Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images

Abstract

Purpose To classify radiation necrosis versus recurrence in glioma patients using a radiomics model based on combinational features and multimodality MRI images. Methods Fifty-one glioma patients who underwent radiation treatments after surgery were enrolled in this study. Sixteen patients revealed radiation necrosis while 35 patients showed tumor recurrence during the follow-up period. After treatment, all patients underwent T1-weighted, T1-weighted postcontrast, T2-weighted, and fluid-attenuated inversion recovery scans. A total of 41,284 handcrafted and 24,576 deep features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (denoted as 0.632 + bootstrap AUC) metric were used to select the features. The stepwise forward method was applied to construct 10 logistic regression models based on different combinations of image features. Results For handcrafted features on multimodality MRI, model 7 with seven features yielded the highest AUC of 0.9624, sensitivity of 0.8497, and specificity of 0.9083 in the validation set. These values were higher than the accuracy of using handcrafted features on single-modality MRI (paired t-test, p < 0.05, except sensitivity). For combined handcrafted and AlexNet features on multimodality MRI, model 6 with six features achieved the highest AUC of 0.9982, sensitivity of 0.9941, and specificity of 0.9755 in the validation set. These values were higher than the accuracy of using handcrafted features on multimodality MRI (paired t-test, p < 0.05). Conclusions Handcrafted and deep features extracted from multimodality MRI images reflecting the heterogeneity of gliomas can provide useful information for glioma necrosis/recurrence classification.

A Radiomics Model for Predicting Early Recurrence in Grade II Gliomas Based on Preoperative Multiparametric Magnetic Resonance Imaging

“…The model performed well in both training and validation datasets with an AUC of 0.988 and 0.914, respectively. A similar model based on 51 glioma patients developed by Quan Zhang et al ( 19 ) achieved outstanding performance with an AUC of 0.962 following validation. However, to the best of our knowledge, this is the first multiparametric model developed to predict recurrence in LGG before surgery.…”

“…Mutations of the isocitrate dehydrogenase (IDH)1/2 genes are common events in gliomas ( 27 ), especially among grade II gliomas, where IDH1 mutations are observed in about 70% to 80% of cases ( 27 , 28 ). Some studies indicated that IDH1 mutation status could improve OS and PFS in grade II and III glioma ( 19 , 29 ). Although the IDH1 mutation has been identified as an independent positive prognostic biomarker for survival in patients with glioma ( 26 , 30 ), the association between the IDH mutant status and the risk of developing recurrence is still not clear.…”

“…An alternative method that can be used to assess tumor recurrence post-surgery is magnetic resonance imaging (MRI). Previous studies have shown that radiomics could be used to quantitatively extract and assess numerous imaging features to effectively differentiate between high and low-grade gliomas ( 17 , 18 ) and differentiate tumor recurrence from radiation necrosis ( 19 ). When combined with clinical data, imaging features could be used to assess the OS and hence optimize the treatment for the patient.…”

A Radiomics nomogram for preoperative predicting recurrence of low grade gliomas based on multiparametric MRI

“…Although histological analysis is the gold criteria for glioma grading, there is a main reason to illustrate that is not absolutely reliable: the tumor may contain high-grade lesions, pathological specimens resected during primary surgery could not completely re ect the nature of the whole tumor.Hence,relied on radiomics technology,we could extract numerous image parameters from the whole tumor region and quantitatively analyse the tumor properties.many previous studies have demonstrated that the application of radiomics technology can effectively differentiate high and low grade gliomas [17][18];Quan,Z et al [19]used 51 LGG patients and developed the radiomics model to differentiate recurrence from radiation necrosis in gliomas,these previous studies could provide reference for strategy determine.In our study,64 LGG cases were used to establish radiomics models and also yield an optimal performance.It is conducive to clinician to develop more accurate treatment plans and prognosis evaluation for patients with high risk of recurrence.…”

“…Studies have proven that some molecular alterations show great predictive and prognostic value in gliomas recurrence,numerous studies have shown that in grade II ~ III gliomas ,1p/19q chromosomal codeletion is associated with improved PFS and OS.Similarly, IDH mutations are a good prognostic marker for OS improvement in grade II ~ III glioma [19,24].However, it's not satisfying to predict the recurrence of LGG only based on genotype alone [15].More accurate predictions require us to develop.Therefore,base on the MRI technology and numerous image features,we developed a radiomics methods to discriminate the recurrence LGG and non-recurrence LGG.According to our study the combination model of multi-parametric MRI features yield a AUC of 0.966 in testing set and 0.93 in validation set,these results were obviously better than the individual MRI sequence model;in addition, the DCA showed that the net bene t of using a radiomics nomogram constructed on the basis of all three sequences was higher than any single sequence,it demonstrated that the nomogram we constructed is worthy of clinical use.…”