U-Net Based Segmentation and Characterization of Gliomas

Shingo Kihira, Xueyan Mei, Keon Mahmoudi, Zelong Liu, Siddhant Dogra, Puneet Belani, Nadejda Tsankova, Adilia Hormigo, Zahi A. Fayad, Amish Doshi, Kambiz Nael

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

(1) Background: Gliomas are the most common primary brain neoplasms accounting for roughly 40–50% of all malignant primary central nervous system tumors. We aim to develop a deep learning-based framework for automated segmentation and prediction of biomarkers and prognosis in patients with gliomas. (2) Methods: In this retrospective two center study, patients were included if they (1) had a diagnosis of glioma with known surgical histopathology and (2) had preoperative MRI with FLAIR sequence. The entire tumor volume including FLAIR hyperintense infiltrative component and necrotic and cystic components was segmented. Deep learning-based U-Net framework was developed based on symmetric architecture from the 512 × 512 segmented maps from FLAIR as the ground truth mask. (3) Results: The final cohort consisted of 208 patients with mean ± standard deviation of age (years) of 56 ± 15 with M/F of 130/78. DSC of the generated mask was 0.93. Prediction for IDH-1 and MGMT status had a performance of AUC 0.88 and 0.62, respectively. Survival prediction of <18 months demonstrated AUC of 0.75. (4) Conclusions: Our deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.

Original languageEnglish (US)
Article number4457
JournalCancers
Volume14
Issue number18
DOIs
StatePublished - Sep 2022
Externally publishedYes

Keywords

  • deep learning
  • glioma
  • isocitrate dehydrogenase 1
  • magnetic resonance imaging
  • radiogenomic
  • tumor segmentation

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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