Multidisciplinary Quantitative and Qualitative Assessment of IDH-mutant Gliomas with Full Diagnostic Deep Learning Image Reconstruction

Overview

Journal Eur J Radiol Open

Specialty Radiology

Date 2024 Dec 24

PMID 39717474

Authors

Christer Ruff

Paula Bombach

Constantin Roder

Eliane Weinbrenner

Christoph Artzner

Leonie Zerweck

Frank Paulsen

Till-Karsten Hauser

Ulrike Ernemann

Georg Gohla

Affiliations

Soon will be listed here.

Abstract

Materials And Methods: This study explores the feasibility of deep learning-based reconstruction (DLR) in MRI for IDH-mutant gliomas. The research utilizes a multidisciplinary approach, engaging neuroradiologists, neurosurgeons, neuro-oncologists, and radiotherapists to evaluate qualitative aspects of DLR and conventional reconstructed (CR) sequences. Furthermore, quantitative image quality and tumor volumes according to Response Assessment in Neuro-Oncology (RANO) 2.0 standards were assessed.

Results: All DLR sequences consistently outperformed CR sequences (median of 4 for all) in qualitative image quality across all raters (p < 0.001 for all) and revealed higher SNR and CNR values (p < 0.001 for all). Preference for all DLR over CR was overwhelming, with ratings of 84 % from the neuroradiologist, 100 % from the neurosurgeon, 92 % from the neuro-oncologist, and 84 % from the radiation oncologist. The RANO 2.0 compliant measurements showed no significant difference between the CR and DRL sequences (p = 0.142).

Conclusion: This study demonstrates the clinical feasibility of DLR in MR imaging of IDH-mutant gliomas, with significant time savings of 29.6 % on average and non-inferior image quality to CR. DLR sequences received strong multidisciplinary preference, underscoring their potential for enhancing neuro-oncological decision-making and suitability for clinical implementation.

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