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Modelling MR and clinical features in grade II/III astrocytomas to predict IDH mutation status
Harpreet Hyare1, Louise Rice2, Stefanie Thust3
1Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK; Imaging Department, UCLH NHS Trust, London, UK.
Background And Purpose:
There is increasing evidence that many IDH wildtype (IDHwt) astrocytomas have a poor prognosis and although MR features have been identified, there remains diagnostic uncertainty in the clinic. We have therefore conducted a comprehensive analysis of conventional MR features of IDHwt astrocytomas and performed a Bayesian logistic regression model to identify critical radiological and basic clinical features that can predict IDH mutation status.
Materials And Methods:
146 patients comprising 52 IDHwt astrocytomas (19 WHO Grade II diffuse astrocytomas (A II) and 33 WHO Grade III anaplastic astrocytomas (A III)), 68 IDHmut astrocytomas (53 A II and 15 A III) and 26 GBM were studied. Age, sex, presenting symptoms and Overall Survival were recorded. Two neuroradiologists assessed 23 VASARI imaging descriptors of MRI features and the relation between IDH mutation status and MR and basic clinical features was modelled by Bayesian logistic regression, and survival by Kaplan-Meier plots.
Results:
The features of greatest predictive power for IDH mutation status were, age at presentation (OR = 0.94 +/-0.03), tumour location within the thalamus (OR = 0.15 +/-0.25), involvement of speech receptive areas (OR = 0.21 +/-0.26), deep white matter invasion of the brainstem (OR = 0.10 +/-0.32), and T1/FLAIR signal ratio (OR = 1.63 +/-0.64). A logistic regression model based on these five features demonstrated excellent out-of-sample predictive performance (AUC = 0.92 +/-0.07; balanced accuracy 0.81 +/-0.09). Stepwise addition of further VASARI variables did not improve performance.
Conclusion:
Five demographic and VASARI features enable excellent individual prediction ofIDH mutation status, opening the way to identifying patients with IDHwt astrocytomas for earlier tissue diagnosis and more aggressive management.
Insights
Identifying isocitrate dehydrogenase wildtype (IDHwt) astrocytomas is crucial for prognosis. Five key MRI and clinical features accurately predict IDH mutation status, aiding in early diagnosis and treatment.
Area of Science:
- Neuro-oncology
- Radiology
- Genetics
Background:
- Isocitrate dehydrogenase wildtype (IDHwt) astrocytomas often present with poor prognosis.
- Diagnostic uncertainty persists despite identified MRI features.
- Predicting IDH mutation status is critical for patient management.
Purpose of the Study:
- To comprehensively analyze conventional MRI features of IDHwt astrocytomas.
- To develop a Bayesian logistic regression model for predicting IDH mutation status.
- To identify critical radiological and clinical features for predicting IDH status.
Main Methods:
- Analysis of 146 patients: 52 IDHwt astrocytomas, 68 IDHmut astrocytomas, and 26 GBM.
- Recording of age, sex, symptoms, and overall survival.
- Assessment of 23 VASARI imaging descriptors by two neuroradiologists; Bayesian logistic regression and Kaplan-Meier survival analysis.
Main Results:
- Key predictive features for IDH mutation status include age, thalamic location, speech area involvement, brainstem invasion, and T1/FLAIR signal ratio.
- A logistic regression model using these five features achieved excellent predictive performance (AUC = 0.92 +/- 0.07).
- No significant improvement in prediction was observed with additional VASARI variables.
Conclusions:
- Five demographic and VASARI features accurately predict IDH mutation status.
- This predictive capability aids in identifying patients with IDHwt astrocytomas.
- Early identification facilitates timely tissue diagnosis and more aggressive management strategies.
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