Corrigendum to "Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study" [Phys. Imaging Radiat. Oncol. 22 (2022) 131-136].

[This corrects the article DOI: 10.1016/j.phro.2022.05.006.].

Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study.

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.

Improved Survival Prediction by Combining Radiological Imaging and S-100B Levels Into a Multivariate Model in Metastatic Melanoma Patients Treated With Immune Checkpoint Inhibition.

Purpose: We explored imaging and blood bio-markers for survival prediction in a cohort of patients with metastatic melanoma treated with immune checkpoint inhibition. Materials and Methods: 94 consecutive metastatic melanoma patients treated with immune checkpoint inhibition were included into this study. PET/CT imaging was available at baseline (Tp0), 3 months (Tp1) and 6 months (Tp2) after start of immunotherapy. Radiological response at Tp2 was evaluated using iRECIST. Total tumor burden (TB) at each time-point was measured and relative change of TB compared to baseline was calculated. LDH, CRP and S-100B were also analyzed. Cox proportional hazards model and logistic regression were used for survival analysis. Results: iRECIST at Tp2 was significantly associated with overall survival (OS) with C-index=0.68. TB at baseline was not associated with OS, whereas TB at Tp1 and Tp2 provided similar predictive power with C-index of 0.67 and 0.71, respectively. Appearance of new metastatic lesions during follow-up was an independent prognostic factor (C-index=0.73). Elevated LDH and S-100B ratios at Tp2 were significantly associated with worse OS: C-index=0.73 for LDH and 0.73 for S-100B. Correlation of LDH with TB was weak (r=0.34). A multivariate model including TB change, S-100B, and appearance of new lesions showed the best predictive performance with C-index=0.83. Conclusion: Our analysis shows only a weak correlation between LDH and TB. Additionally, baseline TB was not a prognostic factor in our cohort. A multivariate model combining early blood and imaging biomarkers achieved the best predictive power with regard to survival, outperforming iRECIST.

Quantification of the spatial distribution of primary tumors in the lung to develop new prognostic biomarkers for locally advanced NSCLC.

The anatomical location and extent of primary lung tumors have shown prognostic value for overall survival (OS). However, its manual assessment is prone to interobserver variability. This study aims to use data driven identification of image characteristics for OS in locally advanced non-small cell lung cancer (NSCLC) patients. Five stage IIIA/IIIB NSCLC patient cohorts were retrospectively collected. Patients were treated either with radiochemotherapy (RCT): RCT1* (n = 107), RCT2 (n = 95), RCT3 (n = 37) or with surgery combined with radiotherapy or chemotherapy: S1* (n = 135), S2 (n = 55). Based on a deformable image registration (MIM Vista, 6.9.2.), an in-house developed software transferred each primary tumor to the CT scan of a reference patient while maintaining the original tumor shape. A frequency-weighted cumulative status map was created for both exploratory cohorts (indicated with an asterisk), where the spatial extent of the tumor was uni-labeled with 2 years OS. For the exploratory cohorts, a permutation test with random assignment of patient status was performed to identify regions with statistically significant worse OS, referred to as decreased survival areas (DSA). The minimal Euclidean distance between primary tumor to DSA was extracted from the independent cohorts (negative distance in case of overlap). To account for the tumor volume, the distance was scaled with the radius of the volume-equivalent sphere. For the S1 cohort, DSA were located at the right main bronchus whereas for the RCT1 cohort they further extended in cranio-caudal direction. In the independent cohorts, the model based on distance to DSA achieved performance: AUCRCT2 [95% CI] = 0.67 [0.55-0.78] and AUCRCT3 = 0.59 [0.39-0.79] for RCT patients, but showed bad performance for surgery cohort (AUCS2 = 0.52 [0.30-0.74]). Shorter distance to DSA was associated with worse outcome (p = 0.0074). In conclusion, this explanatory analysis quantifies the value of primary tumor location for OS prediction based on cumulative status maps. Shorter distance of primary tumor to a high-risk region was associated with worse prognosis in the RCT cohort.

Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging.

BACKGROUND: Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). METHODS: A total of 1404 primary tumour radiomic features were extracted from pre-treatment [18F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS: Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). CONCLUSIONS: A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol.

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints.

Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.

Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial.

BACKGROUND: Based on promising results from radiomic approaches to predict O6-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 PFS2, 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 PFS2 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 PFS2 and OS failed.

Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types.

OBJECTIVES: In this study, we aimed to assess the impact of different CT reconstruction kernels on the stability of radiomic features and the transferability between different diseases and tissue types. Three lung diseases were evaluated, i.e. non-small cell lung cancer (NSCLC), malignant pleural mesothelioma (MPM) and interstitial lung disease related to systemic sclerosis (SSc-ILD) as well as four different tissue types, i.e. primary tumor, largest involved lymph node ipsilateral and contralateral lung. METHODS: Pre-treatment non-contrast enhanced CT scans from 23 NSCLC, 10 MPM and 12 SSc-ILD patients were collected retrospectively. For each patient, CT scans were reconstructed using smooth and sharp kernel in filtered back projection. The regions of interest (ROIs) were contoured on the smooth kernel-based CT and transferred to the sharp kernel-based CT. The voxels were resized to the largest voxel dimension of each cohort. In total, 1386 features were analyzed. Feature stability was assessed using the intraclass correlation coefficient. Features above the stability threshold >0.9 were considered stable. RESULTS: We observed a strong impact of the reconstruction method on stability of the features (at maximum 26% of the 1386 features were stable). Intensity features were the most stable followed by texture and wavelet features. The wavelet features showed a positive correlation between percentage of stable features and size of the ROI (R2 = 0.79, p = 0.005). Lymph node radiomics showed poorest stability (<10%) and lung radiomics the largest stability (26%). Robustness analysis done on the contralateral lung could to a large extent be transferred to the ipsilateral lung, and the overlap of stable lung features between different lung diseases was more than 50%. However, results of robustness studies cannot be transferred between tissue types, which was investigated in NSCLC and MPM patients; the overlap of stable features for lymph node and lung, as well as for primary tumor and lymph node was very small in both disease types. CONCLUSION: The robustness of radiomic features is strongly affected by different reconstruction kernels. The effect is largely influenced by the tissue type and less by the disease type. ADVANCES IN KNOWLEDGE: The study presents to our knowledge the most complete analysis on the impact of convolution kernel on the robustness of CT-based radiomics for four relevant tissue types in three different lung diseases. .

Radiomics Feature Activation Maps as a New Tool for Signature Interpretability.

INTRODUCTION: In the field of personalized medicine, radiomics has shown its potential to support treatment decisions. However, the limited feature interpretability hampers its introduction into the clinics. Here, we propose a new methodology to create radiomics feature activation maps, which allows to identify the spatial-anatomical locations responsible for signature activation based on local radiomics. The feasibility of this technique will be studied for histological subtype differentiation (adenocarcinoma versus squamous cell carcinoma) in non-small cell lung cancer (NSCLC) using computed tomography (CT) radiomics. MATERIALS AND METHODS: Pre-treatment CT scans were collected from a multi-centric Swiss trial (training, n=73, IIIA/N2 NSCLC, SAKK 16/00) and an independent cohort (validation, n=32, IIIA/N2/IIIB NSCLC). Based on the gross tumor volume (GTV), four peritumoral region of interests (ROI) were defined: lung_exterior (expansion into the lung), iso_exterior (expansion into lung and soft tissue), gradient (GTV border region), GTV+Rim (GTV and iso_exterior). For each ROI, 154 radiomic features were extracted using an in-house developed software implementation (Z-Rad, Python v2.7.14). Features robust against delineation variability served as an input for a multivariate logistic regression analysis. Model performance was quantified using the area under the receiver operating characteristic curve (AUC) and verified using five-fold cross validation and internal validation. Local radiomic features were extracted from the GTV+Rim ROI using non-overlapping 3x3x3 voxel patches previously marked as GTV or rim. A binary activation map was created for each patient using the median global feature value from the training. The ratios of activated/non-activated patches of GTV and rim regions were compared between histological subtypes (Wilcoxon test). RESULTS: Iso_exterior, gradient, GTV+Rim showed good performances for histological subtype prediction (AUCtraining=0.68-0.72 and AUCvalidation=0.73-0.74) whereas GTV and lung_exterior models failed validation. GTV+Rim model feature activation maps showed that local texture feature distribution differed significantly between histological subtypes in the rim (p=0.0481) but not in the GTV (p=0.461). CONCLUSION: In this exploratory study, radiomics-based prediction of NSCLC histological subtypes was predominantly based on the peritumoral region indicating that radiomics activation maps can be useful for tracing back the spatial location of regions responsible for signature activation.

Associations of Socio-Demographic, Clinical and Biochemical Parameters with Healthcare Cost, Health- and Renal-Related Quality of Life in Hemodialysis Patients: A Clinical Observational Study.

We examined factors associated with healthcare cost, health-related quality of life (HRQOL), and kidney disease quality of life (KDQOL) in hemodialysis patients. We conducted a cross-sectional study on 160 patients from January to April 2019 at a hemodialysis center. Socio-demographic, clinical, and laboratory parameters and quality of life (QOL) (using KDQOL-SF-v1.3) were assessed. Monthly healthcare costs were extracted from the hospital information system. The means of healthcare cost, HRQOL, and KDQOL were VND 9.4 ± 1.6 million, VND 45.1 ± 21.9 and VND 51.3 ± 13.0, respectively. In the multivariate analysis, the healthcare cost was higher in patients with a longer hemodialysis vintage (regression coefficient (B): 0.74; 95% confidence interval (95% CI): 0.25; 1.23), comorbidity (B: 0.77; 95% CI: 0.24; 1.31); and lower in those with a higher hematocrit concentration (B: -0.07; 95% CI: -0.13; -0.01). Patients that lived in urban areas (B: 9.08; 95% CI: 2.30; 15.85) had a better HRQOL; those with a comorbidity (B: -14.20; 95% CI: -21.43; -6.97), and with hypoalbuminemia (B: -9.31; 95% CI: -16.58; -2.04) had a poorer HRQOL. Patients with a higher level of education (B: 5.38~6.29) had a better KDQOL; those with a comorbidity had a poorer KDQOL (B: -6.17; 95% CI: -10.49; -1.85). In conclusion, a longer hemodialysis vintage, a comorbidity and a lower hematocrit concentration were associated with higher healthcare costs. Patients who lived in urban areas had a better HRQOL and a higher level of education led to a better KDQOL. Patients with a comorbidity had a lower HRQOL and KDQOL. Malnourished patients had a lower HRQOL.

Radiomic biomarkers for head and neck squamous cell carcinoma.

Tumor heterogeneity is a well-known prognostic factor in head and neck squamous cell carcinoma (HNSCC). A major limitation of tissue- and blood-derived tumor markers is the lack of spatial resolution to image tumor heterogeneity. Tissue markers derived from tumor biopsies usually represent only a small tumor subregion at a single timepoint and are therefore often not representative of the tumors' biology or the biological alterations during and after treatment. Similarly, liquid biopsies give an overall picture of the tumors' secreted factors but completely lack any spatial resolution. Radiomics has the potential to give complete three-dimensional information about the tumor. We conducted a comprehensive literature search to assess the correlation of radiomics to tumor biology and treatment outcome in HNSCC and to assess current limitations of the radiomic biomarkers. In total, 25 studies that explored the ability of radiomics to predict tumor biology and phenotype in HNSCC and 28 studies that explored radiomics to predict post-treatment events were identified. Out of these 53 studies, only three failed to show a significant correlation. The major technical challenges are currently artifacts due to metal implants, non-standardized contrast injection, and delineation uncertainties. All studies to date were retrospective and none of the above-mentioned radiomics signatures have been validated in an independent cohort using an independent software implementation, which shows that transferability due to the numerous technical challenges is currently a major limitation. However, radiomics is a very young field and these studies hopefully pave the way for clinical implementation of radiomics for HNSCC in the future.

Comparison of robust to standardized CT radiomics models to predict overall survival for non-small cell lung cancer patients.

BACKGROUND: Radiomics is a promising tool for the identification of new prognostic biomarkers. Radiomic features can be affected by different scanning protocols, often present in retrospective and prospective clinical data. We compared a computed tomography (CT) radiomics model based on a large but highly heterogeneous multicentric image dataset with robust feature pre-selection to a model based on a smaller but standardized image dataset without pre-selection. MATERIALS AND METHODS: Primary tumor radiomics was extracted from pre-treatment CTs of IIIA/N2/IIIB NSCLC patients from a prospective Swiss multicentric randomized trial (npatient  = 124, ninstitution  = 14, SAKK 16/00) and a validation dataset (npatient  = 31, ninstitution  = 1). Four robustness studies investigating inter-observer delineation variation, motion, convolution kernel, and contrast were conducted to identify robust features using an intraclass correlation coefficient threshold >0.9. Two 12-months overall survival (OS) logistic regression models were trained: (a) on the entire multicentric heterogeneous dataset but with robust feature pre-selection (MCR) and (b) on a smaller standardized subset using all features (STD). Both models were validated on the validation dataset acquired with similar reconstruction parameters as the STD dataset. The model performances were compared using the DeLong test. RESULTS: In total, 113 stable features were identified (nshape  = 8, nintensity  = 0, ntexture  = 7, nwavelet  = 98). The convolution kernel had the strongest influence on the feature robustness (<20% stable features). The final models of MCR and STD consisted of one and two features respectively. Both features of the STD model were identified as non-robust. MCR did not show performance significantly different from STD on the validation cohort (AUC [95%CI] = 0.72 [0.48-0.95] and 0.79 [0.63-0.95], p = 0.59). CONCLUSION: Prognostic OS CT radiomics model for NSCLC based on a heterogeneous multicentric imaging dataset with robust feature pre-selection performed equally well as a model on a standardized dataset.

Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition.

PURPOSE: We assessed the predictive potential of positron emission tomography (PET)/CT-based radiomics, lesion volume, and routine blood markers for early differentiation of pseudoprogression from true progression at 3 months. EXPERIMENTAL DESIGN: 112 patients with metastatic melanoma treated with immune checkpoint inhibition were included in our study. Median follow-up duration was 22 months. 716 metastases were segmented individually on CT and 2[18F]fluoro-2-deoxy-D-glucose (FDG)-PET imaging at three timepoints: baseline (TP0), 3 months (TP1), and 6 months (TP2). Response was defined on a lesion-individual level (RECIST 1.1) and retrospectively correlated with FDG-PET/CT radiomic features and the blood markers LDH/S100. Seven multivariate prediction model classes were generated. RESULTS: Two-year (median) overall survival, progression-free survival, and immune progression-free survival were 69% (not reached), 24% (6 months), and 42% (16 months), respectively. At 3 months, 106 (16%) lesions had progressed, of which 30 (5%) were identified as pseudoprogression at 6 months. Patients with pseudoprogressive lesions and without true progressive lesions had a similar outcome to responding patients and a significantly better 2-year overall survival of 100% (30 months), compared with 15% (10 months) in patients with true progressions/without pseudoprogression (P = 0.002). Patients with mixed progressive/pseudoprogressive lesions were in between at 53% (25 months). The blood prediction model (LDH+S100) achieved an AUC = 0.71. Higher LDH/S100 values indicated a low chance of pseudoprogression. Volume-based models: AUC = 0.72 (TP1) and AUC = 0.80 (delta-volume between TP0/TP1). Radiomics models (including/excluding volume-related features): AUC = 0.79/0.78. Combined blood/volume model: AUC = 0.79. Combined blood/radiomics model (including volume-related features): AUC = 0.78. The combined blood/radiomics model (excluding volume-related features) performed best: AUC = 0.82. CONCLUSIONS: Noninvasive PET/CT-based radiomics, especially in combination with blood parameters, are promising biomarkers for early differentiation of pseudoprogression, potentially avoiding added toxicity or delayed treatment switch.

Dosimetric and geometric end-to-end accuracy of a magnetic resonance guided linear accelerator.

The introduction of real-time imaging by magnetic resonance guided linear accelerators (MR-Linacs) enabled adaptive treatments and gating on the tumor position. Different end-to-end tests monitored the accuracy of our MR-Linac during the first year of clinical operation. We report on the stability of these tests covering a static, adaptive and gating workflow. Film measurements showed gamma passing rates of 96.4% ± 3.4% for the static tests (five measurements) and for the two adaptive tests 98.9% and 99.99%, respectively (criterion 2%/2mm). The gated point dose measurements in the breathing phantom were 2.7% lower than in the static phantom.

CT radiomics and PET radiomics: ready for clinical implementation?

INTRODUCTION: Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest. EVIDENCE ACQUISITION: Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications. EVIDENCE SYNTHESIS: We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling. CONCLUSIONS: Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.

Interchangeability of radiomic features between [18F]-FDG PET/CT and [18F]-FDG PET/MR.

PURPOSE: Radiomics is a promising tool for identification of new prognostic biomarkers. However, image reconstruction settings and test-retest variability may influence the absolute values of radiomic features. Unstable radiomic features cannot be used as reliable biomarkers. PET/MR is becoming increasingly available and often replaces PET/CT for different indications. The aim of this study was to quantify to what extend [18F]-FDG PET/CT radiomics models can be transferred to [18F]-FDG PET/MR and thereby to investigate the feasibility of combined PET/CT-PET/MR models. For this purpose, we compared PET radiomic features calculated on PET/MR and PET/CT and on a 4D-gated PET/MR dataset to select radiomic features that are robust to attenuation correction differences and test-retest variability, respectively. METHODS: Two cohorts of patients with lung lesions were studied. In the first cohort (n = 10), inhale and exhale phases of a 4D [18F]-FDG PET/MR (4DPETMR) scan were used as a surrogate for a test-retest dataset. In the second cohort (n = 9), patients underwent first an [18F]-FDG PET/MR scan (SIGNA PET/MR, GE Healthcare, Waukesha) followed by an [18F]-FDG PET/CT scan (Discovery 690, GE Healthcare) with a delay of 33 ± 5 min (PETCT-PETMR). Lesions were segmented on inhale and exhale 4D-PET phases and on the individual PET scans from PET/CT and PET/MR with two semi-automated methods (gradient-based and threshold-based). The scan resolution was 2.73 × 2.73 × 3.27 mm and 2.34 × 2.34 × 2.78 mm for the PET/CT and PET/MR, respectively. In total, 1355 radiomic features were calculated, i.e., shape (n = 18), intensity (n = 17), texture (n = 136), and wavelet (n = 1184). The intraclass correlation coefficient (ICC) was calculated to compare the radiomic features of the 4DPETMR (ICC(1,1)) and PETCT-PETMR (ICC(3,1)) datasets. An ICC > 0.9 was considered stable among both types of PET scans. RESULTS AND CONCLUSION: The 4DPETMR showed highest stability for shape, intensity, and texture (>80%) and lower stability for wavelet features (40%). Gradient-based method showed higher stability compared to threshold-based method except from shape features. In PETCT-PETMR, more than 61% of shape and intensity features were stable for both segmentation methods. However, a reduced stability was observed for texture (50%) and wavelet (<30%) features. More wavelet features were robust in the smoothed images (low-pass filtering) compared to images with emphasized heterogeneity (high-pass filtering). Comparing stable features of both investigations, highest agreement was found for intensity and lower agreement for shape, texture, and wavelet features. Only 53.6% of stable texture features in 4DPETMR were also stable in PETCT-PETMR, and even less in case of wavelet features (40.4%). Approximately 16.9% (texture) and 43.2% (wavelet) of stable PETCT-PETMR features are unstable in 4DPETMR. To conclude, shape and intensity features were robust when comparing two types of [18F]-FDG PET scans (PET/CT and PET/MR). Reduced stability was observed for texture and wavelet features. We identified multiple origins of instability of radiomic features, such as attenuation correction differences, different uptake times, and spatial resolution. This needs to be considered when models based on PET/CT are transferred PET/MR models or when combined models are used.

The traumata in elderly people of 3 provinces QuangNgai, PhuYen and DaNang

1.321 elderly subjects in 3 provinces of the South Central Vietnam were enrolled into investigation. Their morbidity was rather high 25.8%, mainly chronical diseases 15.4%, and 4.2% related to trauma. The mortality was 0.08% in the cases of falling down, 0.23% in all general cases of trauma. The rate of hospitalized by trauma accounted for 43.6%, less one week treatment duration was 45.8%, from one week to one month 50%. The most traumatic victims were not the main sourse of household income, therefore the family income was not effected significantly. Post-trauma infirmity accounted for high rate, mainly for motion organs.