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Objectives To evaluate the predictive value of volumetric bone mineral density (BMD) assessment of the lumbar spine derived from phantomless dual-energy CT (DECT)-based volumetric material decomposition as an indicator for the 2-year occurrence risk of osteoporosis-associated fractures. Methods L1 of 92 patients (46 men, 46 women; mean age, 64 years, range, 19–103 years) who had undergone third-generation dual-source DECT between 01/2016 and 12/2018 was retrospectively analyzed. For phantomless BMD assessment, dedicated DECT postprocessing software using material decomposition was applied. Digital files of all patients were sighted for 2 years following DECT to obtain the incidence of osteoporotic fractures. Receiver operating characteristic (ROC) analysis was used to calculate cut-off values and logistic regression models were used to determine associations of BMD, sex, and age with the occurrence of osteoporotic fractures. Results A DECT-derived BMD cut-off of 93.70 mg/cm 3 yielded 85.45% sensitivity and 89.19% specificity for the prediction to sustain one or more osteoporosis-associated fractures within 2 years after BMD measurement. DECT-derived BMD was significantly associated with the occurrence of new fractures (odds ratio of 0.8710, 95% CI, 0.091–0.9375, p  < .001), indicating a protective effect of increased DECT-derived BMD values. Overall AUC was 0.9373 (CI, 0.867–0.977, p  < .001) for the differentiation of patients who sustained osteoporosis-associated fractures within 2 years of BMD assessment. Conclusions Retrospective DECT-based volumetric BMD assessment can accurately predict the 2-year risk to sustain an osteoporosis-associated fracture in at-risk patients without requiring a calibration phantom. Lower DECT-based BMD values are strongly associated with an increased risk to sustain fragility fractures. Key Points •Dual-energy CT–derived assessment of bone mineral density can identify patients at risk to sustain osteoporosis-associated fractures with a sensitivity of 85.45% and a specificity of 89.19%. •The DECT-derived BMD threshold for identification of at-risk patients lies above the American College of Radiology (ACR) QCT guidelines for the identification of osteoporosis (93.70 mg/cm 3 vs 80 mg/cm 3 ).

Objectives To compare dual-energy CT (DECT) and MRI for assessing presence and extent of traumatic bone marrow edema (BME) and fracture line depiction in acute vertebral fractures. Methods Eighty-eight consecutive patients who underwent dual-source DECT and 3-T MRI of the spine were retrospectively analyzed. Five radiologists assessed all vertebrae for presence and extent of BME and for identification of acute fracture lines on MRI and, after 12 weeks, on DECT series. Additionally, image quality, image noise, and diagnostic confidence for overall diagnosis of acute vertebral fracture were assessed. Quantitative analysis of CT numbers was performed by a sixth radiologist. Two radiologists analyzed MRI and grayscale DECT series to define the reference standard. Results For assessing BME presence and extent, DECT showed high sensitivity (89% and 84%, respectively) and specificity (98% in both), and similarly high diagnostic confidence compared to MRI (2.30 vs. 2.32; range 0–3) for the detection of BME ( p = .72). For evaluating acute fracture lines, MRI achieved high specificity (95%), moderate sensitivity (76%), and a significantly lower diagnostic confidence compared to DECT (2.42 vs. 2.62, range 0–3) ( p < .001). A cutoff value of − 0.43 HU provided a sensitivity of 89% and a specificity of 90% for diagnosing BME, with an overall AUC of 0.96. Conclusions DECT and MRI provide high diagnostic confidence and image quality for assessing acute vertebral fractures. While DECT achieved high overall diagnostic accuracy in the analysis of BME presence and extent, MRI provided moderate sensitivity and lower confidence for evaluating fracture lines. Key Points • In the setting of spinal trauma, dual-energy CT (DECT) is highly accurate in the evaluation of acute vertebral fractures and bone marrow edema presence and extent. • MRI provides moderate sensitivity and lower diagnostic confidence for the depiction of acute fracture lines, when compared to DECT, which might result in potentially inaccurate and underestimated severity assessment of injuries in certain cases when no fracture lines are visible on MRI. • DECT may represent a valid imaging alternative to MRI in specific settings of acute spinal trauma and in follow-up examinations, especially in elderly or unstable patients and in cases of subtle or complex orientated fracture lines.

ObjectivesTo evaluate the diagnostic performance of a dual-energy computed tomography (DECT)-based technique using iodine quantification and fat fraction analysis for the diagnosis of early acute pancreatitisMethodsIn this retrospective study, 45 patients (35 men and 10 women; mean age, 54.9 ± 14.0 years) with early acute pancreatitis were included. Serum lipase levels and follow-up examinations served as the reference standard. A matched control group (n = 45) was assembled for evaluation of material decomposition values of normal pancreatic parenchyma. Three blinded radiologists independently interpreted all cases on conventional grayscale DECT series. In addition, readers re-evaluated all cases by manually performing region-of-interest (ROI) measurements on pancreatic-phase DECT material density images of the head, body, and tail of each patient’s pancreas. Receiver operating characteristic (ROC) curve analysis was performed to estimate the optimal threshold for discriminating between inflammatory and normal pancreas parenchyma.ResultsDECT-based iodine density values showed significant differences between inflammatory (1.8 ± 0.3 mg/mL) and normal pancreatic parenchyma (2.7 ± 0.7 mg/mL) (p ≤ 0.01). Fat fraction measurements showed no significant differences (p = 0.08). The optimal iodine density threshold for the diagnosis of acute pancreatitis was 2.1 mg/mL with a sensitivity of 96% and specificity of 77%. Iodine quantification revealed an area under the curve (AUC) of 0.86, significantly higher compared to standard image evaluation of the radiologists (AUC, 0.80; sensitivity, 78%; specificity, 82%) (p < 0.01).ConclusionDECT using iodine quantification allows for diagnosis of early acute pancreatitis with higher sensitivity compared to standard image evaluation.Key Points• Iodine density values showed significant differences between inflammatory and normal pancreatic parenchyma.• DECT using iodine quantification allows for diagnosis of early acute pancreatitis.• An iodine density of ≤ 2.1 mg/mL optimizes the diagnosis of acute pancreatitis.

Objectives To investigate the diagnostic accuracy of color-coded dual-energy CT virtual non-calcium (VNCa) reconstructions for the assessment of bone marrow edema (BME) of the scaphoid in patients with acute wrist trauma. Methods Our retrospective study included data from 141 patients (67 women, 74 men; mean age 43 years, range 19–80 years) with acute wrist trauma who had undergone third-generation dual-source dual-energy CT and 3-T MRI within 7 days. Eight weeks after assessment of conventional grayscale dual-energy CT scans for the presence of fractures, corresponding color-coded VNCa reconstructions were independently analyzed by the same six radiologists for the presence of BME. CT numbers on VNCa reconstructions were evaluated by a seventh radiologist. Consensus reading of MRI series by two additional radiologists served as the reference standard. Results MRI depicted 103 scaphoideal zones with BME in 76 patients. On qualitative analysis, VNCa images yielded high overall sensitivity (580/618 [94%]), specificity (1880/1920 [98%]), and accuracy (2460/2538 [97%]) for assessing BME as compared with MRI as reference standard. The interobserver agreement was excellent ( κ  = 0.98). CT numbers derived from VNCa images were significantly different in zones with and without edema ( p  < 0.001). A cutoff value of – 46 Hounsfield units provided a sensitivity of 91% and specificity of 97% for differentiating edematous scaphoid lesions. Receiver operating characteristic curve analysis revealed an overall area under the curve of 0.98. Conclusions Qualitative and quantitative analyses showed excellent diagnostic accuracy of color-coded VNCa reconstructions for assessing traumatic BME of the scaphoid compared to MRI. Key Points • Color-coded virtual non-calcium (VNCa) reconstructions yield excellent diagnostic accuracy in assessing bone marrow edema of the scaphoid. • VNCa imaging enables detection of non-displaced fractures that are occult on standard grayscale CT. • Diagnostic confidence is comparable between VNCa imaging and MRI.

Objectives To evaluate the performance of radiomic features extracted from high-resolution computed tomography (HRCT) for the differentiation between cholesteatoma and middle ear inflammation (MEI), and to investigate the impact of post-reconstruction harmonization and data resampling. Methods One hundred patients were included in this retrospective dual-center study: 48 with histology-proven cholesteatoma (center A: 23; center B: 25) and 52 with MEI (A: 27; B: 25). Radiomic features (co-occurrence and run-length matrix, absolute gradient, autoregressive model, Haar wavelet transform) were extracted from manually defined 2D-ROIs. The ten best features for lesion differentiation were selected using probability of error and average correlation coefficients. A multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used for radiomics-based classification, with histopathology serving as the reference standard (70% of cases for training, 30% for validation). The analysis was performed five times each on (a) unmodified data and on data that were (b) resampled to the same matrix size, and (c) corrected for acquisition protocol differences using ComBat harmonization. Results Using unmodified data, the MLP-ANN classification yielded an overall median area under the receiver operating characteristic curve (AUC) of 0.78 (0.72–0.84). Using original data from center A and resampled data from center B, an overall median AUC of 0.88 (0.82–0.99) was yielded, while using ComBat harmonized data, an overall median AUC of 0.89 (0.79–0.92) was revealed. Conclusion Radiomic features extracted from HRCT differentiate between cholesteatoma and MEI. When using multi-centric data obtained with differences in CT acquisition parameters, data resampling and ComBat post-reconstruction harmonization clearly improve radiomics-based lesion classification. Key Points • Unenhanced high-resolution CT coupled with radiomics analysis may be useful for the differentiation between cholesteatoma and middle ear inflammation. • Pooling of data extracted from inhomogeneous CT datasets does not appear meaningful without further post-processing. • When using multi-centric CT data obtained with differences in acquisition parameters, post-reconstruction harmonization and data resampling clearly improve radiomics-based soft-tissue differentiation.

Background Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method. Methods Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method. Results Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p  < 0.001). The correlation between AI-derived BA and reference BA ( r  = 0.99) was significantly higher than between reader-calculated BA and reference BA ( r  = 0.90; p  < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender ( p =  0.241). Mean reading times were reduced by 87% using the AI system. Conclusions A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.

Background Dual-source dual-energy computed tomography (DECT) offers the potential for opportunistic osteoporosis screening by enabling phantomless bone mineral density (BMD) quantification. This study sought to assess the accuracy and precision of volumetric BMD measurement using dual-source DECT in comparison to quantitative CT (QCT). Methods A validated spine phantom consisting of three lumbar vertebra equivalents with 50 (L1), 100 (L2), and 200 mg/cm 3 (L3) calcium hydroxyapatite (HA) concentrations was scanned employing third-generation dual-source DECT and QCT. While BMD assessment based on QCT required an additional standardised bone density calibration phantom, the DECT technique operated by using a dedicated postprocessing software based on material decomposition without requiring calibration phantoms. Accuracy and precision of both modalities were compared by calculating measurement errors. In addition, correlation and agreement analyses were performed using Pearson correlation, linear regression, and Bland-Altman plots. Results DECT-derived BMD values differed significantly from those obtained by QCT ( p < 0.001) and were found to be closer to true HA concentrations. Relative measurement errors were significantly smaller for DECT in comparison to QCT (L1, 0.94% versus 9.68%; L2, 0.28% versus 5.74%; L3, 0.24% versus 3.67%, respectively). DECT demonstrated better BMD measurement repeatability compared to QCT (coefficient of variance < 4.29% for DECT, < 6.74% for QCT). Both methods correlated well to each other ( r = 0.9993; 95% confidence interval 0.9984–0.9997; p < 0.001) and revealed substantial agreement in Bland-Altman plots. Conclusions Phantomless dual-source DECT-based BMD assessment of lumbar vertebra equivalents using material decomposition showed higher diagnostic accuracy compared to QCT.

Objectives To determine the diagnostic accuracy of dual-energy CT (DECT) virtual noncalcium (VNCa) reconstructions for assessing thoracic disk herniation compared to standard grayscale CT. Methods In this retrospective study, 87 patients (1131 intervertebral disks; mean age, 66 years; 47 women) who underwent third-generation dual-source DECT and 3.0-T MRI within 3 weeks between November 2016 and April 2020 were included. Five blinded radiologists analyzed standard DECT and color-coded VNCa images after a time interval of 8 weeks for the presence and degree of thoracic disk herniation and spinal nerve root impingement. Consensus reading of independently evaluated MRI series served as the reference standard, assessed by two separate experienced readers. Additionally, image ratings were carried out by using 5-point Likert scales. Results MRI revealed a total of 133 herniated thoracic disks. Color-coded VNCa images yielded higher overall sensitivity (624/665 [94%; 95% CI, 0.89–0.96] vs 485/665 [73%; 95% CI, 0.67–0.80]), specificity (4775/4990 [96%; 95% CI, 0.90–0.98] vs 4066/4990 [82%; 95% CI, 0.79–0.84]), and accuracy (5399/5655 [96%; 95% CI, 0.93–0.98] vs 4551/5655 [81%; 95% CI, 0.74–0.86]) for the assessment of thoracic disk herniation compared to standard CT (all p < .001). Interrater agreement was excellent for VNCa and fair for standard CT ( ϰ = 0.82 vs 0.37; p < .001). In addition, VNCa imaging achieved higher scores regarding diagnostic confidence, image quality, and noise compared to standard CT (all p < .001). Conclusions Color-coded VNCa imaging yielded substantially higher diagnostic accuracy and confidence for assessing thoracic disk herniation compared to standard CT. Key Points • Color-coded VNCa reconstructions derived from third-generation dual-source dual-energy CT yielded significantly higher diagnostic accuracy for the assessment of thoracic disk herniation and spinal nerve root impingement compared to standard grayscale CT. • VNCa imaging provided higher diagnostic confidence and image quality at lower noise levels compared to standard grayscale CT. • Color-coded VNCa images may potentially serve as a viable imaging alternative to MRI under circumstances where MRI is unavailable or contraindicated.

We aimed to identify hepatocellular carcinoma (HCC) patients who will respond to repetitive transarterial chemoembolization (TACE) to improve the treatment algorithm. Retrospectively, 61 patients (mean age, 65.3 years ± 10.0 [SD]; 49 men) with 94 HCC mRECIST target-lesions who had three consecutive TACE between 01/2012 and 01/2020 were included. Robust and non-redundant radiomics features were extracted from the 24 h post-embolization CT. Five different clinical TACE-scores were assessed. Seven different feature selection methods and machine learning models were used. Radiomics, clinical and combined models were built to predict response to TACE on a lesion-wise and patient-wise level as well as its impact on overall-survival prognostication. 29 target-lesions of 19 patients were evaluated in the test set. Response rates were 37.9% (11/29) on the lesion-level and 42.1% (8/19) on the patient-level. Radiomics top lesion-wise response prognostications was AUC 0.55–0.67. Clinical scores revealed top AUCs of 0.65–0.69. The best working model combined the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical score mHAP_II_score_group with AUC = 0.70, accuracy = 0.72. We transferred this model on a patient-level to achieve AUC = 0.62, CI = 0.41–0.83. The two radiomics-clinical features revealed overall-survival prognostication of C-index = 0.67. In conclusion, a random forest model using the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical mHAP-II-score-group seems promising for TACE response prognostication.

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.