Deep learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma and idiopathic orbital inflammation.

Abstract

Objectives: To evaluate the value of deep-learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).

Methods: Nighty-seven patients with histopathologically confirmed OAL (n = 43) and IOI (n = 54) were randomly divided into training (n = 79) and test (n = 18) groups. DL-based intratumoral and peritumoral features were extracted to characterize the differences in heterogeneity and tissue invasion between different lesions, respectively. Subsequently, an attention-based fusion model was employed to fuse the features extracted from intra- and peritumoral regions and multiple MR sequences. A comprehensive comparison was conducted among different methods for extracting intratumoral, peritumoral, and fused features. Area under the curve (AUC) was used to evaluate the performance under a 10-fold cross-validation and independent test. Chi-square and student's t-test were used to compare discrete and continuous variables, respectively.

Results: Fused intra-peritumoral features achieved AUC values of 0.870-0.930 and 0.849-0.924 on individual MR sequences in the validation and test sets, respectively. This was significantly higher than those using intratumoral features (p < 0.05), but not significantly different than those using peritumoral features (p > 0.05). By combining multiple MR sequences, AUC values of the intra-peritumoral features were boosted to 0.943 and 0.940, higher than those obtained from each sequence alone. Moreover, intra-peritumoral features yielded higher AUC values compared to entire orbital cone features extracted by either the intra- or the peritumoral DL model, although no significant difference was found from the latter (p > 0.05).

Conclusion: DL-based intratumoral, peritumoral, and especially fused intra-peritumoral features may help differentiate between OAL and IOI.

Key points: Question What is the diagnostic value of the peritumoral region and its combination with the intratumoral region for radiomic analysis of orbital lymphoproliferative disorders? Findings Fused intra- and peritumoral features achieved significantly higher performance than intratumoral features, but had no significant difference to the peritumoral features. Clinical relevance Peritumoral contextual features, which characterize the invasion patterns of orbital lesions within the surrounding areas of the entire orbital cone, might serve as an independent imaging marker for differentiating between OAL and IOI.