BACKGROUND AND PURPOSE: A new brain tumor entity occurring in early childhood characterized by a somatic BCL6 corepressor gene internal tandem duplication was recently described. The aim of this study was to describe the radiologic pattern of these tumors and correlate this pattern with histopathologic findings. MATERIALS AND METHODS: This retrospective, noninterventional study included 10 children diagnosed with a CNS tumor, either by ribonucleic acid–sequencing analysis or deoxyribonucleic acid methylation analysis. Clinical, radiologic, and histopathologic data were collected. A neuropathologist reviewed 9 tumor samples. Preoperative images were analyzed in consensus by 7 pediatric radiologists. RESULTS: All tumors were relatively large (range, 4.7–9.2 cm) intra-axial peripheral masses with well-defined borders and no peritumoral edema. All tumors showed mild and heterogeneous enhancement and marked restriction on DWI of the solid portions. Perfusion imaging showed a relatively lower CBF in the tumor than in the adjacent normal parenchyma. Nine of 10 tumors showed areas of necrosis, with the presence of hemorrhage in 8/10 and calcifications in 4/7. Large intratumoral macroscopic veins were observed in 9/10 patients. No intracranial or spinal leptomeningeal dissemination was noted at diagnosis. CONCLUSIONS: CNS tumors with a BCL6 corepressor gene internal tandem duplication present as large intra-axial peripheral masses with well-defined borders, no edema, restricted diffusion, weak contrast enhancement, frequent central necrosis, hemorrhage and calcifications, intratumoral veins, and no leptomeningeal dissemination at the time of diagnosis. Knowledge of these imaging characteristics may aid in histologic, genomic, and molecular profiling of brain tumors in young children.

Purpose: Tumor growth rate (TGR) represents the percentage change in tumor volume per month (%/m). Previous results from the GREPONET study showed that TGR measured after 3 months (TGR3m) of starting systemic treatment (ST) or watch and wait (WW) was an early biomarker predicting progression-free survival (PFS) in neuroendocrine tumors (NET). Experimental Design: Patients from 7 centers with advanced grade (G) 1/2 NETs from the pancreas (P)/small bowel (SB) initiating ST/WW were eligible. Computed tomography (CT)/MRI performed at prebaseline, baseline, and 3(±1) months of study entry were retrospectively reviewed. Aim-1: explore treatment-induced changes in TGR (ΔTGR3m-BL; paired T test), and Aim-2: validate TGR3m (<0.8%/m vs. ≥0.8%/m) as an early biomarker in an independent cohort (Kaplan–Meier/Cox regression). Results: Of 785 patients screened, 127 were eligible. Mean (SD) TGR0 and TGR3m were 5.4%/m (14.9) and −1.4%/m (11.8), respectively. Mean (SD) ΔTGR3m-BL paired-difference was −6.8%/m (19.3; P < 0.001). Most marked ΔTGR3m-BL [mean (SD)] were identified with targeted therapies [−11.3%/m (4.7); P = 0.0237] and chemotherapy [−7.9%/m (3.4); P = 0.0261]. Multivariable analysis confirmed the absence of previous treatment (OR = 4.65; 95% CI, 1.31–16.52; P = 0.018) and low TGR3m (continuous variable; OR 1.09; 95% CI, 1.01–1.19; P = 0.042) to be independent predictors of radiologic objective response. When the multivariable survival analysis for PFS (Cox regression) was adjusted to grade (P = 0.004) and stage (P = 0.017), TGR3m ≥ 0.8 (vs. <0.8) maintained its significance as a prognostic factor (P < 0.001), whereas TGR0 and ΔTGR3m-BL did not. TGR3m ≥ 0.8%/m was confirmed as an independent prognostic factor for PFS [external validation; Aim-2; multivariable HR 2.21 (95% CI, 1.21–3.70; P = 0.003)]. Conclusions: TGR has a role as a biomarker for monitoring response to therapy for early identification of treatment-induced changes and for early prediction of PFS and radiologic objective response.

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

Objective: To investigate the feasibility of diffusion-weighted magnetic resonance imaging (DW-MRI) as a predictive imaging marker after neoadjuvant chemotherapy in patients with rhabdomyosarcoma. Material and methods: We performed a multicenter retrospective study including pediatric, adolescent and young adult patients with rhabdomyosarcoma, Intergroup Rhabdomyosarcoma Study group III/IV, treated according to the European paediatric Soft tissue sarcoma Study Group (EpSSG) RMS2005 or MTS2008 studies. DW-MRI was performed according to institutional protocols. We performed two-dimensional single-slice tumor delineation. Areas of necrosis or hemorrhage were delineated to be excluded in the primary analysis. Mean, median and 5th and 95th apparent diffusion coefficient (ADC) were extracted. Results: Of 134 included patients, 82 had measurable tumor at diagnosis and response and DW-MRI scans of adequate quality and were included in the analysis. Technical heterogeneity in scan acquisition protocols and scanners was observed. Mean ADC at diagnosis was 1.1 (95% confidence interval [CI]: 1.1–1.2) (all ADC expressed in * 10⁻³ mm²/s), versus 1.6 (1.5–1.6) at response assessment. The 5th percentile ADC was 0.8 (0.7–0.9) at diagnosis and 1.1 (1.0–1.2) at response. Absolute change in mean ADC after neoadjuvant chemotherapy was 0.4 (0.3–0.5). Exploratory analyses for association between ADC and clinical parameters showed a significant difference in mean ADC at diagnosis for alveolar versus embryonal histology. Landmark analysis at nine weeks after the date of diagnosis showed no significant association (hazard ratio 1.3 [0.6–3.2]) between the mean ADC change and event-free survival. Conclusion: A significant change in the 5th percentile and the mean ADC after chemotherapy was observed. Strong heterogeneity was identified in DW-MRI acquisition protocols between centers and in individual patients. Graphical

Background/Objectives: To evaluate and optimize the reconstruction parameters of images acquired with a photon-counting CT scanner to achieve a stable radiomics signal. Methods: Radiomics is a quantitative imaging biomarker correlated to survival in oncology patients. Implementing radiomics in clinical routine remains challenging due to the feature’s instability. Photon-counting CT scans use innovative technology directly converting photons into electrical signals resulting in higher-resolution images with reduced artifacts. This study used two organic phantoms: a natural wet sponge and a dry sausage. UHR images were acquired using a NAEOTOM Alpha photon-counting CT scan (Siemens) with a 0.4 mm slice thickness and 0.3 × 0.3 mm pixel size. Tube current and voltage were fixed at 112 mA and 120 KvP. A total of 24 reconstruction parameter sets were obtained by combining different values of kernel (Br), quantitative iterative reconstruction (QIR), spectral reconstruction (keV), and matrix size. Ten successive acquisitions were obtained on both phantoms. In total, 93 radiomic features were extracted on an ROI using the default parameters of Pyradiomic 3.0.1. Each feature’s stability was evaluated using the coefficient of variation (CV) within each parameter set. Results: Of the 24 reconstruction parameter sets, 5 were selected based on best image quality by seven radiologists and three radiology technologists. Radiomics features were considered stable on a set when CV was less than 15%. Feature stability was impacted by reconstruction parameters and the phantom used. The most stable combination included 90 and 65 stable features of the 93 tested on the sausage and sponge respectively. It was configured with Br36, QIR 4, 60 keV, and a 1024 × 1024 matrix size. Conclusions: Images obtained on photon-counting CT scans offer promising radiomic feature stability with optimal parameter configurations that could be applied in a clinical setting.

Background: The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) is a potential biomarker for treatment response in pediatric rhabdomyosarcoma. Due to its rarity, investigations into this marker require multicenter approaches, which can result in variability in acquisition parameters. Objective: To evaluate the impact of different acquisition parameters on ADC estimates in a multicenter dataset of rhabdomyosarcoma patients. Materials and methods: We included 114 pediatric and adolescent rhabdomyosarcoma patients from 22 treatment centers (195 scans). Median age: 6.0 years (0.3–21.8). We evaluated the impact of voxel size, (number of) b-values, and echo time on tumor ADC values. The effect of the highest b-value was separately investigated on a subset of scans with five or more b-values. Results: We observed a large variability in key acquisition parameters in the overall cohort, and for individual imaging centers. No parameter showed a significant effect on ADC estimates of the whole cohort when corrected for multiple-comparisons. Decreasing the highest b-value within the same acquisition caused ADC to decrease on average by 2.8% per 100 s mm^-2. Differing b-values between scans at diagnosis and treatment response yielded significant changes in the longitudinal ADC for each patient (P<0.05). Conclusion: While we observed wide variation of acquisition parameters within a multicenter cohort, this did not lead to significant cross-sectional differences of tumor ADC. However, we found that modifying the highest b-value between baseline and follow-up can impact longitudinal ADC estimates. As such, we recommend the highest b-value to remain constant. This retrospective study was reviewed and approved by the Internal Review Board (UMC Utrecht, reference ID: 18-412). Graphical