Physiological iodine uptake of the spine's bone marrow in dual-energy computed tomography - using artificial intelligence to define reference values based on 678 CT examinations of 189 individuals.

Purpose: The bone marrow's iodine uptake in dual-energy CT (DECT) is elevated in malignant disease. We aimed to investigate the physiological range of bone marrow iodine uptake after intravenous contrast application, and examine its dependence on vBMD, iodine blood pool, patient age, and sex. Method: Retrospective analysis of oncological patients without evidence of metastatic disease. DECT examinations were performed on a spectral detector CT scanner in portal venous contrast phase. The thoracic and lumbar spine were segmented by a pre-trained neural network, obtaining volumetric iodine concentration data [mg/ml]. vBMD was assessed using a phantomless, CE-certified software [mg/cm3]. The iodine blood pool was estimated by ROI-based measurements in the great abdominal vessels. A multivariate regression model was fit with the dependent variable "median bone marrow iodine uptake". Standardized regression coefficients (ß) were calculated to assess the impact of each covariate. Results: 678 consecutive DECT exams of 189 individuals (93 female, age 61.4 ± 16.0 years) were evaluated. AI-based segmentation provided volumetric data of 97.9% of the included vertebrae (n=11,286). The 95th percentile of bone marrow iodine uptake, as a surrogate for the upper margin of the physiological distribution, ranged between 4.7-6.4 mg/ml. vBMD (p <0.001, mean ß=0.50) and portal vein iodine blood pool (p <0.001, mean ß=0.43) mediated the strongest impact. Based thereon, adjusted reference values were calculated. Conclusion: The bone marrow iodine uptake demonstrates a distinct profile depending on vBMD, iodine blood pool, patient age, and sex. This study is the first to provide the adjusted reference values.

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.

Background: The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset. Methods: In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0-25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy. Results: Median CCS among 250 evaluated patients was 10 [6-15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97-0.98)]. Best attenuation threshold for identification of involved lung was -522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r2 deep-learning vs. threshold=0.84), both automated quantification methods translated to the semi-quantitative CCS in a non-linear fashion, with an increasing slope towards higher CCS (r2 deep-learning vs. CCS= 0.80, r2 threshold vs. CCS=0.63). Conclusions: The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: -522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.

Dual-Energy CT, Virtual Non-Calcium Bone Marrow Imaging of the Spine: An AI-Assisted, Volumetric Evaluation of a Reference Cohort with 500 CT Scans.

Virtual non-calcium (VNCa) images from dual-energy computed tomography (DECT) have shown high potential to diagnose bone marrow disease of the spine, which is frequently disguised by dense trabecular bone on conventional CT. In this study, we aimed to define reference values for VNCa bone marrow images of the spine in a large-scale cohort of healthy individuals. DECT was performed after resection of a malignant skin tumor without evidence of metastatic disease. Image analysis was fully automated and did not require specific user interaction. The thoracolumbar spine was segmented by a pretrained convolutional neuronal network. Volumetric VNCa data of the spine's bone marrow space were processed using the maximum, medium, and low calcium suppression indices. Histograms of VNCa attenuation were created for each exam and suppression setting. We included 500 exams of 168 individuals (88 female, patient age 61.0 ± 15.9). A total of 8298 vertebrae were segmented. The attenuation histograms' overlap of two consecutive exams, as a measure for intraindividual consistency, yielded a median of 0.93 (IQR: 0.88-0.96). As our main result, we provide the age- and sex-specific bone marrow attenuation profiles of a large-scale cohort of individuals with healthy trabecular bone structure as a reference for future studies. We conclude that artificial-intelligence-supported, fully automated volumetric assessment is an intraindividually robust method to image the spine's bone marrow using VNCa data from DECT.

Feasibility of artificial intelligence-supported assessment of bone marrow infiltration using dual-energy computed tomography in patients with evidence of monoclonal protein - a retrospective observational study.

OBJECTIVES: To demonstrate the feasibility of an automated, non-invasive approach to estimate bone marrow (BM) infiltration of multiple myeloma (MM) by dual-energy computed tomography (DECT) after virtual non-calcium (VNCa) post-processing. METHODS: Individuals with MM and monoclonal gammopathy of unknown significance (MGUS) with concurrent DECT and BM biopsy between May 2018 and July 2020 were included in this retrospective observational study. Two pathologists and three radiologists reported BM infiltration and presence of osteolytic bone lesions, respectively. Bone mineral density (BMD) was quantified CT-based by a CE-certified software. Automated spine segmentation was implemented by a pre-trained convolutional neural network. The non-fatty portion of BM was defined as voxels > 0 HU in VNCa. For statistical assessment, multivariate regression and receiver operating characteristic (ROC) were conducted. RESULTS: Thirty-five patients (mean age 65 ± 12 years; 18 female) were evaluated. The non-fatty portion of BM significantly predicted BM infiltration after adjusting for the covariable BMD (p = 0.007, r = 0.46). A non-fatty portion of BM > 0.93% could anticipate osteolytic lesions and the clinical diagnosis of MM with an area under the ROC curve of 0.70 [0.49-0.90] and 0.71 [0.54-0.89], respectively. Our approach identified MM-patients without osteolytic lesions on conventional CT with a sensitivity and specificity of 0.63 and 0.71, respectively. CONCLUSIONS: Automated, AI-supported attenuation assessment of the spine in DECT VNCa is feasible to predict BM infiltration in MM. Further, the proposed method might allow for pre-selecting patients with higher pre-test probability of osteolytic bone lesions and support the clinical diagnosis of MM without pathognomonic lesions on conventional CT. KEY POINTS: • The retrospective study provides an automated approach for quantification of the non-fatty portion of bone marrow, based on AI-supported spine segmentation and virtual non-calcium dual-energy CT data. • An increasing non-fatty portion of bone marrow is associated with a higher infiltration determined by invasive biopsy after adjusting for bone mineral density as a control variable (p = 0.007, r = 0.46). • The non-fatty portion of bone marrow might support the clinical diagnosis of multiple myeloma when conventional CT images are negative (sensitivity 0.63, specificity 0.71).

Do antiosteoporotic drugs improve bone regeneration in vivo?

PURPOSE: Treatment of complex fractures in the elderly is a challenge for operative reconstruction due to degraded bone structure. Early peri-operative bone anabolic treatment could improve new bone formation, avoid implant loosening and accelerate fracture healing. METHODS: To compare the osteoanabolic potential of different drugs after distraction osteogenesis, 168 female Sprague-Dawley rats underwent lengthening of the right femur using a monolateral external fixator. Animals were randomly divided into six groups: vehicle-injected group, PTH(1-34), raloxifen, strontium ranelate, alendronate and simvastatin. Histomorphometry, CT-scanning, DEXA- and biomechanical analysis were performed to evaluate new bone formation, callus volume, mineralisation and biomechanical strength. Expression of bone metabolic mediators and differentiation indicators of distracted and intact bone were examined by RT-PCR and western blot. RESULTS: Histological analysis showed significant increase of the bone mass after treatment with PTH(1-34), raloxifen and strontium ranelate (p = 0.02). Raloxifen increased bone mineral content (BMC) of the whole distracted femur significantly (p = 0.007). Callus volume was significantly larger in the PTH(1-34), raloxifen and simvastatin groups (p = 0.001) compared to control. Ultimate load of distracted new formed bone was increased in PTH(1-34) and raloxifen groups. It seems that PTH(1-34) and raloxifen have a stronger effect on bone where a repair response is activated. Strontium ranelate demonstrates similar effects to PTH regarding new bone formation but shows low values for mineralisation and biomechanical strength. CONCLUSION: This study suggests that peri-operative treatment of complex and/or osteoporotic fractures with PTH(1-34) and raloxifen might be useful as a stimulator of bone formation and mineralisation to shorten the consolidation time in humans.

Life cycle adapted upstream open reading frames (uORFs) in Trypanosoma congolense: A post-transcriptional approach to accurate gene regulation.

The presented work explores the regulatory influence of upstream open reading frames (uORFs) on gene expression in Trypanosoma congolense. More than 31,000 uORFs in total were identified and characterized here. We found evidence for the uORFs' appearance in the transcriptome to be correlated with proteomic expression data, clearly indicating their repressive potential in T. congolense, which has to rely on post-transcriptional gene expression regulation due to its unique genomic organization. Our data show that uORF's translation repressive potential does not only correlate with elemental sequence features such as length, position and quantity, but involves more subtle components, in particular the codon and amino acid profiles. This corresponds with the popular mechanistic model of a ribosome shedding initiation factors during the translation of a uORF, which can prevent reinitiation at the downstream start codon of the actual protein-coding sequence, due to the former extensive consumption of crucial translation components. We suggest that uORFs with uncommon codon and amino acid usage can slow down the translation elongation process in T. congolense, systematically deplete the limited factors, and restrict downstream reinitiation, setting up a bottleneck for subsequent translation of the protein-coding sequence. Additionally we conclude that uORFs dynamically influence the T. congolense life cycle. We found evidence that transition to epimastigote form could be supported by gain of uORFs due to alternative trans-splicing, which down-regulate housekeeping genes' expression and render the trypanosome in a metabolically reduced state of endurance.