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Background and purpose Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value.

Background Based on promising results from radiomic approaches to predict O$^{6}$-methylguanine DNA methyltransferase promoter methylation status (MGMT status) and clinical outcome in patients with newly diagnosed glioblastoma, the current study aimed to evaluate radiomics in recurrent glioblastoma patients. Methods Pre-treatment MR-imaging data of 69 patients enrolled into the DIRECTOR trial in recurrent glioblastoma served as a training cohort, and 49 independent patients formed an external validation cohort. Contrast-enhancing tumor and peritumoral volumes were segmented on MR images. 180 radiomic features were extracted after application of two MR intensity normalization techniques: fixed number of bins and linear rescaling. Radiomic feature selection was performed via principal component analysis, and multivariable models were trained to predict MGMT status, progression-free survival from first salvage therapy, referred to herein as PFS$_{2}$, and overall survival (OS). The prognostic power of models was quantified with concordance index (CI) for survival data and area under receiver operating characteristic curve (AUC) for the MGMT status. Results We established and validated a radiomic model to predict MGMT status using linear intensity interpolation and considering features extracted from gadolinium-enhanced T1-weighted MRI (training AUC = 0.670, validation AUC = 0.673). Additionally, models predicting PFS$_{2}$ and OS were found for the training cohort but were not confirmed in our validation cohort. Conclusions A radiomic model for prediction of MGMT promoter methylation status from tumor texture features in patients with recurrent glioblastoma was successfully established, providing a non-invasive approach to anticipate patient's response to chemotherapy if biopsy cannot be performed. The radiomic approach to predict PFS$_{2}$ and OS failed.

OBJECTIVES To investigate the dependence of signal-to-noise ratio (SNR) and calculated average dose per volume of spiral breast-CT (B-CT) on breast size and breast density and to provide a guideline for choosing the optimal tube current for each B-CT examination. MATERIALS AND METHODS Three representative B-CT datasets (small, medium, large breast size) were chosen to create 3D-printed breast phantoms. The phantoms were filled with four different agarose-oil-emulsions mimicking differences in breast densities. Phantoms were scanned in a B-CT system with systematic variation of the tube current (6, 12.5, 25, 32, 40, 50, 64, 80, 100, 125 mA). Evaluation of SNR and the average dose per volume using Monte Carlo simulations were performed for high (HR) and standard (STD) spatial resolution. RESULTS SNR and average dose per volume increased with increasing tube current. Artifacts had negligible influence on image evaluation. SNR values ≥ 35 (HR) and ≥ 100 (STD) offer sufficient image quality for clinical evaluation with SNR being more dependent on breast density than on breast size. For an average absorbed dose limit of 6.5 mGy for the medium and large phantoms and 7 mGy for the small phantom, optimal tube currents were either 25 or 32 mA. CONCLUSIONS B-CT offers the possibility to vary the X-ray tube current, allowing image quality optimization based on individual patient's characteristics such as breast size and density. This study describes the optimal B-CT acquisition parameters, which provide diagnostic image quality for various breast sizes and densities, while keeping the average dose at a level similar to digital mammography. KEY POINTS • Image quality optimization based on breast size and density varying the tube current using spiral B-CT.

OBJECTIVES To assess the impact of patient off-centering on organ dose and image noise for head and thoracoabdominal CT in a pediatric phantom. METHODS An anthropomorphic phantom simulating a 5-year-old child was used. Semiconductor dosimeters were placed in various cranial and thoracoabdominal organs. Head and thoracoabdominal CT were performed using automatic tube current modulation (ATCM) and default bowtie filters. The phantom was imaged repeatedly at vertical table positions ranging from - 6 to + 6 cm from the 0-position. Tube current time products (TCTP), organ doses, and image noise were recorded. Scatter radiation was measured in the thyroid for head CT. The effect of ATCM and bowtie filters was assessed. RESULTS Depending on patient position, organ doses differed up to 22% for the supratentorial brain, 34% for the infratentorial brain, 19% for the eyes, 28% for the lungs, 25% for the stomach, and 22% for the liver compared with those in the 0-position. The relation between position and dose was linear and mainly affected by the bowtie filter in head CT, while it was quadratic and affected by ATCM and bowtie filter in thoracoabdominal CT. It further depended on the relative position of each organ to the isocenter. An inverse relation was found between position and image noise. Scatter radiation was not significantly related to patient positioning (p = 0.21). CONCLUSIONS In pediatric CT, vertical patient positioning had a substantial impact on radiation dose with differences of up to 34%, depending on the body region and location of each individual organ. KEY POINTS Patient off-centering has a substantial impact on organ radiation dose and image noise in pediatric CT. Impact of patient off-centering on radiation dose and noise differs between head and thoracoabdominal CT. Differences are caused by both ATCM and bowtie filter in thoracoabdominal CT, but mainly by bowtie filter in head CT.

Purpose To calculate the effect of localizer radiography projections to the total radiation dose, including both the dose from localizer radiography and that from subsequent chest computed tomography (CT) with tube current modulation (TCM). Materials and Methods An anthropomorphic phantom was scanned with 192-section CT without and with differently sized breast attachments. Chest CT with TCM was performed after one localizer radiographic examination with anteroposterior (AP) or posteroanterior (PA) projections. Dose distributions were obtained by means of Monte Carlo simulations based on acquired CT data. For Monte Carlo simulations of localizer radiography, the tube position was fixed at 0° and 180°; for chest CT, a spiral trajectory with TCM was used. The effect of tube start angles on dose distribution was investigated with Monte Carlo simulations by using TCM curves with fixed start angles (0°, 90°, and 180°). Total doses for lungs, heart, and breast were calculated as the sum of the dose from localizer radiography and CT. Image noise was defined as the standard deviation of attenuation measured in 14 circular regions of interest. The Wilcoxon signed rank test, paired t test, and Friedman analysis of variance were conducted to evaluate differences in noise, TCM curves, and organ doses, respectively. Results Organ doses from localizer radiography were lower when using a PA instead of an AP projection (P = .005). The use of a PA projection resulted in higher TCM values for chest CT (P < .001) owing to the higher attenuation (P < .001) and thus resulted in higher total organ doses for all investigated phantoms and protocols (P < .001). Noise in CT images was lower with PA localizer radiography than with AP localizer radiography (P = .03). The use of an AP projection allowed for total dose reductions of 16%, 15%, and 12% for lungs, breast, and heart, respectively. Differences in organ doses were not related to tube start angles (P = .17). Conclusion The total organ

PURPOSE: Lung cancer screening with CT has been recently recommended for decreasing lung cancer mortality. The radiation dose of CT, however, must be kept as low as reasonably achievable for reducing potential stochastic risks from ionizing radiation. The purpose of this study was to calculate individual patients' lung doses and to estimate cancer risks in low-dose CT (LDCT) in comparison with a standard dose CT (SDCT) protocol. MATERIALS AND METHODS: This study included 47 adult patients (mean age 63.0 ± 5.7 years) undergoing chest CT on a third-generation dual-source scanner. 23/47 patients (49%) had a non-enhanced chest SDCT, 24 patients (51%) underwent LDCT at 100 kVp with spectral shaping at a dose equivalent to a chest x-ray. 3D-dose distributions were obtained from Monte Carlo simulations for each patient, taking into account their body size and individual CT protocol. Based on the dose distributions, patient-specific lung doses were calculated and relative cancer risk was estimated according to BEIR VII recommendations. RESULTS: As compared to SDCT, the LDCT protocol allowed for significant organ dose and cancer risk reductions (p<0.001). On average, lung dose was reduced from 7.7 mGy to 0.3 mGy when using LDCT, which was associated with lowering of the cancer risk from 8.6 to 0.35 per 100'000 cases. A strong linear correlation between lung dose and patient effective diameter was found for both protocols (R2 = 0.72 and R2 = 0.75 for SDCT and LDCT, respectively). CONCLUSION: Use of a LDCT protocol for chest CT with a dose equivalent to a chest x-ray allows for significant lung dose and cancer risk reduction from ionizing radiation.