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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1449235
This article is part of the Research Topic Application of Emerging Technologies in the Diagnosis and Treatment of Patients with Brain Tumors: New Frontiers in Imaging for Neuro-oncology View all 5 articles

Development and validation of a MRI-radiomics-based machine learning approach in High Grade Glioma to detect early recurrence

Provisionally accepted
Fabrizio Pignotti Fabrizio Pignotti 1,2Tamara Ius Tamara Ius 3Rosellina Russo Rosellina Russo 4*Daniele Bagatto Daniele Bagatto 5Francesco Beghella Bartoli Francesco Beghella Bartoli 4Edda boccia Edda boccia 4Luca Boldrini Luca Boldrini 4Silvia Chiesa Silvia Chiesa 4Chiara Ciardi Chiara Ciardi 5Davide Cusumano Davide Cusumano 6Carolina Giordano Carolina Giordano 7Giuseppe La Rocca Giuseppe La Rocca 8Ciro Mazzarella Ciro Mazzarella 4Edoardo Mazzucchi Edoardo Mazzucchi 1Alessandro Olivi Alessandro Olivi 2,8Miran Skrap Miran Skrap 9Huong Elena Tran Huong Elena Tran 4Giuseppe Varcasia Giuseppe Varcasia 7Simona Gaudino Simona Gaudino 2,7Giovanni Sabatino Giovanni Sabatino 1,10,2
  • 1 Department of Neurosurgery, Mater Olbia Hospital, Olbia, Italy
  • 2 Catholic University of the Sacred Heart, Rome, Rome, Sicily, Italy
  • 3 Neurosurgery Unit, Head-Neck and NeuroScience Department, Department of Neurosurgery, University Hospital of Udine, Udine, Italy
  • 4 Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Agostino Gemelli University Polyclinic (IRCCS), Rome, Italy
  • 5 Department of Neuroradiology, Ospedale Santa Maria della Misericordia di Udine, Udine, Italy
  • 6 Medical Physics Unit, Mater Olbia Hospital, Olbia, Italy
  • 7 Advanced Radiodiagnostics Centre, UOSD Neuroradiology, Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Agostino Gemelli University Polyclinic (IRCCS), Rome, Italy
  • 8 Institute of Neurosurgery, Agostino Gemelli University Polyclinic (IRCCS), Rome, Lazio, Italy
  • 9 Neurosurgery Unit, Head-Neck, Department of Neurosurgery, University Hospital of Udine, Udine, Italy
  • 10 Institute of Neurosurgery, Fondazione, Agostino Gemelli University Polyclinic (IRCCS), Rome, Lazio, Italy

The final, formatted version of the article will be published soon.

    Purpose: Patients diagnosed with High Grade Gliomas (HGG) generally tend to have a relatively negative prognosis with a high risk of early tumor recurrence (TR) after post-operative radio-chemotherapy. The assessment of the pre-operative risk of early versus delayed TR can be crucial to develop a personalized surgical approach. The purpose of this article is to predict TR using MRI radiomic analysis.Methods: Data were retrospectively collected from a database. A total of 248 patients were included based on the availability of 6-month TR results: 188 were used to train the model, the others to externally validate it. After manual segmentation of the tumor, Radiomic features were extracted and different machine learning models were implemented considering a combination of T1 and T2 weighted MR sequences.Receiver Operating Characteristic (ROC) curve was calculated with relative model performance metrics (accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)) at the best threshold based on the Youden Index.Results: Models performance were evaluated based on test set results. The best model resulted to be the XGBoost, with an area under ROC curve of 0.72 (95% CI: 0.56 -0.87) At the best threshold, the model exhibits 0.75 (95% CI: 0.63 -0.75) as accuracy, 0.62 (95% CI: 0.38 -0.83) as sensitivity 0.80 (95% CI: 0.66 -0.89 as specificity, 0.53 (95% CI: 0.31 -0.73) as PPV, 0.88 (95% CI: 0.72 -0.94) as NPV.MRI radiomic analysis represents a powerful tool to predict late HGG recurrence, which can be useful to plan personalized surgical treatments and to offer pertinent patient pre-operative counseling.

    Keywords: machine learning, Recurrence, high grade glioma, Radiomics, prognosis

    Received: 14 Jun 2024; Accepted: 16 Oct 2024.

    Copyright: © 2024 Pignotti, Ius, Russo, Bagatto, Beghella Bartoli, boccia, Boldrini, Chiesa, Ciardi, Cusumano, Giordano, La Rocca, Mazzarella, Mazzucchi, Olivi, Skrap, Tran, Varcasia, Gaudino and Sabatino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Rosellina Russo, Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Agostino Gemelli University Polyclinic (IRCCS), Rome, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.