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. 2023 Feb 23;13(5):849.
doi: 10.3390/diagnostics13050849.

Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation

Maria-Fatima Chilaca-Rosas  1 , Melissa Garcia-Lezama  2 , Sergio Moreno-Jimenez  3 , Ernesto Roldan-Valadez  2   4
Affiliations

Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation

Maria-Fatima Chilaca-Rosas et al. Diagnostics (Basel). .

Abstract

Background: Radiomics refers to a recent area of knowledge that studies features extracted from different imaging techniques and subsequently transformed into high-dimensional data that can be associated with biological events. Diffuse midline gliomas (DMG) are one of the most devastating types of cancer, with a median survival of approximately 11 months after diagnosis and 4-5 months after radiological and clinical progression.

Methods: A retrospective study. From a database of 91 patients with DMG, only 12 had the H3.3K27M mutation and brain MRI DICOM files available. Radiomic features were extracted from MRI T1 and T2 sequences using LIFEx software. Statistical analysis included normal distribution tests and the Mann-Whitney U test, ROC analysis, and calculation of cut-off values.

Results: A total of 5760 radiomic values were included in the analyses. AUROC demonstrated 13 radiomics with statistical significance for progression-free survival (PFS) and overall survival (OS). Diagnostic performance tests showed nine radiomics with specificity for PFS above 90% and one with a sensitivity of 97.2%. For OS, 3 out of 4 radiomics demonstrated between 80 and 90% sensitivity.

Conclusions: Several radiomic features demonstrated statistical significance and have the potential to further aid DMG diagnostic assessment non-invasively. The most significant radiomics were first- and second-order features with GLCM texture profile, GLZLM_GLNU, and NGLDM_Contrast.

Keywords: MRI; brain tumour; midline glioma; paediatric; prognosis; radiomic.

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Conflict of interest statement

The authors declare that there are no conflict of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
Patient selection process.
Figure 2
Figure 2
The axial and sagittal planes show the MRI appearance of DMG on T1- and T2-weighted images. (A,C) Images show contrast-enhanced tumoural lesions, but (B,D) images showed extensive oedema tumoural and diffuse/infiltrative patterns.
Figure 3
Figure 3
Description of steps followed for the data acquisition of patients. In the initial stage, patients underwent MRI imaging studies that showed pathological lesions compatible with DMG. This assessment was followed by histopathological diagnostic and nuclear immunostaining with recognition of the mutated H3F3A K27M protein. Segmentation of the tumour regions was performed. Images were uploaded into the software LIFEx for postprocessing and extraction of radiomic features.
Figure 4
Figure 4
Survival analyses “Kaplan Meier” curves. Part (left) represents the curve for PFS. Part (right) represents the curve for OS.
Figure 5
Figure 5
ROC curves of the significant radiomics. Sections (A,B) show the radiomics with significance for progression-free survival; (C,D) show the radiomics with significance for overall survival above the median.
See this image and copyright information in PMC

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References

    1. Di Ruscio V., Del Baldo G., Fabozzi F., Vinci M., Cacchione A., de Billy E., Megaro G., Carai A., Mastronuzzi A. Pediatric Diffuse Midline Gliomas: An Unfinished Puzzle. Diagnostics. 2022;12:2064. doi: 10.3390/diagnostics12092064. - DOI - PMC - PubMed
    1. Gianno F., Giovannoni I., Cafferata B., Diomedi-Camassei F., Minasi S., Barresi S., Buttarelli F.R., Alesi V., Cardoni A., Antonelli M., et al. Paediatric-type diffuse high-grade gliomas in the 5th CNS WHO Classification. Pathologica. 2022;114:422–435. doi: 10.32074/1591-951X-830. - DOI - PMC - PubMed
    1. Mackay A., Burford A., Carvalho D., Izquierdo E., Fazal-Salom J., Taylor K.R., Bjerke L., Clarke M., Vinci M., Nandhabalan M., et al. Integrated Molecular Meta-Analysis of 1000 Pediatric High-Grade and Diffuse Intrinsic Pontine Glioma. Cancer Cell. 2017;32:520–537.e5. doi: 10.1016/j.ccell.2017.08.017. - DOI - PMC - PubMed
    1. Coleman C., Chen K., Lu A., Seashore E., Stoller S., Davis T., Braunstein S., Gupta N., Mueller S. Interdisciplinary care of children with diffuse midline glioma. Neoplasia. 2023;35:100851. doi: 10.1016/j.neo.2022.100851. - DOI - PMC - PubMed
    1. Li Q., Dong F., Jiang B., Zhang M. Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics. Front. Oncol. 2021;11:646267. doi: 10.3389/fonc.2021.646267. - DOI - PMC - PubMed

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