Use of artificial intelligence models for prognosis and survival prediction in brain tumor patients: a systematic review and meta-analysis
DOI:
https://doi.org/10.18203/2320-6012.ijrms20260349Keywords:
Brain tumor, Artificial intelligence, Prognosis, Machine learning, Deep learningAbstract
Brain tumors are associated with significant morbidity and mortality. Early prognostication of preoperative cases via AI models may improve treatment planning and clinical outcome. This systematic review and meta-analysis followed PRISMA 2020 guidelines. Literature review was conducted across various databases including PubMed, Web of Science, Cochrane etc. Out of the 433 papers obtained, 17 retrospective observational studies met the inclusion criteria. Risk of bias assessment was carried out using PROBAST+AI tool. Meta-analysis was carried out for the reporting area under the curve (AUC) or C-index for survival prediction models. With a total of 98,464 observations across 17 studies, machine learning (ML) and deep learning (DL) models were used to predict survival and prognosis in brain tumor patients. The risk of bias was low in 6% studies, moderate in 59% studies and high in 35% of the studies. The pooled AUC was 0.87 (SE: 0.04) with a 95% prediction interval between studies ranging between 0.61 and 1.13. Cochran’s Q statistic for heterogeneity was 48.81 (p<0.001). Subgroup analyses showed pooled AUCs of 0.92 for ML models and 0.81 for DL models. Significant publication bias was demonstrated by Funnel plot and Egger’s test. This systematic review and meta-analysis are the first to include multiple brain tumor types for predicting prognosis via AI models, with ML models showing slightly higher pooled performance than DL models. However, variability in datasets, limited external validation, and high heterogeneity among studies highlight the need for standardization and further research.
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References
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK. Overview of primary brain tumors: pathologic classification, epidemiology, molecular biology, and prognostic markers. Hematol Oncol Clin North Am. 2012;26(4):715-32. DOI: https://doi.org/10.1016/j.hoc.2012.05.004
Nejo T, Mende A, Okada H. The current state of immunotherapy for primary and secondary brain tumors: similarities and differences. Jpn J Clin Oncol. 2020; 50(11):1231-45. DOI: https://doi.org/10.1093/jjco/hyaa164
Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2021;142(1):11-28. DOI: https://doi.org/10.1093/neuonc/noab106
Global Burden of Disease Cancer Collaboration. Global, regional, and national burden of brain and other CNS cancers, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Front Neurol. 2022;13:950725. DOI: https://doi.org/10.3389/fneur.2022.955367
Ostrom QT, Cioffi G, Gittleman H. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2015-2019. Neuro Oncol. 2022;24(5):v1-95. DOI: https://doi.org/10.1093/neuonc/noac202
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-49. DOI: https://doi.org/10.3322/caac.21660
Ostrom QT, Patil N, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS. CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2013-2017. Neuro Oncol. 2020;22(2):iv1-96. DOI: https://doi.org/10.1093/neuonc/noaa200
Nair M, Varghese C, Swaminathan R. Cancer: Current Scenario, Intervention Strategies and Projections for 2015. NCMH Background Papers. 2015.
Yeole BB. Trends in the brain cancer incidence in India. Asian Pac J Cancer Prev. 2008;9:267-70.
Mayo Clinic. Pituitary tumors-Symptoms and causes. Mayo Clinic; 2023. Available at: https://www.mayoclinic.org/diseases-conditions/pituitary-tumors/symptoms-causes/syc-20350548. Accessed on 15 November 2025.
Darnell RB, Posner JB. Paraneoplastic syndromes involving the nervous system. N Engl J Med. 2003;349(16):1543-1554. DOI: https://doi.org/10.1056/NEJMra023009
American Academy of Family Physicians. Primary brain tumors in adults. Am Fam Physician. 2008;77(10):1423-30.
Rahman AMJZ, Gupta M, Aarathi S, Mahesh TR, Vinoth Kumar V, Yogesh Kumaran S, et al. Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering. BMC Med Inform Decis Mak. 2024;24:113. DOI: https://doi.org/10.1186/s12911-024-02519-x
Rauschenbach L, Kolbe P, Engel A, Ahmadipour Y, Oppong MD, Santos AN, et al. Predictors and surgical outcome of hemorrhagic metastatic brain malignancies. J Neurooncol. 2024;169(1):165-73. DOI: https://doi.org/10.1007/s11060-024-04714-2
Cacho-Díaz B, Lorenzana-Mendoza NA, Chávez-Hernandez JD, González-Aguilar A, Reyes-Soto G, Herrera-Gómez Á. Clinical manifestations and location of brain metastases as prognostic markers. Curr Probl Cancer. 2019;43(4):312-23. DOI: https://doi.org/10.1016/j.currproblcancer.2018.06.002
Cagney DN, Martin AM, Catalano PJ, Redig AJ, Lin NU, Lee EQ, et al. Incidence and prognosis of patients with brain metastases at diagnosis of systemic malignancy: a population-based study. Neurooncol. 2017;19(11):1511-21. DOI: https://doi.org/10.1093/neuonc/nox077
Arvanitis CD, Ferraro GB, Jain RK. The blood–brain barrier and blood–tumour barrier in brain tumours and metastases. Nat Rev Cancer. 2020;20(1):26-41. DOI: https://doi.org/10.1038/s41568-019-0205-x
Mustaf M, Sali AF, Illzam EM, Sharifa AM, Nang MK. Brain cancer: Current concepts, diagnosis and prognosis. IOSR J Dent Med Sci. 2018;17(3):41-6.
Michel A, Dinger TF, Santos AN, Pierscianek D, Darkwah Oppong M, Ahmadipour Y, et al. Time interval between the diagnosis of breast cancer and brain metastases impacts prognosis after metastasis surgery. J Neurooncol. 2022;159(1):53-63. DOI: https://doi.org/10.1007/s11060-022-04043-2
GBD 2016 Brain and Other CNS Cancer Collaborators. Global, regional, and national burden of brain and other CNS cancer, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(4):376-93. DOI: https://doi.org/10.1016/S1474-4422(18)30468-X
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. DOI: https://doi.org/10.1136/bmj.n71
Chen H, Li C, Zheng L, Lu W, Li Y, Wei Q. A machine learning-based survival prediction model of high grade glioma by integration of clinical and dose-volume histogram parameters. Cancer Med. 2021;10(8):2774-86. DOI: https://doi.org/10.1002/cam4.3838
Wei J, Yang G, Hao X, Gu D, Tan Y, Wang X, et al. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication. Eur Radiol. 2019;29(2):877-88. DOI: https://doi.org/10.1007/s00330-018-5575-z
Li Y, Bao L, Yang C, Deng Z, Zhang X, Xu P, et al. A multiparameter radiomic model for accurate prognostic prediction of glioma. MedComm-Future Med. 2023;2(2):e41. DOI: https://doi.org/10.1002/mef2.41
Liu X, Li Y, Qian Z, Sun Z, Xu K, Wang K, et al. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. NeuroImage Clin. 2018;20:1070-7. DOI: https://doi.org/10.1016/j.nicl.2018.10.014
Nath G, Coursey A, Li Y, Prabhu S, Garg H, Halder SC, et al. An interactive web-based tool for predicting and exploring brain cancer survivability. Healthc Anal. 2023;3:100132. DOI: https://doi.org/10.1016/j.health.2022.100132
Musigmann M, Akkurt BH, Krähling H, Brokinkel B, Spille DC, Stummer W, et al. Analysis of the Predictability of Postoperative Meningioma Resection Status Based on Clinical Features. Cancers. 2024;16:22. DOI: https://doi.org/10.3390/cancers16223751
She Z, Marzullo A, Destito M, Spadea MF, Leone R, Anzalone N, et al. Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma. Int J Comput Assist Radiol Surg. 2023;18(10):1849-56. DOI: https://doi.org/10.1007/s11548-023-02886-2
Marcus AP, Marcus HJ, Camp SJ, Nandi D, Kitchen N, Thorne L. Improved Prediction of Surgical Resectability in Patients with Glioblastoma using an Artificial Neural Network. Sci Rep. 2020;10(1):5143. DOI: https://doi.org/10.1038/s41598-020-62160-2
Link KE, Schnurman Z, Liu C, Kwon YJF, Jiang LY, Nasir-Moin M, et al. Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark. Nat Commun. 2024;15(1):8170. DOI: https://doi.org/10.1038/s41467-024-52414-2
Pan ZQ, Zhang SJ, Wang XL, Jiao YX, Qiu JJ. Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma. Behav Neurol. 2020;2020(1):1712604. DOI: https://doi.org/10.1155/2020/1712604
McGarry SD, Hurrell SL, Kaczmarowski AL, Cochran EJ, Connelly J, Rand SD, et al. Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy. Tomography. 2016;2(3):223-8. DOI: https://doi.org/10.18383/j.tom.2016.00250
Nie D, Lu J, Zhang H, Adeli E, Wang J, Yu Z, et al. Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages. Sci Rep. 2019;9:1103. DOI: https://doi.org/10.1038/s41598-018-37387-9
Baid U, Rane SU, Talbar S, Gupta S, Thakur MH, Moiyadi A, et al. Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning. Front Comput Neurosci. 2020;14:61. DOI: https://doi.org/10.3389/fncom.2020.00061
Shaheen A, Bukhari ST, Nadeem M, Burigat S, Bagci U, Mohy-ud-Din H. Overall Survival Prediction of Glioma Patients With Multiregional Radiomics. Front Neurosci. 2022;16:911065. DOI: https://doi.org/10.3389/fnins.2022.911065
Wahed SA, Wahed MA. Predicting Post-Surgical Complications using Machine Learning Models for Patients with Brain Tumors. Int J Open Inf Technol. 2025;13(4):43-8.
Beig N, Patel J, Prasanna P, Hill V, Gupta A, Correa R, et al. Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma. Sci Rep. 2018;8(1):7. DOI: https://doi.org/10.1038/s41598-017-18310-0
Weninger L, Haarburger C, Merhof D. Robustness of Radiomics for Survival Prediction of Brain Tumor Patients Depending on Resection Status. Front Comput Neurosci. 2019;13:73. DOI: https://doi.org/10.3389/fncom.2019.00073
Moons KGM, Damen JAA, Kaul T, Hooft L, Navarro CA, Dhiman P, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025;388:e082505. DOI: https://doi.org/10.1136/bmj-2024-082505
Suurmond R, van Rhee H, Hak T. Introduction, comparison, and validation of Meta-Essentials: A free and simple tool for meta-analysis. Res Synth Methods. 2017;8(4):537-53. DOI: https://doi.org/10.1002/jrsm.1260
Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455-63. DOI: https://doi.org/10.1111/j.0006-341X.2000.00455.x
Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-34. DOI: https://doi.org/10.1136/bmj.315.7109.629
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088-101. DOI: https://doi.org/10.2307/2533446
Rosenthal R. The file drawer problem and tolerance for null results. Psychol Bull. 1979;86(3):638-41. DOI: https://doi.org/10.1037/0033-2909.86.3.638
McGuinnes LA. “robvis: An R package and web application for visualizing risk of bias assessments. 2019.
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