Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2025 Aug 6;48(1):591.
doi: 10.1007/s10143-025-03718-4.

Artificial intelligence algorithms for differentiating pseudoprogression from true progression in high-grade gliomas: A systematic review and meta-analysis

Lucca B Palavani  1 , Bernardo Vieira Nogueira  2 , Lucas Pari Mitre  3 , Hsien-Chung Chen  4   5 , Gean Carlo Müller  6 , Marina Vilardo  7 , Vinicius G Pereira  8 , Luis F Fabrini Paleare  9 , Filipe Virgilio Ribeiro  10 , Arthur Antônio Soutelo Araujo  11 , Marcio Yuri Ferreira  12 , Harivardhani Varre  13 , Christian Ferreira  14 , Wellingson Silva Paiva  15 , Raphael Bertani  15 , Randy S D Amico  12 , Iuri Santana Neville  15   16
Affiliations
Meta-Analysis

Artificial intelligence algorithms for differentiating pseudoprogression from true progression in high-grade gliomas: A systematic review and meta-analysis

Lucca B Palavani et al. Neurosurg Rev. .

Abstract

Differentiating pseudoprogression (PsP) from true progression (TP) in high-grade glioma (HGG) patients is still challenging and critical for effective treatment management. This meta-analysis evaluates the diagnostic accuracy of artificial intelligence (AI) algorithms in making this distinction. We aimed to assess the performance of AI algorithms in distinguishing between pseudoprogression and true progression in patients with high-grade glioma. We searched PubMed, Cochrane, and Embase databases for studies reporting on AI algorithms that differentiate pseudoprogression from true progression in high-grade gliomas. The analysis evaluated reported metrics such as accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. The meta-analysis included 26 articles involving 1,972 patients. In the high-grade glioma group, AI algorithms demonstrated a sensitivity of 88% (95% CI: 77%-100%) and a specificity of 75% (95% CI: 54%-97%). For the glioblastoma (GBM) group, accuracy was 77% (95% CI: 68%-86%), with sensitivity of 77% (95% CI: 67%-86%) and specificity of 63% (95% CI: 43%-82%). Overall, the algorithms achieved an accuracy of 80% (95% CI: 76%-85%), sensitivity of 85% (95% CI: 80%-91%), specificity of 69% (95% CI: 58%-80%), a PPV of 79% (95% CI: 58%-100%), a NPV of 97% (95% CI: 90%-100%), and an F1 score of 74% (95% CI: 67%-81%). AI algorithms show significant promise in accurately distinguishing between pseudoprogression and true progression in high-grade gliomas, suggesting their potential utility in clinical decision-making.

Keywords: Algorithms; Artificial intelligence; Glioblastoma; High-grade gliomas; Pseudoprogression; True progression.

PubMed Disclaimer

Conflict of interest statement

Declarations: No protocol registration. Ethics approval: Not applicable as this is a systematic review per PRISMA Guidelines. Consent to participate: Not relevant, as no individual patient data was used. Clinical trial number: Not applicable. Competing interests: The authors declare no competing interests. Consent for publication: All authors consent to publication, and this study has not been accepted for publication elsewhere. Disclosures: All authors report no relationships that could be construed as a conflict of interest. They also take responsibility for the reliability and freedom from bias of the data presented and their discussed interpretation.

Similar articles

See all similar articles

References

    1. Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 131(6):803–820. https://doi.org/10.1007/s00401-016-1545-1 - DOI - PubMed
    1. Alexander BM, Cloughesy TF (2017) Adult Glioblastoma. J Clin Oncol 35(21):2402–2409. https://doi.org/10.1200/JCO.2017.73.0119 - DOI - PubMed
    1. Radbruch A, Fladt J, Kickingereder P et al (2015) Pseudoprogression in patients with glioblastoma: clinical relevance despite low incidence. Neuro Oncol 17(1):151–159. https://doi.org/10.1093/neuonc/nou129 - DOI - PubMed
    1. Awuah WA, Adebusoye FT, Wellington J et al (2024) Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery. World Neurosurg X 23:100301. https://doi.org/10.1016/j.wnsx.2024.100301 - DOI - PubMed - PMC
    1. Kickingereder P, Burth S, Wick A et al (2016) Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. Radiology 280(3):880–889. https://doi.org/10.1148/radiol.2016160845 - DOI - PubMed

LinkOut - more resources