Multiparametric MRI Radiomics for the Early Prediction of Response to Chemoradiotherapy in Patients With Postoperative Residual Gliomas: An Initial Study

Abstract

PurposeTo evaluate whether multiparametric magnetic resonance imaging (MRI)-based logistic regression models can facilitate the early prediction of chemoradiotherapy response in patients with residual brain gliomas after surgery.Patients and MethodsA total of 84 patients with residual gliomas after surgery from January 2015 to September 2020 who were treated with chemoradiotherapy were retrospectively enrolled and classified as treatment-sensitive or treatment-insensitive. These patients were divided into a training group (from institution 1, 57 patients) and a validation group (from institutions 2 and 3, 27 patients). All preoperative and postoperative MR images were obtained, including T1-weighted (T1-w), T2-weighted (T2-w), and contrast-enhanced T1-weighted (CET1-w) images. A total of 851 radiomics features were extracted from every imaging series. Feature selection was performed with univariate analysis or in combination with multivariate analysis. Then, four multivariable logistic regression models derived from T1-w, T2-w, CET1-w and Joint series (T1+T2+CET1-w) were constructed to predict the response of postoperative residual gliomas to chemoradiotherapy (sensitive or insensitive). These models were validated in the validation group. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were applied to compare the predictive performances of these models.ResultsFour models were created and showed the following areas under the ROC curves (AUCs) in the training and validation groups: Model-Joint series (AUC, 0.923 and 0.852), Model-T1 (AUC, 0.835 and 0.809), Model-T2 (AUC, 0.784 and 0.605), and Model-CET1 (AUC, 0.805 and 0.537). These results indicated that the Model-Joint series had the best performance in the validation group, followed by Model-T1, Model-T2 and finally Model-CET1. The calibration curves indicated good agreement between the Model-Joint series predictions and actual probabilities. Additionally, the DCA curves demonstrated that the Model-Joint series was clinically useful.ConclusionMultiparametric MRI-based radiomics models can potentially predict tumor response after chemoradiotherapy in patients with postoperative residual gliomas, which may aid clinical decision making, especially to help patients initially predicted to be treatment-insensitive avoid the toxicity of chemoradiotherapy.

Role of Neuro Navigation in Enhancing Surgical Precision: A Single-Center Experience – A Comprehensive Review Article

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“…This review endeavors to unravel the intricacies of Neuronavigation, tracing its evolutionary trajectory from its nascent stages to its contemporary applications, and casting a visionary gaze towards the future advancements in the field of neurosurgery. Through this narrative, the review aims to illuminate the complexities and multifaceted nature of Neuronavigation, underscoring its pivotal role in sculpting the future landscape of neurosurgical practice (5).…”

Predicting Tumour Response With Radiomics and Machine Learning in MR-Guided Cervix Brachytherapy

“…The MR imaging parameters for each sequence are provided in Table 2.1. T1 and T2 have been proven to offer predictive radiomic features for a variety of disease outcomes [94,95]. More recently, radiomic features extracted from high b-value diffusionweighted MR images have been shown to predict outcomes of osteosarcomas [62].…”

MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models

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