Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI

Objectives To evaluate the effectiveness of bag-of-features (BOF)-based radiomics for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI) from contrast-enhanced MRI (CE-MRI). Methods Fifty-six patients with pathologically confirmed IOI (28 patients) and OAL (28 patients) were randomly divided into training ( n = 42) and testing ( n = 14) groups. One hundred sixty texture features extracted from the CE-MR image were encoded into the BOF representation with fewer features. The support vector machine (SVM) with a linear kernel was used as the classifier. Data augmented was performed by cropping orbital lesions in different directions to alleviate the over-fitting problem. Student’s t test and the Holm-Bonferroni method were employed to compare the performance of different analysis methods. The chi-square test was used to compare the analysis with MRI and human radiological diagnosis. Results In the independent testing group, the differentiation by the BOF features with augmentation achieved an area under the curve (AUC) of 0.803 (95% CI: 0.725–0.880), which was significantly higher than that of the BOF features without augmentation and that of the texture features ( p < 0.05). In addition, the same radiomic analysis with pre-contrast MRI obtained an AUC of 0.618 (95% CI: 0.560–0.677), which was significantly lower than that with CE-MRI. The diagnostic performance of the analysis with CE-MRI was significantly better than the radiology resident ( p < 0.05) but had no significant difference with the experienced radiologist, even though there was less consistency between the radiomic analysis and the human visual diagnosis. Conclusions The BOF-based radiomics may be helpful for the differentiation between OAL and IOI. Key Points • It is challenging to differentiate OAL from IOI due to the similar clinical and image features. • Radiomics has great potential for the noninvasive diagnosis of orbital diseases. • The BOF representation from patch to image may help the differentiation of OAL and IOI.