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Background: Computerized radiological image analysis (radiomics) enables the investigation of image-derived phenotypes by extracting large numbers of quantitative features. We hypothesized that radiomics features may contain prognostic information that enhances conventional body composition analysis. We aimed to investigate whether body composition-associated radiomics features hold additional value over conventional body composition analysis and clinical patient characteristics used to predict survival of pancreatic ductal adenocarcinoma (PDAC) patients. Methods: Computed tomography images of 304 patients undergoing elective pancreatic cancer resection were analysed. 2D radiomics features were extracted from skeletal muscle and subcutaneous and visceral adipose tissue (SAT and VAT) compartments from a single slice at the third lumbar vertebra. The study population was randomly split (80:20) into training and holdout subsets. Feature ranking with Least Absolute Shrinkage Selection Operator (LASSO) followed by multivariable stepwise Cox regression in 1000 bootstrapped resamples of the training data was performed and tested on the holdout data. The fitted regression predictors were used as "scores" for a clinical (C-Score), body composition (B-Score), and radiomics (R-Score) model. To stratify patients into the highest 25% and lowest 25% risk of mortality compared to the middle 50%, the Harrell Concordance Index was used. Results: Based on LASSOand stepwise cox regression for overall survival, ASA ≥3 and age were the most important clinical variables and constituted the C-score, and VAT-index (VATI) was the most important body composition variable and constituted the B-score. Three radiomics features (SATI_original_shape2D_Perimeter, VATI_original_glszm_SmallAreaEmphasis, and VATI_original_firstorder_Maximum) emerged as the most frequent set of features and yielded an R-Score. Of the mean concordance indices of C-, B-, and R-scores, R-score performed best (0.61, 95% CI 0.56--0.65, p<0.001), followed by the C-score (0.59, 95% CI 0.55-0.63, p<0.001) and B-score (0.55, 95% CI 0.50--0.60, p=0.03). Kaplan-Meier projection revealed that C-, B, and R-scores showed a clear split in the survival curves in the training set, although none remained significant in the holdout set. Conclusion: It is feasible to implement a data-driven radiomics approach to body composition imaging. Radiomics features provided improved predictive performance compared to conventional body composition variables for the prediction of overall survival of PDAC patients undergoing primary resection. [ABSTRACT FROM AUTHOR]

Background: Taking multiple anthropometric measurements for the description of body proportions in an accurate way is a time-consuming procedure that requires specific tools and skills. This is why we developed an alternative method based on digital photography for taking these measurements which is faster and easier to use, to make anthropometry more user-friendly and approachable to paediatricians. Methods: We conducted a cross-sectional study in 54 children between 2 and 18 years of age. We compared manual measurements with photogrammetric measurements to validate our method. Results: Inter-observer correlations of all measurements are ≥0.96 and mean differences are 0.3-0.9 cm, except for arm span. Comparison of manual to photogrammetric measurements shows mean differences of 0.6-1.3 cm, with correlations ≥0.92, except for sitting height and arm span. Correlations of ratios between methods are height/sitting height ( r=0.77), biacromium/biiliacum ( r=0.74) and subischial leg length/sitting height ( r=0.75) . Conclusion: Photogrammetric anthropometry is faster, easier to use and provides the paediatrician with more flexibility as taking the digital photographs and performing the analysis are separated. [ABSTRACT FROM AUTHOR]

Fiber tracking of Diffusion Tensor Imaging (DTI) data offers a unique insight into the three-dimensional organisation of white matter structures in the living brain. However, fiber tracking algorithms require a number of user-defined input parameters that strongly affect the output results. Usually the fiber tracking parameters are set once and are then re-used for several patient datasets. However, the stability of the chosen parameters is not evaluated and a small change in the parameter values can give very different results. The user remains completely unaware of such effects. Furthermore it is difficult to reproduce output results between different users. We propose a visualization tool that allows the user to visually explore how small variations in parameter values affect the output of fiber tracking. With this knowledge the user cannot only assess the stability of commonly used parameter values but also evaluate in a more reliable way the output results between different patients. Existing tools do not provide such information. A small user evaluation of our tool has been done to show the potential of the technique. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate whether hypertrophy after portal vein embolization (PVE) and maximum liver function capacity (LiMAx) are predictable by an artificial neural network (ANN) model based on computed tomography (CT) texture features. Methods: We report a retrospective analysis on 118 patients undergoing preoperative assessment by CT before and after PVE for subsequent extended liver resection due to a malignant tumor at RWTH Aachen University Hospital. The LiMAx test was carried out in a subgroup of 55 patients prior to PVE. Associations between CT texture features and hypertrophy as well as liver function were assessed by a multilayer perceptron ANN model. Results: Liver volumetry showed a median hypertrophy degree of 33.9% (16.5–60.4%) after PVE. Non-response, defined as a hypertrophy grade lower than 25%, was found in 36.5% (43/118) of the cases. The ANN prediction of the hypertrophy response showed a sensitivity of 95.8%, specificity of 44.4% and overall prediction accuracy of 74.6% (p < 0.001). The observed median LiMAx was 327 (248–433) μg/kg/h and was strongly correlated with the predicted LiMAx (R2 = 0.89). Conclusion: Our study shows that an ANN model based on CT texture features is able to predict the maximum liver function capacity and may be useful to assess potential hypertrophy after performing PVE. [ABSTRACT FROM AUTHOR]

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers. [ABSTRACT FROM AUTHOR]

Objectives: Body composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images., Methods: Expert-drawn segmentations of visceral and subcutaneous adipose tissue (VAT/SAT) and skeletal muscle (SM) of L3-CT-images of 3187 patients undergoing abdominal surgery were used to train a DLNN. The external validation cohort was comprised of 2535 patients with abdominal cancer. DLNN performance was evaluated with (geometric) dice similarity (DS) and Lin's concordance correlation coefficient., Results: There was a strong concordance between automatic and manual segmentations with median DS for SM, VAT, and SAT of 0.97 (IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: SM 0.964 (0.959-0.968), VAT 0.998 (0.998-0.998), and SAT 0.992 (0.991-0.993). Bland-Altman metrics indicated only small and clinically insignificant systematic offsets; SM radiodensity: 0.23 Hounsfield units (0.5%), SM: 1.26 cm2.m-2 (2.8%), VAT: -1.02 cm2.m-2 (1.7%), and SAT: 3.24 cm2.m-2 (4.6%)., Conclusion: A robustly-performing and independently externally validated DLNN for automated body composition analysis was developed., Advances in Knowledge: This DLNN was successfully trained and externally validated on several large patient cohorts. The trained algorithm could facilitate large-scale population studies and implementation of body composition analysis into clinical practice., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Background: Patients with advanced-stage pancreatic ductal adenocarcinoma (PDAC) are regularly treated with FOLFIRINOX, a chemotherapy regimen based on 5-fluorouracil, irinotecan and oxaliplatin, which is associated with high toxicity. Dosing of FOLFIRINOX is based on body surface area, risking under- or overdosing caused by altered pharmacokinetics due to interindividual differences in body composition. This study aimed to investigate the relationship between body composition and treatment toxicity in advanced stage PDAC patients treated with FOLFIRINOX., Methods: Data from patients treated at the Maastricht University Medical Centre + between 2012 and 2020 were collected retrospectively (n = 65). Skeletal muscle-, visceral adipose tissue, subcutaneous adipose tissue-, (SM-Index, VAT-Index, SAT-Index resp.) and Skeletal Muscle Radiation Attenuation (SM-RA) were calculated after segmentation of computed tomography (CT) images at the third lumbar level using a validated deep learning method. Lean body mass (LBM) was estimated using SM-Index. Toxicities were scored and grade 3-4 adverse events were considered dose-limiting toxicities (DLTs)., Results: Sixty-seven DLTs were reported during the median follow-up of 51.4 (95%CI 39.2-63.7) weeks. Patients who experienced at least one DLT had significantly higher dose intensity per LBM for all separate cytotoxics of FOLFIRINOX. Independent prognostic factors for the number of DLTs per cycle were: sarcopenia (β = 0.292; 95%CI 0.013 to 0.065; p = 0.013), SM-Index change (% per 30 days, β = -0.045; 95%CI -0.079 to -0.011; p = 0.011), VAT-Index change (% per 30 days, β = -0.006; 95%CI -0.012 to 0.000; p = 0.040) between diagnosis and the first follow-up CT scan, and cumulative relative dose intensity >80 % (β = -0.315; 95 % CI -0.543 to -0.087; p = 0.008)., Conclusion: Sarcopenia and early muscle and fat wasting during FOLFIRINOX treatment were associated with treatment-related toxicity, warranting exploration of body composition guided personalized dosing of chemotherapeutics to limit DLTs., Competing Interests: Declaration of competing interest Judith de Vos-Geelen has served as a consultant for Amgen, AstraZeneca, MSD, Pierre Fabre, and Servier, and has received institutional research funding from Servier. All outside the submitted work. All other authors did not have any conflicts to declare., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Fiber tracking of Diffusion Tensor Imaging (DTI) data offers a unique insight into the three-dimensional organisation of white matter structures in the living brain. However, fiber tracking algorithms require a number of user-defined input parameters that strongly affect the output results. Usually the fiber tracking parameters are set once and are then re-used for several patient datasets. However, the stability of the chosen parameters is not evaluated and a small change in the parameter values can give very different results. The user remains completely unaware of such effects. Furthermore, it is difficult to reproduce output results between different users. We propose a visualization tool that allows the user to visually explore how small variations in parameter values affect the output of fiber tracking. With this knowledge the user cannot only assess the stability of commonly used parameter values but also evaluate in a more reliable way the output results between different patients. Existing tools do not provide such information. A small user evaluation of our tool has been done to show the potential of the technique.