Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy

Abstract

Background We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI. Methods A total of 14 T4NxM0 NPC patients with histologically proven "in field" recurrence in the post nasal space following curative intent IMRT were included in this study. Pretreatment MRI were co-registered with MRI at the time of recurrence for the delineation of gross tumor volume at diagnosis(GTV) and at recurrence(GTVr). A total of 7 histogram features and 40 texture features were computed from the recurrent(GTVr) and non-recurrent region(GTV-GTVr). Paired ttests and Wilcoxon signed-rank tests were carried out on the 47 quantified radiomics features. Results A total of 7 features were significantly different between recurrent and non-recurrent regions. Other than the variance from intensity-based histogram, the remaining six significant features were either from the gray-level size zone matrix (GLSZM) or the neighbourhood gray-tone difference matrix (NGTDM). Conclusions The radiomic features extracted from pre-treatment MRI can potentially reflect the difference between recurrent and non-recurrent regions within a tumor and has a potential role in pre-treatment identification of intra-tumoral radio-resistance for selective dose escalation.

Predictive Value of a Combined Model Based on Pre-Treatment and Mid-Treatment MRI-Radiomics for Disease Progression or Death in Locally Advanced Nasopharyngeal Carcinoma

“…The Pearson correlation coefficient was used to remove redundant features. LASSO regression was used for a further selection of the remaining features, which is consistent with most previous studies ( 10 – 12 , 18 , 19 ). Then, 20 MRI-radiomics that were most closely related to PFS tags were selected, and the importance of features in the model was sorted.…”

“…Farhan et al. found significant differences between recurrent and non-recurrent regions in seven features (including GLSZM) in the radiomics analysis of intratumoral spatial heterogeneity in LA-NPC ( 19 ). GLDM quantifies the dependence between the gray values of adjacent pixels and the gray values of central pixels within a certain distance, and its predictive value in NPC had been confirmed by Zhang et al.…”

Radiomics for Diagnosis and Radiotherapy of Nasopharyngeal Carcinoma

“…In a study of PET-MRI combined with radiomics, Feng et al ( 26 ) developed a radiomics model of FDG PET-MRI and reported areas under the curve (AUC) of the training group based on T2-weighted imaging and PET models of 0.85 and 0.84, respectively, and those of the validation group of 0.83 and 0.82, respectively, which offers great promise for the clinical staging of NPCs. In terms of internal heterogeneity of tumors, Akram et al ( 27 ) showed that the imaging features Neighboring Gray Tone Difference Matrix-busyness extracted from MRI data before and after treatment may reflect differences between recurrent and non-recurrent areas in tumors; moreover, they demonstrated the potential of radiomics in the identification of radiation resistance in tumors before treatment to select dose increments.…”

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

“…In [ 99 ], the authors explored the issue of whether there was a difference between radiomic features derived from recurrent and non-recurrent regions within the tumour. Seven histogram features and 40 texture features were extracted from the MRI images of 14 patients with T4NxM0 NPC.…”