Diagnostic Accuracy of Radiomics and Deep Learning for Differentiating Pseudoprogression from True Tumour Recurrence in Glioblastoma: A Systematic Review and Meta-analysis

Isabela Sacco Camara; Aline Pelosini Gomes; Olívia Voelzke Passarin; A.S. Mendes; Kalil Sallum Haddad Dib; Juliano Dos Santos

doi:10.9734/irjo/2026/v9i2213

June 4, 2026

Diagnostic Accuracy of Radiomics and Deep Learning for Differentiating Pseudoprogression from True Tumour Recurrence in Glioblastoma: A Systematic Review and Meta-analysis

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Key Points

To assess the diagnostic accuracy of radiomics and deep learning techniques in differentiating pseudoprogression from true tumour recurrence in glioblastoma.
Conducted a systematic review and meta-analysis of studies focused on radiomics and deep learning methods.
Included studies that evaluated diagnostic performance in glioblastoma patients.
Analyzed sensitivity, specificity, and overall accuracy across identified studies.
Radiomics demonstrated a sensitivity of 85% (95% CI, 80-90) for distinguishing pseudoprogression from recurrence.
Deep learning models achieved an overall accuracy of 88% (95% CI, 83-93) in detecting true tumour recurrence.
Findings suggest a significant improvement in diagnostic capabilities compared to traditional imaging methods.

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Implication

Systematic review and meta-analysis reveals diagnostic accuracy of radiomics and deep learning for distinguishing pseudoprogression from true tumour recurrence in glioblastoma, suggesting improved...

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Camara et al. (2026) studied this question.

synapsesocial.com/papers/6a265b55ad53cfb9357c4f81 https://doi.org/10.9734/irjo/2026/v9i2213

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Diagnostic Accuracy of Radiomics and Deep Learning for Differentiating Pseudoprogression from True Tumour Recurrence in Glioblastoma: A Systematic Review and Meta-analysis

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Diagnostic Accuracy of Radiomics and Deep Learning for Differentiating Pseudoprogression from True Tumour Recurrence in Glioblastoma: A Systematic Review and Meta-analysis

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