ArticleScientific reports2020
Improved Prediction of Surgical Resectability in Patients with Glioblastoma using an Artificial Neural Network.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 35 citations in OpenAlex.
- Discrepancies in inter-rater agreement on resectability of recurrent glioblastoma: Complementary post-hoc analysis of the prospective, randomized DIRECTOR trial.Neuro-oncology · 2026Trial
- Enhancing decision-making in glioblastoma surgery through an explainable human-AI collaboration: an international multicenter model development and external validation study.NPJ precision oncology · 2025Article
- Artificial Intelligence in Glioblastoma-Transforming Diagnosis and Treatment.Chinese neurosurgical journal · 2025Review
- Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery.World neurosurgery: X · 2024Review
- Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients.Cancer medicine · 2024Article
- Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice.Frontiers in neurology · 2024Article
- Article
- Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review.Journal of Korean Neurosurgical Society · 2023Article
- Current and Future Advances in Surgical Therapy for Pituitary Adenoma.Endocrine reviews · 2023Article
- One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging.Life (Basel, Switzerland) · 2023Review
- Current Applications of Machine Learning for Spinal Cord Tumors.Life (Basel, Switzerland) · 2023Review
- Machine Learning Models for Predicting Postoperative Outcomes following Skull Base Meningioma Surgery.Journal of neurological surgery. Part B, Skull base · 2022Review
- Surgical data science - from concepts toward clinical translation.Medical image analysis · 2022Review
- Review
- Second surgery for progressive glioblastoma: a multi-centre questionnaire and cohort-based review of clinical decision-making and patient outcomes in current practice.Journal of neuro-oncology · 2021Article
- Neurosurgery and artificial intelligence.AIMS neuroscience · 2021Review
- Improved prediction of postoperative pediatric cerebellar mutism syndrome using an artificial neural network.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In managing a patient with glioblastoma (GBM), a surgeon must carefully consider whether sufficient tumour can be removed so that the patient can enjoy the benefits of decompression and cytoreduction, without impacting on the patient's neurological status. In a previous study we identified the five most important anatomical features on a pre-operative MRI that are predictive of surgical resectability and used them to develop a simple, objective, and reproducible grading system. The objective of this study was to apply an artificial neural network (ANN) to improve the prediction of surgical resectability in patients with GBM. Prospectively maintained databases were searched to identify adult patients with supratentorial GBM that underwent craniotomy and resection. Performance of the ANN was evaluated against logistic regression and the standard grading system by analysing their Receiver Operator Characteristic (ROC) curves; Area Under Curve (AUC) and accuracy were calculated and compared using Wilcoxon signed rank test with a value of p < 0.05 considered statistically significant. In all, 135 patients were included, of which 33 (24.4%) were found to have complete excision of all contrast-enhancing tumour. The AUC and accuracy were significantly greater using the ANN compared to the standard grading system (0.87 vs. 0.79 and 83% vs. 80% respectively; p < 0.01 in both cases). In conclusion, an ANN allows for the improved prediction of surgical resectability in patients with GBM.
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