Machine Learning-Based Prediction of Early Recurrence in Glioblastoma Patients: A Glance Towards Precision Medicine. Issue 5 (30th August 2021)

• Record Type: Journal Article
• Authors: Della Pepa, Giuseppe Maria; Caccavella, Valerio Maria; Menna, Grazia; Ius, Tamara; Auricchio, Anna Maria; Sabatino, Giovanni; La Rocca, Giuseppe; Chiesa, Silvia; Gaudino, Simona; Marchese, Enrico; Olivi, Alessandro
• Is Part Of: Neurosurgery. Volume 89:Issue 5(2021)
• Page Start: 873
• Page End: 883
• Publication Date: 2021-08-30
• Subjects: Glioblastoma -- Machine learning -- Progression free survival -- Precision medicine; Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1093/neuros/nyab320
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract: BACKGROUND: Ability to thrive and time-to-recurrence following treatment are important parameters to assess in patients with glioblastoma multiforme (GBM), given its dismal prognosis. Though there is an ongoing debate whether it can be considered an appropriate surrogate endpoint for overall survival in clinical trials, progression-free survival (PFS) is routinely used for clinical decision-making. OBJECTIVE: To investigate whether machine learning (ML)-based models can reliably stratify newly diagnosed GBM patients into prognostic subclasses on PFS basis, identifying those at higher risk for an early recurrence (≤6 mo). METHODS: Data were extracted from a multicentric database, according to the following eligibility criteria: histopathologically verified GBM and follow-up >12 mo: 474 patients met our inclusion criteria and were included in the analysis. Relevant demographic, clinical, molecular, and radiological variables were selected by a feature selection algorithm (Boruta) and used to build a ML-based model. RESULTS: Random forest prediction model, evaluated on an 80:20 split ratio, achieved an AUC of 0.81 (95% CI: 0.77; 0.83) demonstrating high discriminative ability. Optimizing the predictive value derived from the linear and nonlinear combinations of the selected input features, our model outperformed across all performance metrics multivariable logistic regression. CONCLUSION: A robust ML-based prediction model that identifies patients at high risk for early recurrence was successfully trained and internally validated. Considerable effort remains to integrate these predictions in a patient-centered care context. Graphical Abstract: