特征。然后将这些多模态影像组学特征与临床和影像特征相结合，共同用于鉴别OAL和IOI。在五折交叉验证下，使用曲线下面积（AUC）评估具有不同特征的DL模型。采用Student t检验、卡方检验或Fisher精确检验对不同组进行比较。
在验证组中，使用联合特征的DL模型的诊断AUC为0.953（95%CI，0.895 - 1.000），高于使用多模态影像组学特征的DL模型（0.843，95%CI，0.786 - 0.898，p < 0.01）或仅使用临床和影像特征的DL模型（0.882，95%CI，0.782 - 0.982，p = 0.13）。基于多模态影像组学特征构建的DL模型优于基于大多数双模态和单模态构建的模型（p < 0.05）。此外，基于眼眶圆锥区域（覆盖眼眶肿物及其周围组织）的DL分析优于仅覆盖肿物区域的感兴趣区域（ROI）分析，尽管差异不显著（p = 0.33）。
基于DL的分析将多模态影像组学特征与临床和影像特征相结合，可能有助于鉴别OAL和IOI。
• 由于临床和影像表现存在重叠，OAL与IOI难以鉴别。• 影像组学已显示出对不同眼眶淋巴增殖性疾病进行无创诊断的潜力。• 基于DL的分析结合影像组学、影像和临床特征可能有助于鉴别OAL和IOI。
