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Purpose: The radiologists' workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes., Methods: Retrospectively, 72 patients [m, 47; age, 63.5 (27-87) years] with nodal lymphoma (n = 27) or benign abdominal lymph nodes (n = 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test., Results: About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435-0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000-1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly (p = 0.011, DeLong) superior to the DECT model., Conclusions: Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment., (© 2023. The Author(s).)

Background: Treatment plans for squamous cell carcinoma of the head and neck (SCCHN) are individually decided in tumor board meetings but some treatment decision-steps lack objective prognostic estimates. Our purpose was to explore the potential of radiomics for SCCHN therapy-specific survival prognostication and to increase the models' interpretability by ranking the features based on their predictive importance., Methods: We included 157 SCCHN patients (male, 119; female, 38; mean age, 64.39 ± 10.71 years) with baseline head and neck CT between 09/2014 and 08/2020 in this retrospective study. Patients were stratified according to their treatment. Using independent training and test datasets with cross-validation and 100 iterations, we identified, ranked and inter-correlated prognostic signatures using elastic net (EN) and random survival forest (RSF). We benchmarked the models against clinical parameters. Inter-reader variation was analyzed using intraclass-correlation coefficients (ICC)., Results: EN and RSF achieved top prognostication performances of AUC = 0.795 (95% CI 0.767-0.822) and AUC = 0.811 (95% CI 0.782-0.839). RSF prognostication slightly outperformed the EN for the complete (ΔAUC 0.035, p = 0.002) and radiochemotherapy (ΔAUC 0.092, p < 0.001) cohort. RSF was superior to most clinical benchmarking (p ≤ 0.006). The inter-reader correlation was moderate or high for all features classes (ICC ≥ 0.77 (± 0.19)). Shape features had the highest prognostic importance, followed by texture features., Conclusions: EN and RSF built on radiomics features may be used for survival prognostication. The prognostically leading features may vary between treatment subgroups. This warrants further validation to potentially aid clinical treatment decision making in the future., (© 2023. The Author(s).)

Objectives: The aim of this study was to evaluate whether simple 2D measurements in axial slices of head and neck CT examinations correlate with generally established measurements of body composition in abdominal CT at the height of the third lumbar vertebra and thus allow for an estimation of muscle and fat masses., Methods: One hundred twenty-two patients who underwent concurrent CT of the head and neck and the abdomen between July 2016 and July 2020 were retrospectively included. For a subset of 30 patients, additional bioelectrical impedance analysis (BIA) was available. Areas of paraspinal muscles at the height of the third (C3) and fifth cervical vertebrae (C5) as well as the total cross-sectional area at the height of C3 and at the submandibular level were correlated with the results of abdominal measurements and BIA. Furthermore, intra- and interreader variabilities of all measurements were assessed., Results: Regarding adipose tissue, good correlations were found between the total cross-sectional area of the patient's body at the submandibular level and at the height of C3 between both abdominal measurements and BIA results (r = 0.8-0.92; all p < 0.001). Regarding muscle, the total paraspinal muscle area at the height of C3 and C5 showed strong correlations with abdominal measurements and moderate to strong correlations with BIA results (r = 0.44-0.80; all p < 0.001), with the muscle area on C5 yielding slightly higher correlations., Conclusions: Body composition information can be obtained with comparable reliability from head and neck CT using simple biplanar measurements as from abdominal CT., Key Points: • The total paraspinal muscle area at the height of C3 and C5 correlates strongly with abdominal muscle mass. • The total cross-sectional area at the submandibular level and at the height of C3 shows good correlations with abdominal fat mass. • The described measurements facilitate a rapid, opportunistic assessment of relevant body composition parameters., (© 2022. The Author(s).)

Objectives: To compare the accuracy of lesion detection of trauma-related injuries using combined "all-in-one" fused (AIO) and conventionally reconstructed images (CR) in acute trauma CT., Methods: In this retrospective study, trauma CT of 66 patients (median age 47 years, range 18-96 years; 20 female (30.3%)) were read using AIO and CR. Images were independently reviewed by 4 blinded radiologists (two residents and two consultants) for trauma-related injuries in 22 regions. Sub-analyses were performed to analyze the influence of experience (residents vs. consultants) and body region (chest, abdomen, skeletal structures) on lesion detection. Paired t-test was used to compare the accuracy of lesion detection. The effect size was calculated (Cohen's d). Linear mixed-effects model with patients as the fixed effect and random forest models were used to investigate the effect of experience, reconstruction/image processing, and body region on lesion detection., Results: Reading time of residents was significantly faster using AIO (AIO: 266 ± 72 s, CR: 318 ± 113 s; p < 0.001; d = 0.46) while no significant difference was observed in the accuracy of lesion detection (AIO: 93.5 ± 6.0%, CR: 94.6 ± 6.0% p = 0.092; d =  - 0.21). Reading time of consultants showed no significant difference (AIO: 283 ± 82 s, CR: 274 ± 95 s; p = 0.067; d = 0.16). Accuracy was significantly higher using CR; however, the difference and effect size were very small (AIO 95.1 ± 4.9%, CR: 97.3 ± 3.7%, p = 0.002; d =  - 0.39). The linear mixed-effects model showed only minor effect of image processing/reconstruction for lesion detection., Conclusions: Residents at the emergency department might benefit from faster reading time without sacrificing lesion detection rate using AIO for trauma CT., Key Points: • Image fusion techniques decrease the reading time of acute trauma CT without sacrificing diagnostic accuracy., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: To evaluate the association between the coronavirus disease 2019 (COVID-19) and post-inflammatory emphysematous lung alterations on follow-up low-dose CT scans., Methods: Consecutive patients with proven COVID-19 infection and a follow-up CT were retrospectively reviewed. The severity of pulmonary involvement was classified as mild, moderate and severe. Total lung volume, emphysema volume and the ratio of emphysema/-to-lung volume were quantified semi-automatically and compared inter-individually between initial and follow-up CT and to a control group of healthy, age- and sex-matched patients. Lung density was further assessed by drawing circular regions of interest (ROIs) into non-affected regions of the upper lobes., Results: A total of 32 individuals (mean age: 64 ± 13 years, 12 females) with at least one follow-up CT (mean: 52 ± 66 days, range: 5-259) were included. In the overall cohort, total lung volume, emphysema volume and the ratio of lung-to-emphysema volume did not differ significantly between the initial and follow-up scans. In the subgroup of COVID-19 patients with > 30 days of follow-up, the emphysema volume was significantly larger as compared to the subgroup with a follow-up < 30 days (p = 0.045). Manually measured single ROIs generally yielded lower attenuation values prior to COVID-19 pneumonia, but the difference was not significant between groups (all p > 0.05)., Conclusion: COVID-19 patients with a follow-up CT >30 days showed significant emphysematous lung alterations. These findings may help to explain the long-term effect of COVID-19 on pulmonary function and warrant validation by further studies., Competing Interests: The authors have declared that no competing interests exist.

Objectives: To investigate whether virtual monoenergetic images (VMI) and iodine maps derived from spectral detector computed tomography (SDCT) improve early assessment of technique efficacy in patients who underwent microwave ablation (MWA) for hepatocellular carcinoma (HCC) in liver cirrhosis., Methods: This retrospective study comprised 39 patients with 49 HCC lesions treated with MWA. Biphasic SDCT was performed 7.7±4.0 days after ablation. Conventional images (CI), VMI and IM were reconstructed. Signal- and contrast-to-noise ratio (SNR, CNR) in the ablation zone (AZ), hyperemic rim (HR) and liver parenchyma were calculated using regions-of-interest analysis and compared between CI and VMI between 40-100 keV. Iodine concentration and perfusion ratio of HR and residual tumor (RT) were measured. Two readers evaluated subjective contrast of AZ and HR, technique efficacy (complete vs. incomplete ablation) and diagnostic confidence at determining technique efficacy., Results: Attenuation of liver parenchyma, HR and RT, SNR of liver parenchyma and HR, CNR of AZ and HR were significantly higher in low-keV VMI compared to CI (all p<0.05). Iodine concentration and perfusion ratio differed significantly between HR and RT (all p<0.05; e.g. iodine concentration, 1.6±0.5 vs. 2.7±1.3 mg/ml). VMI50keV improved subjective AZ-to-liver contrast, HR-to-liver contrast, visualization of AZ margin and vessels adjacent to AZ compared to CI (all p<0.05). Diagnostic accuracy for detection of incomplete ablation was slightly higher in VMI50keV compared to CI (0.92 vs. 0.89), while diagnostic confidence was significantly higher in VMI50keV (p<0.05)., Conclusions: Spectral detector computed tomography derived low-keV virtual monoenergetic images and iodine maps provide superior early assessment of technique efficacy of MWA in HCC compared to CI., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: David Maintz and Nils Große Hokamp received speaker’s honor from Philips Healthcare. Nils Große Hokamp, David Zopfs and Simon Lennartz received research support unrelated to this project. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All other authors have declared that no competing interest exist.

Background:  Radiological reports of pancreatic lesions are currently widely formulated as free texts. However, for optimal characterization, staging and operation planning, a wide range of information is required but is sometimes not captured comprehensively. Structured reporting offers the potential for improvement in terms of completeness, reproducibility and clarity of interdisciplinary communication., Method:  Interdisciplinary consensus finding of structured report templates for solid and cystic pancreatic tumors in computed tomography (CT) and magnetic resonance imaging (MRI) with representatives of the German Society of Radiology (DRG), German Society for General and Visceral Surgery (DGAV), working group Oncological Imaging (ABO) of the German Cancer Society (DKG) and other radiologists, oncologists and surgeons., Results:  Among experts in the field of pancreatic imaging, oncology and pancreatic surgery, as well as in a public online survey, structured report templates were developed by consensus. These templates are available on the DRG homepage under www.befundung.drg.de and will be regularly revised to the current state of scientific knowledge by the participating specialist societies and responsible working groups., Conclusion:  This article presents structured report templates for solid and cystic pancreatic tumors to improve clinical staging (cTNM, ycTNM) in everyday radiology., Key Points:   · Structured report templates offer the potential of optimized radiological reporting with regard to completeness, reproducibility and differential diagnosis.. · This article presents consensus-based, structured reports for solid and cystic pancreatic lesions in CT and MRI.. · These structured reports are available open source on the homepage of the German Society of Radiology (DRG) under www.befundung.drg.de.., Citation Format: · Persigehl T, Baumhauer M, Baeßler B et al. Structured Reporting of Solid and Cystic Pancreatic Lesions in CT and MRI: Consensus-Based Structured Report Templates of the German Society of Radiology (DRG). Fortschr Röntgenstr 2020; 192: 641 - 655., Competing Interests: The authors declare that they have no conflict of interest., (© Georg Thieme Verlag KG Stuttgart · New York.)

Objectives: To evaluate the correlation between simple planimetric measurements in axial computed tomography (CT) slices and measurements of patient body composition and anthropometric data performed with bioelectrical impedance analysis (BIA) and metric clinical assessments., Methods: In this prospective cross-sectional study, we analyzed data of a cohort of 62 consecutive, untreated adult patients with advanced malignant melanoma who underwent concurrent BIA assessments at their radiologic baseline staging by CT between July 2016 and October 2017. To assess muscle and adipose tissue mass, we analyzed the areas of the paraspinal muscles as well as the cross-sectional total patient area in a single CT slice at the height of the third lumbar vertebra. These measurements were subsequently correlated with anthropometric (body weight) and body composition parameters derived from BIA (muscle mass, fat mass, fat-free mass, and visceral fat mass). Linear regression models were built to allow for estimation of each parameter based on CT measurements., Results: Linear regression models allowed for accurate prediction of patient body weight (adjusted R 2 = 0.886), absolute muscle mass (adjusted R 2 = 0.866), fat-free mass (adjusted R 2 = 0.855), and total as well as visceral fat mass (adjusted R 2 = 0.887 and 0.839, respectively)., Conclusions: Our data suggest that patient body composition can accurately and quantitatively be determined by using simple measurements in a single axial CT slice. This could be useful in various medical and scientific settings, where the knowledge of the patient's anthropometric parameters is not immediately or easily available., Key Points: • Easy to perform measurements on a single CT slice highly correlate with clinically valuable parameters of body composition. • Body composition data were acquired using bioelectrical impedance analysis to correlate CT measurements with a non-imaging-based method, which is frequently lacking in previous studies. • The obtained equations facilitate a quick, opportunistic assessment of relevant parameters of body composition.

Objectives: To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning., Methods: 200 kidney stones with a known composition as determined by infrared spectroscopy were examined using a non-anthropomorphic phantom on a spectral detector computed tomography scanner. Stones were of either pure (monocrystalline, n = 116) or compound (dicrystalline, n = 84) composition. Image acquisition was repeated twice using both, normal and low-dose protocols, respectively (ND/LD). Conventional images and low and high keV virtual monoenergetic images were reconstructed. Stones were semi-automatically segmented. A shallow neural network was trained using data from ND1 acquisition split into training (70%), testing (15%) and validation-datasets (15%). Performance for ND2 and both LD acquisitions was tested. Accuracy on a per-voxel and a per-stone basis was calculated., Results: Main components were: Whewellite (n = 80), weddellite (n = 21), Ca-phosphate (n = 39), cysteine (n = 20), struvite (n = 13), uric acid (n = 18) and xanthine stones (n = 9). Stone size ranged from 3 to 18 mm. Overall accuracy for predicting the main component on a per-voxel basis attained by ND testing dataset was 91.1%. On independently tested acquisitions, accuracy was 87.1-90.4%., Conclusions: Even in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol., Key Points: • Spectral Detector Dual Energy CT and Machine Learning allow for an accurate prediction of stone composition. • Ex-vivo study demonstrates the dose independent assessment of pure and compound stones. • Lowest accuracy is reported for compound stones with struvite as main component.

Objectives: To compare objective and subjective image quality between low keV virtual monoenergetic images (VMI) of the excretory phase and conventional venous phase images derived from spectral dual-energy CT (DECT) in the assessment of urothelial carcinoma., Methods: 26 consecutive patients with histologically confirmed urothelial carcinoma who received clinically indicated venous- and excretory phase abdominal CT scans were included retrospectively. Attenuation, image noise as well as signal- and contrast-to-noise-ratio (SNR, CNR) in venous and excretory phase CT and excretory phase VMI from 40 to 70 keV were obtained from ROI-based measurements in the following regions: urothelial carcinoma, liver, pancreas, renal cortex, subcutaneous fat, renal vein/artery, portal vein, urinary bladder wall, lymph nodes, prostate/uterus. Subjective vessel contrast and delineation of primary tumor manifestations and distant metastases were rated on 5-point Likert scales., Results: In comparison to venous phase CT, attenuation and SNR in excretory phase VMI40keV were higher (p < 0.001), except for liver parenchyma, where they were comparable (p = 0.07 and p = 0.17, respectively). Regarding image noise, no significant difference was found between venous phase CT and excretory phase VMI40keV (p-range: 0.08-1.00), except for liver, portal vein and renal artery, where it was lower in VMI40keV (p < 0.05). CNR of urothelial carcinoma to circumjacent bladder wall was significantly higher in excretory phase VMI40keV compared to venous phase CT. Subjective vessel contrast and delineation of primary tumor and distant metastases received equivalent or higher Likert scores in excretory phase VMI40keV than in venous phase CT., Conclusion: This feasibility study indicates that in the assessment of urothelial carcinoma, virtual monoenergetic excretory phase images at 40 keV acquired with spectral DECT could be feasible to maintain subjective and objective image quality as provided by conventional venous phase images. Still, equivalence with regards to metastatic lesion detection requires further investigation before employing this technique in a potential signal-scan, single-bolus approach., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Purpose: To evaluate quantitative iodine density mapping (IDM) with spectral detector computed tomography (SDCT) as a quantitative biomarker for separation of vertebral trabecular bone metastases (BM) from healthy-appearing trabecular bone (HTB)., Materials and Methods: IRB-approved retrospective single-center-study of portal venous SDCT datasets acquired between June 2016 and March 2017. Inclusion of 43 consecutive cancer patients with BM and 40 without. Target lesions and non-affected control vertebrae were defined using follow-up imaging, MRI, and/or bone scintigraphy. ID and standard deviation were determined with ROI measures by two readers in (a) bone metastases, (b) HTB of BM patients and controls, and (c) ID of various vessels. Volumetric bone mineral density (vBMD) of the lumbar spine and age were recorded. Multivariate ROC analyses und Wilcoxon test were used to determine thresholds for separation of BM and HTB. p < 0.05 was considered significant., Results: ID measurements of 40 target lesions and 83 reference measurements of HTB were acquired. Age (p < 0.0001) and vBMD (p < 0.05) affected ID measurements independently in multivariate models. There were significant differences of ID between metastases (n = 43) and HTB ID (n = 124; mean 5.5 ± 0.9 vs. 3.5 ± 0.9; p < 0.0001), however, with considerable overlap. In univariate analysis, increased ID discriminated bone lesions (AUC 0.90) with a maximum combined specificity/sensitivity of 77.5%/90.7% when applying a threshold of 4.5 mg/ml. Multivariate regression models improved significantly when considering vBMD, the noise of ID, and vertebral venous ID (AUC 0.98)., Conclusion: IDM of SDCT yielded a statistical separation of vertebral bone lesions and HTB. Adjustment for confounders such as age and lumbar vBMD as well as for vertebral venous ID and lesion heterogeneity improved discrimination of trabecular lesions., Key Points: • SDCT iodine density mapping provides the possibility for quantitative analysis of iodine uptake in tissue, which allows to differentiate bone lesions from healthy bone marrow. • Age and vBMD have a significant impact on iodine density measurements. • Iodine density measured in SDCT yielded highest sensitivity and specificity for the statistical differentiation of vertebral trabecular metastases and healthy trabecular bone using an iodine density threshold of 4.5 mg/ml (most performant)-5.0 mg/ml (optimized for specificity).

Purpose: To investigate radiation exposure in computed tomography (CT)-guided interventions, to establish reference levels for exposure, and to discuss strategies for dose reduction., Materials and Methods: We analyzed 1576 consecutive CT-guided procedures in 1284 patients performed over 4.5 years, including drainage placements; biopsies of different organs; radiofrequency and microwave ablations (RFA/MWA) of liver, bone, and lung tumors; pain blockages, and vertebroplasties. Data were analyzed with respect to scanner settings, overall radiation doses, and individual doses of planning CT series, CT intervention, and control CT series., Results: Eighty-five percent of the total radiation dose was applied during the pre- and post-interventional CT series, leaving only 15% applied by the CT-guided intervention itself. Single slice acquisition was associated with lower doses than continuous CT-fluoroscopy (37 mGy cm vs. 153 mGy cm, p<0.001). The third quartile of radiation doses varied considerably for different interventions. The highest doses were observed in complex interventions like RFA/MWA of the liver, followed by vertebroplasty and RFA/MWA of the lung., Conclusions: This paper suggests preliminary reference levels for various intervention types and discusses strategies for dose reduction. A multicenter registry of radiation exposure including a broader spectrum of scanners and intervention types is needed to develop definitive reference levels., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)

Objectives: In chemotherapy monitoring, an estimation of the change in tumour size is an important criterion for the assessment of treatment success. This requires a comparison between corresponding lesions in the baseline and follow-up computed tomography (CT) examinations. We evaluate the clinical benefits of an automatic lesion tracking tool that identifies the target lesions in the follow-up CT study and pre-computes the lesion volumes., Methods: Four radiologists performed volumetric follow-up examinations for 52 patients with and without lesion tracking. In total, 139 lung nodules, liver metastases and lymph nodes were given as target lesions. We measured reading time, inter-reader variability in lesion identification and volume measurements, and the amount of manual adjustments of the segmentation results., Results: With lesion tracking, target lesion assessment time decreased by 38 % or 22 s per lesion. Relative volume difference between readers was reduced from 0.171 to 0.1. Segmentation quality was comparable with and without lesion tracking., Conclusions: Our automatic lesion tracking tool can make interpretation of follow-up CT examinations quicker and provide results that are less reader-dependent., Key Points: Computed tomography is widely used to follow-up lesions in oncological patients. Novel software automatically identifies and measures target lesions in oncological follow-up examinations. This enables a reduction of target lesion assessment. The automated measurements are less reader-dependent.