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Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles

Published: 01 February 2024 Publication History

Highlights

A set of 11 MRI and 11 histopathologic imaging features were identified as discriminative for prediction of overall survival (OS) based on a log-rank p value less than 0.01.
Cox proportional hazard models accounting for the censored patients were trained, and the discriminative performance was calculated using the concordance index (C-index) and Kaplan-Meier analysis.
OS time was predicted with a C-index of 0.87 using a full multi-omics model integrating MRI, pathology, clinical, and genetic profiles, which was higher than that achieved with individual modalities.

Abstract

Background

Magnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy.

Purpose

To evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients.

Materials and methods

Multi-contrast MRI, comprising T1-weighted, T1-weighted post-contrast, T2-weighted, T2 fluid-attenuated-inversion-recovery, and pathology images from glioma patients (n = 160) were retrospectively collected (1983–2008) from TCGA alongside age and sex. Phenotypic profiling of tumors was performed by quantifying the radiographic and histopathologic descriptors extracted from the delineated region-of-interest in MRI and PATH images. A Cox proportional hazard model was trained with the MRI and PATH features, IDH mutation status, and basic demographic variables (age and sex) to predict OS. The performance was evaluated in a split-train-test configuration using the concordance-index, computed between the predicted risk score and observed OS.

Results

The average age of patients was 51.2years (women: n = 77, age-range=18–84years; men: n = 83, age-range=21–80years). The median OS of the participants was 494.5 (range,3–4752), 481 (range,7–4752), and 524.5 days (range,3–2869), respectively, in complete dataset, training, and test datasets. The addition of MRI or PATH features improved prediction of OS when compared to models based on age, sex, and mutation status alone or their combination (p < 0.001). The full multi-omics model integrated MRI, PATH, clinical, and genetic profiles and predicted the OS best (c-index= 0.87).

Conclusion

The combination of imaging, genetic, and clinical profiles leads to a more accurate prognosis than the clinical and/or mutation status.

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Information & Contributors

Information

Published In

cover image Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine  Volume 242, Issue C
Dec 2023
870 pages

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 February 2024

Author Tags

  1. Multi-omics
  2. Radiographic images
  3. Digital histopathology images
  4. Clinical measures
  5. Genomic markers
  6. Gliomas
  7. Machine learning

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