An algorithm on the basis of standard MRI sequences and age predicted isocitrate dehydrogenase status in lower-grade gliomas without advanced computational methods. Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) stat...

Objectives To investigate if quantitative apparent diffusion coefficient (ADC) measurements can predict genetic subtypes of non-gadolinium-enhancing gliomas, comparing whole tumour against single slice analysis. Methods Volumetric T2-derived masks of 44 gliomas were co-registered to ADC maps with ADC mean (ADC_mean) calculated. For the slice analysis, two observers placed regions of interest in the largest tumour cross-section. The ratio (ADC_ratio) between ADC_mean in the tumour and normal appearing white matter was calculated for both methods. Results Isocitrate dehydrogenase (IDH) wild-type gliomas showed the lowest ADC values throughout (p < 0.001). ADC_mean in the IDH-mutant 1p19q intact group was significantly higher than in the IDH-mutant 1p19q co-deleted group (p < 0.01). A volumetric ADC_mean threshold of 1201 × 10⁻⁶ mm²/s identified IDH wild-type with a sensitivity of 83% and a specificity of 86%; a volumetric ADC_ratio cut-off value of 1.65 provided a sensitivity of 80% and a specificity of 92% (area under the curve (AUC) 0.9–0.94). A slice ADC_ratio threshold for observer 1 (observer 2) of 1.76 (1.83) provided a sensitivity of 80% (86%), specificity of 91% (100%) and AUC of 0.95 (0.96). The intraclass correlation coefficient was excellent (0.98). Conclusions ADC measurements can support the distinction of glioma subtypes. Volumetric and two-dimensional measurements yielded similar results in this study. Key Points • Diffusion-weighted MRI aids the identification of non-gadolinium-enhancing malignant gliomas • ADC measurements may permit non-gadolinium-enhancing glioma molecular subtyping • IDH wild-type gliomas have lower ADC values than IDH-mutant tumours • Single cross-section and volumetric ADC measurements yielded comparable results in this study

The aim was to predict survival of glioblastoma at 8 months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. Retrospective and prospective data were collected from 206 consecutive glioblastoma, isocitrate dehydrogenase -wildtype patients diagnosed between March 2014 and February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from 3 centers. Holdout test sets were retrospective (n = 19; internal validation), and prospective (n = 29; external validation from 8 distinct centers). Neural network branches for T₂-weighted and contrast-enhanced T₁-weighted inputs were concatenated to predict survival. A nonimaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; nonimaging features; and weighted dense blocks pretrained for abnormality detection. The imaging model outperformed the nonimaging model in all test sets (area under the receiver-operating characteristic curve, AUC P = .038) and performed similarly to a combined imaging/nonimaging model (P > .05). Imaging, nonimaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10 000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; P = .003). A deep learning model using MRI images after radiotherapy reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Background: To assess anatomical and quantitative diffusion-weighted MR imaging features in a recently classified lethal neoplasm, H3 K27M histone-mutant diffuse midline glioma [World Health Organization (WHO) IV]. Methods: Fifteen untreated gliomas in teenagers and adults (median age 19, range, 14–64) with confirmed H3 K27M histone-mutant genotype were analysed at a national referral centre. Morphological characteristics including tumour epicentre(s), T2/FLAIR and Gadolinium enhancement patterns, calcification, haemorrhage and cyst formation were recorded. Multiple apparent diffusion coefficient (ADC_min, ADC_mean) regions of interest were sited in solid tumour and normal appearing white matter (ADC_NAWM) using post-processing software (Olea Sphere v2.3, Olea Medical). ADC histogram data (2^nd, 5^th, 10^th percentile, median, mean, kurtosis, skewness) were calculated from volumetric tumour segmentations and tested against the regions of interest (ROI) data (Wilcoxon signed rank test). Results: The median interval from imaging to tissue diagnosis was 9 (range, 0–74) days. The structural MR imaging findings varied between individuals and within tumours, often featuring signal heterogeneity on all MR sequences. All gliomas demonstrated contact with the brain midline, and 67% exhibited rim-enhancing necrosis. The mean ROI ADC_min value was 0.84 (±0.15 standard deviation, SD) ×10⁻³ mm²/s. In the largest tumour cross-section (excluding necrosis), an average ADC_mean value of 1.12 (±0.25)×10⁻³ mm²/s was observed. The mean ADC_min/NAWM ratio was 1.097 (±0.149), and the mean ADC_mean/NAWM ratio measured 1.466 (±0.299). With the exception of the 2^nd centile, no statistical difference was observed between the regional and histogram derived ADC results. Conclusions: H3 K27M-mutant gliomas demonstrate variable morphology and diffusivity, commonly featuring moderately low ADC values in solid tumour. Regional ADC measurements appeared representative of volumetric histogram data in this study.

Relapsed or refractory primary central nervous system lymphoma (rrPCNSL) confers a poor prognosis with no accepted standard of care. Very few prospective studies have been conducted in this patient group. This study was a multicenter phase 1/2 study that investigated thiotepa in combination with ifosfamide, etoposide, and rituximab (TIER) for the treatment of PCNSL relapsed or refractory to high-dose methotrexate-based chemotherapy. A 3 + 3 design investigated the recommended phase 2 dose of thiotepa for a single-stage phase 2 cohort by assessing the activity of 2 cycles of TIER against rrPCNSL. The primary outcome was overall response rate. The dose-finding study demonstrated that 50 mg/m2 of thiotepa could be safely delivered within the TIER regimen. No dose-limiting toxicities were encountered in phase 1, and TIER was well-tolerated by the 27 patients treated in phase 2. The most common grade 3 to 4 toxicities were neutropenia (56% of patients) and thrombocytopenia (39%). An overall response was confirmed in 14 patients (52%), which met the prespecified threshold for clinically relevant activity. The median progression-free survival was 3 months (95% confidence interval [CI], 2 to 6 months) and overall survival 5 months (95% CI, 3 to 9 months). Exploratory analyses suggest a greater benefit for thiotepa-naïve patients. Six patients successfully completed autologous stem cell transplantation (ASCT) consolidation, with 4 experiencing durable remissions after a median follow-up of 50 months. The TIER regimen can be delivered safely and is active against rrPCNSL. When it is followed by ASCT, it can provide durable remission and long-term survival. However, for the majority of patients, prognosis remains poor, and novel treatment strategies are urgently needed. This trial was registered at https://www.clinicaltrialsregister.eu/ctr-search/search as EudraCT 2014-000227-24 and ISRCTN 12857473.