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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]

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.