The added prognostic value of radiological phenotype combined with clinical features and molecular subtype in anaplastic gliomas

Minsu Lee, Kyunghwa Han, Sung Soo Ahn, Sohi Bae, Yoon Seong Choi, Je Beom Hong, Jong Hee Chang, Se Hoon Kim, Seung Koo Lee

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Purpose: To determine whether radiological phenotype can improve the predictive performance of the risk model based on molecular subtype and clinical risk factors in anaplastic glioma patients. Methods: This retrospective study was approved by our institutional review board with waiver of informed consent. MR images of 86 patients with pathologically diagnosed anaplastic glioma (WHO grade III) between January 2007 and February 2016 were analyzed according to the Visually Accessible Rembrandt Images (VASARI) features set. Significant imaging findings were selected to generate a radiological risk score (RRS) for overall survival (OS) and progression-free survival (PFS) using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The prognostic value of RRS was evaluated with multivariate Cox regression including molecular subtype and clinical risk factors. The C-indices of multivariate models with and without RRS were compared by bootstrapping. Results: Eight VASARI features contributed to RRS for OS and six contributed to PFS. Multifocality or multicentricity was the most influential feature, followed by restricted diffusion. RRS was significantly associated with OS and PFS (P <.001), as well as age and molecular subtype. The multivariate model with RRS demonstrated a significantly higher predictive performance than the model without (C-index difference: 0.074, 95% confidence interval [CI]: 0.031, 0.148 for OS; C-index difference: 0.054, 95% CI: 0.014, 0.123 for PFS). Conclusion: RRS derived from VASARI features was an independent predictor of survival in patients with anaplastic gliomas. The addition of RRS significantly improved the predictive performance of the molecular feature based model.

Original languageEnglish
Pages (from-to)129-138
Number of pages10
JournalJournal of Neuro-Oncology
Issue number1
Publication statusPublished - 2019 Mar 15

Bibliographical note

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Oncology
  • Neurology
  • Clinical Neurology
  • Cancer Research


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