Radiogenomics prediction for MYCN amplification in neuroblastoma: A hypothesis generating study

Abstract

Background: MYCN amplification represents a powerful prognostic factor in neuroblastoma (NB) and may occasionally account for intratumoral heterogeneity. Radiomics is an emerging field of advanced image analysis that aims to extract a large number of quantitative features from standard radiological images, providing valuable clinical information.Procedure: In this retrospective study, we aimed to create a radiogenomics model by correlating computed tomography (CT) radiomics analysis with MYCN status. NB lesions were segmented on pretherapy CT scans and radiomics features subsequently extracted using a dedicated library. Dimensionality reduction/features selection approaches were then used for features procession and logistic regression models have been developed for the considered outcome.Results: Seventy-eight patients were included in this study, as training dataset, of which 24 presented MYCN amplification. In total, 232 radiomics features were extracted. Eight features were selected through Boruta algorithm and two features were lastly chosen through Pearson correlation analysis: mean of voxel intensity histogram (p = .0082) and zone size non-uniformity (p = .038). Five-times repeated threefold cross-validation logistic regression models yielded an area under the curve (AUC) value of 0.879 on the training set. The model was then applied to an independent validation cohort of 21 patients, of which five presented MYCN amplification. The validation of the model yielded a 0.813 AUC value, with 0.85 accuracy on previously unseen data.Conclusions: CT-based radiomics is able to predict MYCN amplification status in NB, paving the way to the in-depth analysis of imaging based biomarkers that could enhance outcomes prediction.

Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics

“…Previous studies have reported radiomics potential role to predict molecular biomarkers in neuroblastoma, including MYCN in neuroblastoma by CT-based radiomics signature [ 8 , 25 ], tumor-associated macrophages by contrast-enhanced CT [ 26 ]. Moreover, some studies demonstrated that combining radiomics features with clinical characteristics can provide incremental predictive value for gene mutant status and expression.…”

Development and validation of a CT‐based radiomics signature for identifying high‐risk neuroblastomas under the revised Children's Oncology Group classification system

“…There have been some studies on radiomics in the pathological differentiation, molecular typing, and prediction of therapeutic effect of neuroblastomas. [9][10][11][12] These studies showed that radiomics has the potential for the accurate diagnosis of neuroblastomas. Therefore, radiomics is expected to provide a noninvasive evaluation method for risk stratification of neuroblastomas.…”

“…Thus, medical images are not limited to providing subjective visual information. There have been some studies on radiomics in the pathological differentiation, molecular typing, and prediction of therapeutic effect of neuroblastomas 9–12 . These studies showed that radiomics has the potential for the accurate diagnosis of neuroblastomas.…”

A review of radiomics and genomics applications in cancers: the way towards precision medicine

“…The literature on radiogenomics has greatly expanded in the past decade. Radiogenomics has been applied in various types of cancer, including lung cancer [70][71][72], breast cancer [73][74][75], genitourinary cancer [76][77][78], digestive system cancer [79][80][81], and brain cancer [82][83][84]. The comprehensive analysis method of radiogenomics can be used to diagnose and predict patient outcomes such as recurrence, metastasis, and survival of patients with cancer; predict the tumour microenvironment; identify potential molecular biomarkers and targets for clinical diagnosis and treatment; and establish a molecular typing system of variable cancers.…”