Article
Data-driven surgical decision-making – prediction of resectability in patients with glioblastoma using machine learning
Datengesteuerte chirurgische Entscheidungsfindung – Vorhersage der Resektabilität bei Glioblastompatienten mittels Machine Learning
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Published: | June 4, 2021 |
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Objective: Surgical decision-making in patients with glioblastoma (GBM) remains challenging as it aims at achieving maximal resection without causing neurological deterioration. Computational approaches leveraging novel artificial intelligence and machine-learning methods may enhance the decision-making process in a “data-driven” fashion – realizing a step towards personalized medicine.
Methods: We conducted a machine learning-based examination of multicentric data from four sites, including 480 patients with GBM (mean [SD] age 62 [12] years; 191 female) in total, of which 47% were found to have complete excision of the contrast-enhanced tumour area. Resection was classified as gross-total versus partial. Data were randomly split into a development set (80%) and a validation set (20%). The model selection included AdaBoost, GradientBoost, Logistic Regression, and Random Forest. L2-penalized Logistic Regression was selected as most competitive and hence evaluated on the validation set in 1000 bootstrap iterations.
Results: The overall accuracy of the learned predictive model was determined by comparing the predicted resectability with the actual rate of removal, which resulted in an area-under-the-curve (AUC, Figure 1 [Fig. 1]) value of 0.70 (95% CI 0.61- 0.79) on the validation set, confirming the generalizability of the approach. Precision and recall of 0.68 (95% CI 0.60-0.77) and 0.67 (0.58-0.75) were observed, respectively. Based on the coefficient weights of the predictive machine learning approach (Figure 2 [Fig. 2]), the use of intraoperative monitoring and the application of 5-ALA, positively impacted the rate of gross-total resection, while unfavorable locations, as well as overall tumor volume, had a negative impact.
Conclusion: The proposed machine learning-based framework allows for reliable prediction of gross-total resectability in patients with GBM and is hoped to complement and ease surgical decision-making, highlighting the benefits of intraoperative fluorescence and monitoring, as well as unfavorable topographical tumour patterns.