Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients

Abstract

The 1p/19q co-deletion status has been demonstrated to be a prognostic biomarker in lower grade glioma (LGG). The objective of this study was to build a magnetic resonance (MRI)-derived radiomics model to predict the 1p/19q co-deletion status. Method: 209 pathology-confirmed LGG patients from 2 different datasets from The Cancer Imaging Archive were retrospectively reviewed; one dataset with 159 patients as the training and discovery dataset and the other one with 50 patients as validation dataset. Radiomics features were extracted from T2-and T1-weighted post-contrast MRI resampled data using linear and cubic interpolation methods. For each of the voxel resampling methods a three-step approach was used for feature selection and a random forest (RF) classifier was trained on the training dataset. Model performance was evaluated on training and validation datasets and clinical utility indexes (CUIs) were computed. The distributions and intercorrelation for selected features were analyzed. Results: Seven radiomics features were selected from the cubic interpolated features and five from the linear interpolated features on the training dataset. The RF classifier showed similar performance for cubic and linear interpolation methods in the training dataset with accuracies of 0.81 (0.75− 0.86) and 0.76 (0.71− 0.82) respectively; in the validation dataset the accuracy dropped to 0.72 (0.6− 0.82) using cubic interpolation and 0.72 (0.6− 0.84) using linear resampling. CUIs showed the model achieved satisfactory negative values (0.605 using cubic interpolation and 0.569 for linear interpolation). Conclusions: MRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation.

Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas

“…Most of the 9 features were extracted from T1WI plain scan and Gadolinium enhanced sequences ( 33 ). The AUC of linear model based on SVM was 0.85 and 0.82 in the internal and external validation cohorts respectively, consistent with findings of other studies which predicted intracranial tumor biomarkers ( 31 , 32 ). There were many radiomics studies involving HER2 in breast cancers and IDH1 in gliomas ( 34 , 35 ).…”

“…Monti et al extracted quantitative radiomic features from DCE-MRI pharmacokinetic data to differentiate ER, PR, and HER2 status in breast cancer (30). In glioma, radiomics had been used to predict IDH mutation and co-deletion of 1p/19q (31,32). However, up to now, the report of such studies on meningiomas is scarce.…”

A review in radiomics: Making personalized medicine a reality via routine imaging

“…The possibility to use radiomics signatures to explore new medical conditions is expanding. Recently, a signature for the determination of chromosome deletion in low‐grade glioma (LGG) patients 149 has been reported. The authors developed two radiomics signatures, composed of seven and five features respectively, extracted from T2‐ and T1‐weighted post‐contrast MRI.…”

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics

“…Most of these characteristics were already described when the diagnosis of oligodendroglioma was determined morphologically without the addition of molecular alterations as is now mandatory. More recently, machine learning identified the codeletion, especially in low-grade tumors, with an accuracy of around 0.81 and AUC of 0.88–0.89 ( 147 – 149 ).…”