Clinical factors and conventional MRI may independently predict progression-free survival and overall survival in adult pilocytic astrocytomas

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 2-Summary of Imaging Findings on Pediatric-Type Diffuse High-Grade Gliomas, Pediatric-Type Diffuse Low-Grade Gliomas, and Circumscribed Astrocytic Gliomas

The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. This Part 2 review focuses on the molecular diagnostics and imaging findings of pediatric-type diffuse high-grade gliomas, pediatric-type diffuse low-grade gliomas, and circumscribed astrocytic gliomas. Each tumor type in pediatric-type diffuse high-grade glioma mostly harbors a distinct molecular marker. On the other hand, in pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas, molecular diagnostics may be extremely complicated at a glance in the 2021 WHO classification. It is crucial for radiologists to understand the molecular diagnostics and imaging findings and leverage the knowledge in clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Comparison of the clinical features and treatment outcomes of pilocytic astrocytoma in pediatric and adult patients

PURPOSE

Pilocytic astrocytoma is a slow-growing tumor that predominantly develops in children, but has a broad age spectrum. A notable characteristic of pilocytic astrocytoma is that the tumor arises in diverse locations and the clinical course is not always benign. Therefore, it is necessary to elucidate the clinical spectrum of the disease and analyze the relevant prognostic factors.

METHODS

Demographic and treatment-related factors were retrospectively reviewed in a cohort of 254 patients with histologically confirmed pilocytic astrocytoma. Clinical features were compared between the pediatric group (N = 208; age < 18 years) and the adult group (N = 46; age ≥ 18 years). Cox regression analysis was performed to identify relevant prognostic factors.

RESULTS

There was no difference in progression-free survival (PFS) between the pediatric and adult groups (p = 0.36); however, patients under 8 years of age exhibited worse PFS (p < 0.01). Leptomeningeal seeding at diagnosis and pilomyxoid histology was observed only in pediatric patients. In the pediatric group, nine patients experienced recurrence after complete resection. Increasing age (hazard ratio (HR) = 0.89, p < 0.01) and adjuvant therapy (HR = 0.32, p < 0.01) were protective factors against tumor progression. In the adult group, no progression occurred after complete resection. Age and adjuvant therapy were not significant factors in the adult group.

CONCLUSION

Pilocytic astrocytoma presents with a diverse clinical spectrum. Complete resection is of utmost importance, and appropriate adjuvant treatment is recommended if complete resection cannot be achieved. Children with younger age are associated with more aggressive tumors, and recurrence may occur even after complete resection.

A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from glioblastomas

OBJECTIVES

To develop a fully automatic radiomics model to differentiate adult pilocytic astrocytomas (PA) from high-grade gliomas (HGGs).

METHODS

This retrospective study included 302 adult patients with PA (n = 62) or HGG (n = 240). The patients were randomly divided into training (n = 211) and test (n = 91) sets. Clinical data were obtained, and radiomic features (n = 372) were extracted from multiparametric MRI with automatic tumour segmentation. After feature selection with F-score, a Light Gradient Boosting Machine classifier with subsampling was trained to develop three models: (1) clinical model, (2) radiomics model, and (3) combined clinical and radiomics model. Human performance was also assessed. The performance of the classifier was validated in the test set. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model.

RESULTS

A total of 15 radiomic features were selected. In the test set, the combined clinical and radiomics model (area under the curve [AUC], 0.93) showed a significantly higher performance than the clinical model (AUC, 0.79, p = 0.037) and had a similar performance to the radiomics model (AUC, 0.92, p = 0.828). The combined clinical and radiomics model also showed a significantly higher performance than humans (AUC, 0.76-0.81, p < 0.05). The model explanation by SHAP suggested that lower intratumoural heterogeneity from T2-weighted images was highly associated with PA diagnosis.

CONCLUSIONS

The fully automatic combined clinical and radiomics model may be helpful for differentiating adult PAs from HGGs.

KEY POINTS

• Differentiating adult PAs from HGGs is challenging because PAs may manifest a large spectrum of imaging presentations, often including aggressive imaging features. • The fully automatic combined clinical and radiomics model showed a significantly higher performance than the clinical model or humans. • The model explanation by SHAP suggested that second-order features from T2-weighted imaging were important in diagnosis and might reflect the underlying pathophysiology that PAs have lesser tissue heterogeneity than HGGs.