Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction

• DOI: 10.1016/j.ejro.2024.100617
• Publication Date: 2024-12-04T12:59:14Z
• Authors: Christer Ruff; Paula Bombach; Constantin Roder; Eliane Weinbrenner; Christoph Artzner; Leonie Zerweck; Frank Paulsen; Till-Karsten Hauser; Ulrike Ernemann; Georg Gohla
• Keywords: Medical physics. Medical radiology. Nuclear medicine; Magnetic resonance imaging; Multidisciplinary; Image reconstruction; R895-920; Deep learning; Original Article; IDH-mutant gliomas; Diagnostic accuracy

Rationale and Objectives: Diagnostic accuracy and therapeutic decision-making for IDH-mutant gliomas in tumor board reviews are based on MRI and multidisciplinary interactions. 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.