Clinical Scorecard: Predictive Models Utilizing Radiomics and Clinical Information for Assessing Pseudoprogression Following Radiotherapy in High-Grade Glioma

At a Glance

Key Highlights

• Developed a multivariable model for individualized pseudoprogression risk estimation
• Integrated model achieved an AUC of 0.811 for predicting pseudoprogression
• Model combines radiomic, clinical, molecular, and treatment-related information
• Pseudoprogression can mimic true tumor progression on MRI, complicating diagnosis
• Study emphasizes the need for external validation with standardized imaging protocols

Guideline-Based Recommendations

Diagnosis

• Utilize a combination of imaging and clinical data for accurate diagnosis of pseudoprogression

Management

• Avoid unnecessary surgery or treatment escalation based on misclassification of pseudoprogression

Monitoring & Follow-up

• Follow-up imaging should be conducted at least 6 months post-radiotherapy for accurate assessment

Risks

• Misclassification of pseudoprogression as true progression may lead to inappropriate treatment decisions

Patient & Prescribing Data

222 patients with high-grade glioma who underwent surgery and radiotherapy

Incorporation of temozolomide (TMZ) treatment data into risk estimation models

Clinical Best Practices

• Implement a multidisciplinary review for adjudicating pseudoprogression cases
• Use radiomics as a complementary tool alongside conventional imaging techniques
• Ensure adequate follow-up imaging for accurate assessment of treatment response

Related Resources & Content

• World Health Organization Classification of Tumours