Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma

Abstract

Background and Objective. Although radiotherapy has become one of the main treatment methods for cancer, there is no noninvasive method to predict the radiotherapeutic response of individual glioblastoma (GBM) patients before surgery. The purpose of this study is to develop and validate a machine learning-based radiomics signature to predict the radiotherapeutic response of GBM patients. Methods. The MRI images, genetic data, and clinical data of 152 patients with GBM were analyzed. 122 patients from the TCIA dataset (training set: n = 82 ; validation set: n = 40 ) and 30 patients from local hospitals were used as an independent test dataset. Radiomics features were extracted from multiple regions of multiparameter MRI. Kaplan-Meier survival analysis was used to verify the ability of the imaging signature to predict the response of GBM patients to radiotherapy before an operation. Multivariate Cox regression including radiomics signature and preoperative clinical risk factors was used to further improve the ability to predict the overall survival (OS) of individual GBM patients, which was presented in the form of a nomogram. Results. The radiomics signature was built by eight selected features. The C -index of the radiomics signature in the TCIA and independent test cohorts was 0.703 ( P < 0.001 ) and 0.757 ( P = 0.001 ), respectively. Multivariate Cox regression analysis confirmed that the radiomics signature (HR: 0.290, P < 0.001 ), age (HR: 1.023, P = 0.01 ), and KPS (HR: 0.968, P < 0.001 ) were independent risk factors for OS in GBM patients before surgery. When the radiomics signature and preoperative clinical risk factors were combined, the radiomics nomogram further improved the performance of OS prediction in individual patients ( C ‐ index = 0.764 and 0.758 in the TCIA and test cohorts, respectively). Conclusion. This study developed a radiomics signature that can predict the response of individual GBM patients to radiotherapy and may be a new supplement for precise GBM radiotherapy.

RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas

“…The patients received partial ( n = 86) or gross total ( n = 90) resection followed by chemotherapy and radiation. Karnofsky Performance Status (KPS) scores (binary, score >70 or ≤70) [ 5 , 33 ], as a strong independent predictor of clinical outcome [ 34 ] determined by postoperative treatment, were retrieved from their electronic medical records. The demographic and clinical information are shown in Table 1 .…”

Treatment Response and Prognosis Evaluation in High‐Grade Glioma: An Imaging Review Based on MRI

“…55 The high-dimensional quantitative imaging features extracted by radiomics not only reflects the macroscopic characteristics of tumor tissues, but also the molecular characteristics, and has great prospects in HGG treatment monitoring. 56 Some studies only analysis of heterogeneity in T2WI, combined with the ML algorithm, to distinguish between GBM progress and PsP; the accuracy of the training set reached 0.88, and the AUC was 0.9, the sensitivity, specificity, accuracy of the testing set were 100.0%, 67.0%, and 86.0%, respectively. 57 When T1C and T2/Flair were combined to quantify three-dimensional shape features, and the SVM classifier was used to distinguish between progress and PsP, the accuracy of the training and test sets were 91.5% and 90.2%, respectively.…”

“…The evaluation/prediction model constructed by combining MRI and ML may be an effective tool to assist clinical decision‐making in follow‐up treatment of HGG patients 55 . The high‐dimensional quantitative imaging features extracted by radiomics not only reflects the macroscopic characteristics of tumor tissues, but also the molecular characteristics, and has great prospects in HGG treatment monitoring 56 . Some studies only analysis of heterogeneity in T2WI, combined with the ML algorithm, to distinguish between GBM progress and PsP; the accuracy of the training set reached 0.88, and the AUC was 0.9, the sensitivity, specificity, accuracy of the testing set were 100.0%, 67.0%, and 86.0%, respectively 57 .…”

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges

“…Moreover, some studies have reported the accuracy of AI in predicting glioma prognosis. The cancer imaging archive (TCIA) and local test cohorts were used by Pan et al to predict the OS using ML techniques with C-indexes of 0.70 and 0.76, respectively, for multiparameter MRI of 152 GBMs (53). When radiomic characteristics were paired with preoperative clinical risk factors (C-index = 0.76 in the TCIA and test cohort), the impact of OS prediction was substantially enhanced.…”