Application of an artificial intelligence algorithm to prognostically stratify grade II gliomas

D. Cesselli, T. Ius, M. Isola, F. Del Ben, G. Da Col, M. Bulfoni, M. Turetta, E. Pegolo, S. Marzinotto, C.A. Scott, L. Mariuzzi, C. Di Loreto, A.P. Beltrami, M. Skrap

Research output: Contribution to journalArticlepeer-review

Abstract

(1) Background: Recently, it has been shown that the extent of resection (EOR) and molecular classification of low-grade gliomas (LGGs) are endowed with prognostic significance. However, a prognostic stratification of patients able to give specific weight to the single parameters able to predict prognosis is still missing. Here, we adopt classic statistics and an artificial intelligence algorithm to define a multiparametric prognostic stratification of grade II glioma patients. (2) Methods: 241 adults who underwent surgery for a supratentorial LGG were included. Clinical, neuroradiological, surgical, histopathological and molecular data were assessed for their ability to predict overall survival (OS), progression-free survival (PFS), and malignant progression-free survival (MPFS). Finally, a decision-tree algorithm was employed to stratify patients. (3) Results: Classic statistics confirmed EOR, pre-operative-and post-operative tumor volumes, Ki67, and the molecular classification as independent predictors of OS, PFS, and MPFS. The decision tree approach provided an algorithm capable of identifying prognostic factors and defining both the cut-off levels and the hierarchy to be used in order to delineate specific prognostic classes with high positive predictive value. Key results were the superior role of EOR on that of molecular class, the importance of second surgery, and the role of different prognostic factors within the three molecular classes. (4) Conclusions: This study proposes a stratification of LGG patients based on the different combinations of clinical, molecular, and imaging data, adopting a supervised non-parametric learning method. If validated in independent case studies, the clinical utility of this innovative stratification approach might be proved. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Original languageEnglish
Number of pages21
JournalCancers
Volume12
Issue number1
DOIs
Publication statusPublished - 2020

Keywords

  • Artificial intelligence
  • Decision trees
  • Extent of resection
  • Grade II glioma
  • Molecular classification
  • MRI data
  • Prognosis
  • Ki 67 antigen
  • adult
  • aged
  • algorithm
  • Article
  • artificial intelligence
  • cancer grading
  • cancer prognosis
  • cancer surgery
  • cancer survival
  • clinical feature
  • clinical outcome
  • cohort analysis
  • controlled study
  • decision tree
  • female
  • glioma
  • histopathology
  • human
  • human tissue
  • major clinical study
  • male
  • nuclear magnetic resonance imaging
  • overall survival
  • predictive value
  • progression free survival
  • retrospective study
  • survival prediction
  • tumor volume

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