Adiposity and Coagulation: Predicting Postinjury Coagulation With Advanced Imaging Analysis.

INTRODUCTION: Anatomic distribution of adipose tissue has demonstrated variable associations with hypercoagulability. Utilizing a retrospective analysis of a previously enrolled prospective cohort, we assessed computed tomography (CT) scan-based anthropometric and volumetric measures of adiposity as predictors of postinjury hypercoagulability. METHODS: Segmentation analysis of arrival CT scans in significantly injured patients at a single level-I trauma center enrolled from December 2017 to August 2021 were analyzed for anthropometric indices of waist circumference (WC) and sagittal abdominal diameter (SAD), and volumetric parameters of visceral adipose tissue, superficial/deep subcutaneous adipose tissue, psoas/paravertebral muscle volume, and abdominal wall muscle volume. Associations with thromboelastography (TEG) were explored. RESULTS: Data from 91 patients showed strong correlations between body mass index and standard anthropometric measures of WC and SAD (P < 0.001); calculated volumes of subcutaneous adipose tissue and visceral adipose tissue (P < 0.001); and ratios of subcutaneous adipose:psoas muscle (SP ratio) and visceral adipose:psoas muscle ratio (both with P < 0.001, respectively). Correlation between TEG maximal amplitude (MA) and body mass index and SAD were not significant, with only weak correlation between TEG-MA and WC (r = 0.238, P = 0.041). Moderate but significant correlations existed between SP ratio and TEG-MA (r = 0.340, P = 0.005), but not visceral adipose:psoas muscle ratio (r = 0.159, P = 0.198). The relationship between TEG-MA and SP ratio remained significant when adjusted for injury severity score and lactate level (b = 0.302, P = 0.001). CONCLUSIONS: SP ratio is more strongly correlated with TEG-MA than standard obesity measures, and independently predicts increasing clot strength/stability after injury. Coagulation-relevant measures of sarcopenic obesity can be measured on CT scan, and may be used to optimize thromboprophylaxis strategies for obese injured patients.

Optimizing MRI sequences and images for MRI-based stereotactic radiosurgery treatment planning.

AIM: Development of MRI sequences and processing methods for the production of images appropriate for direct use in stereotactic radiosurgery (SRS) treatment planning. BACKGROUND: MRI is useful in SRS treatment planning, especially for patients with brain lesions or anatomical targets that are poorly distinguished by CT, but its use requires further refinement. This methodology seeks to optimize MRI sequences to generate distortion-free and clinically relevant MR images for MRI-only SRS treatment planning. MATERIALS AND METHODS: We used commercially available SRS MRI-guided radiotherapy phantoms and eight patients to optimize sequences for patient imaging. Workflow involved the choice of correct MRI sequence(s), optimization of the sequence parameters, evaluation of image quality (artifact free and clinically relevant), measurement of geometrical distortion, and evaluation of the accuracy of our offline correction algorithm. RESULTS: CT images showed a maximum deviation of 1.3 mm and minimum deviation of 0.4 mm from true fiducial position for SRS coordinate definition. Interestingly, uncorrected MR images showed maximum deviation of 1.2 mm and minimum of 0.4 mm, comparable to CT images used for SRS coordinate definition. After geometrical correction, we observed a maximum deviation of 1.1 mm and minimum deviation of only 0.3 mm. CONCLUSION: Our optimized MRI pulse sequences and image correction technique show promising results; MR images produced under these conditions are appropriate for direct use in SRS treatment planning.

Liver Surface Nodularity Score Allows Prediction of Cirrhosis Decompensation and Death.

Purpose To determine whether use of the liver surface nodularity (LSN) score, a quantitative biomarker derived from routine computed tomographic (CT) images, allows prediction of cirrhosis decompensation and death. Materials and Methods For this institutional review board-approved HIPAA-compliant retrospective study, adult patients with cirrhosis and Model for End-Stage Liver Disease (MELD) score within 3 months of initial liver CT imaging between January 3, 2006, and May 30, 2012, were identified from electronic medical records (n = 830). The LSN score was measured by using CT images and quantitative software. Competing risk regression was used to determine the association of the LSN score with hepatic decompensation and overall survival. A risk model combining LSN scores (<3 or ≥3) and MELD scores (<10 or ≥10) was created for predicting liver-related events. Results In patients with compensated cirrhosis, 40% (129 of 326) experienced decompensation during a median follow-up period of 4.22 years. After adjustment for competing risks including MELD score, LSN score (hazard ratio, 1.38; 95% confidence interval: 1.06, 1.79) was found to be independently predictive of hepatic decompensation. Median times to decompensation of patients at high (1.76 years, n = 48), intermediate (3.79 years, n = 126), and low (6.14 years, n = 152) risk of hepatic decompensation were significantly different (P < .001). Among the full cohort with compensated or decompensated cirrhosis, 61% (504 of 830) died during the median follow-up period of 2.26 years. After adjustment for competing risks, LSN score (hazard ratio, 1.22; 95% confidence interval: 1.11, 1.33) and MELD score (hazard ratio, 1.08; 95% confidence interval: 1.06, 1.11) were found to be independent predictors of death. Median times to death of patients at high (0.94 years, n = 315), intermediate (2.79 years, n = 312), and low (4.69 years, n = 203) risk were significantly different (P < .001). Conclusion The LSN score derived from routine CT images allows prediction of cirrhosis decompensation and death. ©RSNA, 2016 Online supplemental material is available for this article.

Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression.

Radiomics is an emerging area within clinical radiology research. It seeks to take full advantage of all the information contained in multiple medical imaging modalities. With a radiomics approach, medical images are not limited to providing only a qualitative assessment but can also provide quantitative data by parameterizing image features. These parameters can be used to identify regions and volumes of interest and discriminate normal healthy tissue from abnormal or diseased tissue. Radiomics is an interlinked sequence of processes of vital importance that begins with the acquisition and selection of medical images that involve standardization of acquisition protocols and inter-equipment normalization. This is followed by the identification and segmentation of regions or volumes of interest by expert radiologists through the use of computational tools that offer speed while reducing variability and bias. The segmentation process is the most critical stage in radiomics. This sometimes requires the incorporation of a pre-processing stage consisting of advanced techniques (reconstruction processes, filtering, etc.). Thereafter, representative characteristics of the region or volume of interest are extracted by approaches based on statistics, morphological features, and transform-based variables. Next, a statistical selection of the parameters that provide a high association and correlation with the clinical condition of interest is performed. Finally, processes such as data integration, standardization, classification, and mining processes can be applied as needed for particular applications. Ongoing research in radiomics aims to reduce the time and costs involved in interpreting medical images while simultaneously increasing the quality of diagnoses and monitoring of as well as the selection of treatment strategies. The results of many studies combining radiomics with standard medical techniques are highly encouraging, and these new approaches are increasingly used. This review article details the components of radiomics and discusses its applications, challenges, and future directions for this exciting new field of study.

Assessment of Treatment Response Following Yttrium-90 Transarterial Radioembolization of Liver Malignancies.

Transarterial radioembolization using yttrium-90 microspheres is an established and effective treatment for liver malignancies. Determining response to this treatment is difficult due to the radical changes that occur in tissue as a response to radiation. Though accurate assessment of treatment response is paramount for proper patient disposition, there is currently no standardized assessment protocol. Current methods of assessment often consider changes in size, necrosis, vascularity, fluorodeoxyglucose-positron emission tomography FDG-PET metabolic activity, and diffusion using diffusion-weighted magnetic resonance imaging (DWI). Current methods of assessment require a lag time of one to two months post-treatment to determine treatment effectiveness. This delay is a hindrance to obtaining better patient outcomes, giving rise to a need to identify markers for faster determination of treatment efficacy.

Multiparametric Magnetic Resonance Imaging in the Assessment of Primary Brain Tumors Through Radiomic Features: A Metric for Guided Radiation Treatment Planning.

Purpose The definition of radiotherapy target volume is a critical step in treatment planning for all tumor sites. Conventional magnetic resonance imaging (MRI) pulse sequences are used for the definition of the gross target volume (GTV) and the contouring of glioblastoma multiforme (GBM) and meningioma. We propose the use of multiparametric MRI combined with radiomic features to improve the texture-based differentiation of tumor from edema for GTV definition and to differentiate vasogenic from tumor cell infiltration edema. Methods Twenty-five patients with brain tumor and peritumoral edema (PTE) were assessed. Of the enrolled patients, 17 (63 ± 10 years old, six female and 11 male patients) were diagnosed with GBM and eight (64 ± 14 years old, five female and three male patients) with meningioma. A 3 Tesla (3T) MRI scanner was used to scan patients using a 3D multi-echo Gradient Echo (GRE) sequence. After the acquisition process, two experienced neuroradiologists independently used an in-house semiautomatic algorithm to conduct a segmentation of two regions of interest (ROI; edema and tumor) in all patients using functional MRI sequences, apparent diffusion coefficient (ADC), and dynamic contrast-enhanced MRI (DCE-MRI), as well as anatomical MRI sequences-T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR). Radiomic (computer-extracted texture) features were extracted from all ROIs through different approaches, including first-, second-, and higher-order statistics, both with and without normalization, leading to the calculation of around 300 different texture parameters for each ROI. Based on the extracted parameters, a least absolute shrinkage and selection operator (LASSO) analysis was used to isolate the parameters that best differentiated edema from tumors while irrelevant parameters were discarded. Results and conclusions The parameters chosen by LASSO were used to perform statistical analyses which allowed identification of the variables with the best discriminant ability in all scenarios. Receiver operating characteristic results showcase both the best single discriminator and the discriminant capacity of the model using all variables selected by LASSO. Excellent results were obtained for patients with GBM with all MRI sequences, with and without normalization; a T1-weighted sequence postcontrast (T1W+C) with normalization offered the best tumor classification (area under the curve, AUC > 0.97). For patients with meningioma, a good model of tumor classification was obtained through the T1-weighted sequence (T1W) without normalization (AUC > 0.71). However, there was no agreement between the results of both radiologists for some MRI sequences analyzed for patients with GBM and meningioma. In conclusion, a small subset of radiomic features showed an excellent ability to distinguish edema from tumor tissue through its most discriminating features.

Precision analysis of a quantitative CT liver surface nodularity score.

PURPOSE: To evaluate precision of a software-based liver surface nodularity (LSN) score derived from CT images. METHODS: An anthropomorphic CT phantom was constructed with simulated liver containing smooth and nodular segments at the surface and simulated visceral and subcutaneous fat components. The phantom was scanned multiple times on a single CT scanner with adjustment of image acquisition and reconstruction parameters (N = 34) and on 22 different CT scanners from 4 manufacturers at 12 imaging centers. LSN scores were obtained using a software-based method. Repeatability and reproducibility were evaluated by intraclass correlation (ICC) and coefficient of variation. Using abdominal CT images from 68 patients with various stages of chronic liver disease, inter-observer agreement and test-retest repeatability among 12 readers assessing LSN by software- vs. visual-based scoring methods were evaluated by ICC. RESULTS: There was excellent repeatability of LSN scores (ICC:0.79-0.99) using the CT phantom and routine image acquisition and reconstruction parameters (kVp 100-140, mA 200-400, and auto-mA, section thickness 1.25-5.0 mm, field of view 35-50 cm, and smooth or standard kernels). There was excellent reproducibility (smooth ICC: 0.97; 95% CI 0.95, 0.99; CV: 7%; nodular ICC: 0.94; 95% CI 0.89, 0.97; CV: 8%) for LSN scores derived from CT images from 22 different scanners. Inter-observer agreement for the software-based LSN scoring method was excellent (ICC: 0.84; 95% CI 0.79, 0.88; CV: 28%) vs. good for the visual-based method (ICC: 0.61; 95% CI 0.51, 0.69; CV: 43%). Test-retest repeatability for the software-based LSN scoring method was excellent (ICC: 0.82; 95% CI 0.79, 0.84; CV: 12%). CONCLUSION: The software-based LSN score is a quantitative CT imaging biomarker with excellent repeatability, reproducibility, inter-observer agreement, and test-retest repeatability.

Comparative Effectiveness of Tumor Response Assessment Methods: Standard of Care Versus Computer-Assisted Response Evaluation.

PURPOSE: To compare the effectiveness of metastatic tumor response evaluation with computed tomography using computer-assisted versus manual methods. MATERIALS AND METHODS: In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, 11 readers from 10 different institutions independently categorized tumor response according to three different therapeutic response criteria by using paired baseline and initial post-therapy computed tomography studies from 20 randomly selected patients with metastatic renal cell carcinoma who were treated with sunitinib as part of a completed phase III multi-institutional study. Images were evaluated with a manual tumor response evaluation method (standard of care) and with computer-assisted response evaluation (CARE) that included stepwise guidance, interactive error identification and correction methods, automated tumor metric extraction, calculations, response categorization, and data and image archiving. A crossover design, patient randomization, and 2-week washout period were used to reduce recall bias. Comparative effectiveness metrics included error rate and mean patient evaluation time. RESULTS: The standard-of-care method, on average, was associated with one or more errors in 30.5% (6.1 of 20) of patients, whereas CARE had a 0.0% (0.0 of 20) error rate ( P < .001). The most common errors were related to data transfer and arithmetic calculation. In patients with errors, the median number of error types was 1 (range, 1 to 3). Mean patient evaluation time with CARE was twice as fast as the standard-of-care method (6.4 minutes v 13.1 minutes; P < .001). CONCLUSION: CARE reduced errors and time of evaluation, which indicated better overall effectiveness than manual tumor response evaluation methods that are the current standard of care.