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  • 1
    Online Resource
    Online Resource
    Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches—an international, multi-center comparative study
    AME Publishing Company ; 2022
    In:  Quantitative Imaging in Medicine and Surgery Vol. 12, No. 11 ( 2022-11), p. 5156-5170
    Details
    In: Quantitative Imaging in Medicine and Surgery, AME Publishing Company, Vol. 12, No. 11 ( 2022-11), p. 5156-5170
    Type of Medium: Online Resource
    ISSN: 2223-4292 , 2223-4306
    URL: Journal
    URL: Article
    DOI: 10.21037/qims
    DOI: 10.21037/qims-22-175
    Language: Unknown
    Publisher: AME Publishing Company
    Publication Date: 2022
    detail.hit.zdb_id: 2653586-5
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  • 2
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    Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network
    Springer Science and Business Media LLC ; 2023
    In:  European Radiology Experimental Vol. 7, No. 1 ( 2023-07-28)
    Details
    In: European Radiology Experimental, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2023-07-28)
    Abstract: In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes (LNs) on contrast-enhanced computed tomography (CECT) examinations. Methods In this IRB-approved retrospective single-center study, 187 CECT examinations of the head and neck region from patients with various primary diseases were collected from our local database, and 3656 LNs (19.5 ± 14.9 LNs/CECT, mean ± standard deviation) with a short-axis diameter (SAD) ≥ 5 mm were segmented manually by expert physicians. With these data, we trained an independent fully convolutional neural network based on 3D foveal patches. Testing was performed on 30 independent CECTs with 925 segmented LNs with an SAD ≥ 5 mm. Results In total, 4,581 LNs were segmented in 217 CECTs. The model achieved an average localization rate (LR), i.e. , percentage of localized LNs/CECT, of 78.0% in the validation dataset. In the test dataset, average LR was 81.1% with a mean Dice coefficient of 0.71. For enlarged LNs with a SAD ≥ 10 mm, LR was 96.2%. In the test dataset, the false-positive rate was 2.4 LNs/CECT. Conclusions Our trained AI model demonstrated a good overall performance in the consistent automatic localization and 3D segmentation of physiological and metastatic cervical LNs with a SAD ≥ 5 mm on CECTs. This could aid clinical localization and automatic 3D segmentation, which can benefit clinical care and radiomics research. Relevance statement Our AI model is a time-saving tool for 3D segmentation of cervical lymph nodes on contrast-enhanced CT scans and serves as a solid base for N staging in clinical practice and further radiomics research. Key points • Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research. Graphical Abstract
    Type of Medium: Online Resource
    ISSN: 2509-9280
    URL: Article
    DOI: 10.1186/s41747-023-00360-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2905812-0
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  • 3
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    Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-08-12)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-08-12)
    Abstract: Computed tomography in suspected urolithiasis provides information about the presence, location and size of stones. Particularly stone size is a key parameter in treatment decision; however, data on impact of reformatation and measurement strategies is sparse. This study aimed to investigate the influence of different image reformatations, slice thicknesses and window settings on stone size measurements. Reference stone sizes of 47 kidney stones representative for clinically encountered compositions were measured manually using a digital caliper (Man-M). Afterwards stones were placed in a 3D-printed, semi-anthropomorphic phantom, and scanned using a low dose protocol (CTDI vol 2 mGy). Images were reconstructed using hybrid-iterative and model-based iterative reconstruction algorithms (HIR, MBIR) with different slice thicknesses. Two independent readers measured largest stone diameter on axial (2 mm and 5 mm) and multiplanar reformatations (based upon 0.67 mm reconstructions) using different window settings (soft-tissue and bone). Statistics were conducted using ANOVA ± correction for multiple comparisons. Overall stone size in CT was underestimated compared to Man-M (8.8 ± 2.9 vs. 7.7 ± 2.7 mm, p   〈  0.05), yet closely correlated ( r  = 0.70). Reconstruction algorithm and slice thickness did not significantly impact measurements ( p   〉  0.05), while image reformatations and window settings did ( p   〈  0.05). CT measurements using multiplanar reformatation with a bone window setting showed closest agreement with Man-M (8.7 ± 3.1 vs. 8.8 ± 2.9 mm, p   〈  0.05, r  = 0.83). Manual CT-based stone size measurements are most accurate using multiplanar image reformatation with a bone window setting, while measurements on axial planes with different slice thicknesses underestimate true stone size. Therefore, this procedure is recommended when impacting treatment decision.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-95962-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
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  • 4
    Online Resource
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    Monitoring treatment effects in lung cancer-bearing mice: clinical CT and clinical MRI compared to micro-CT
    Springer Science and Business Media LLC ; 2020
    In:  European Radiology Experimental Vol. 4, No. 1 ( 2020-12)
    Details
    In: European Radiology Experimental, Springer Science and Business Media LLC, Vol. 4, No. 1 ( 2020-12)
    Abstract: Compared to histology-based methods, imaging can reduce animal usage in preclinical studies. However, availability of dedicated scanners is limited. We evaluated clinical computed tomography (CT) and magnetic resonance imaging (MRI) in comparison to dedicated CT (micro-CT) for assessing therapy effects in lung cancer-bearing mice. Methods Animals received cisplatin ( n = 10), sham ( n = 12), or no treatment ( n = 9). All were examined via micro-CT, CT, and MRI before and after treatment. Semiautomated tumour burden (TB) calculation was performed. The Bland-Altman, receiver operating characteristic (ROC), and Spearman statistics were used. Results All modalities always allowed localising and measuring TB. At all modalities, mice treated with cisplatin showed a TB reduction ( p ≤ 0.012) while sham-treated and untreated individuals presented tumour growth ( p 〈 0.001). Mean relative difference (limits of agreement) between TB on micro-CT and clinical scanners was 24.7% (21.7–27.7%) for CT and 2.9% (−4.0–9.8%) for MRI. Relative TB changes before/after treatment were not different between micro-CT and CT ( p = 0.074) or MRI ( p = 0.241). Mice with cisplatin treatment were discriminated from those with sham or no treatment at all modalities ( p ≤ 0.001). Using micro-CT as reference standard, ROC areas under the curves were 0.988–1.000 for CT and 0.946–0.957 for MRI. TB changes were highly correlated across modalities ( r ≥ 0.900, p 〈 0.001). Conclusions Clinical CT and MRI are suitable for treatment response evaluation in lung cancer-bearing mice. When dedicated scanners are unavailable, they should be preferred to improve animal welfare.
    Type of Medium: Online Resource
    ISSN: 2509-9280
    URL: Article
    DOI: 10.1186/s41747-020-00160-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2905812-0
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  • 5
    Online Resource
    Online Resource
    Automated mapping and N-Staging of thoracic lymph nodes in contrast-enhanced CT scans of the chest using a fully convolutional neural network
    Elsevier BV ; 2021
    In:  European Journal of Radiology Vol. 139 ( 2021-06), p. 109718-
    Details
    In: European Journal of Radiology, Elsevier BV, Vol. 139 ( 2021-06), p. 109718-
    Type of Medium: Online Resource
    ISSN: 0720-048X
    URL: Article
    DOI: 10.1016/j.ejrad.2021.109718
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2005350-2
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  • 6
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    Online Resource
    Intraindividual Consistency of Iodine Concentration in Dual-Energy Computed Tomography of the Chest and Abdomen
    Ovid Technologies (Wolters Kluwer Health) ; 2021
    In:  Investigative Radiology Vol. 56, No. 3 ( 2021-3), p. 181-187
    Details
    In: Investigative Radiology, Ovid Technologies (Wolters Kluwer Health), Vol. 56, No. 3 ( 2021-3), p. 181-187
    Abstract: Dual-energy computed tomography (DECT)–derived quantification of iodine concentration (IC) is increasingly used in oncologic imaging to characterize lesions and evaluate treatment response. However, only limited data are available on intraindividual consistency of IC and its variation. This study investigates the longitudinal reproducibility of IC in organs, vessels, and lymph nodes in a large cohort of healthy patients who underwent repetitive DECT imaging. Materials and Methods A total of 159 patients, who underwent a total of 469 repetitive (range, 2–4), clinically indicated portal-venous phase DECT examinations of the chest and abdomen, were retrospectively included. At time of imaging, macroscopic tumor burden was excluded by follow-up imaging (≥3 months). Iodine concentration was measured region of interest-based (N = 43) in parenchymatous organs, vessels, lymph nodes, and connective tissue. Normalization of IC to the aorta and to the trigger delay as obtained from bolus tracking was performed. For statistical analysis, intraclass correlation coefficient and modified variation coefficient (MVC) were used to assess intraindividual agreement of IC and its variation between different time points, respectively. Furthermore, t tests and analysis of variance with Tukey-Kramer post hoc test were used. Results The mean intraclass correlation coefficient over all regions of interest was good to excellent (0.642–0.936), irrespective of application of normalization or the normalization technique. Overall, MVC ranged from 1.8% to 25.4%, with significantly lower MVC in data normalized to the aorta (5.8% [1.8%–15.8%]) in comparison with the MVC of not normalized data and data normalized to the trigger delay ( P 〈 0.01 and P = 0.04, respectively). Conclusions Our study confirms intraindividual, longitudinal variation of DECT-derived IC, which varies among vessels, lymph nodes, organs, and connective tissue, following different perfusion characteristics; normalizing to the aorta seems to improve reproducibility when using a constant contrast media injection protocol.
    Type of Medium: Online Resource
    ISSN: 1536-0210 , 0020-9996
    URL: Article
    DOI: 10.1097/RLI.0000000000000724
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2021
    detail.hit.zdb_id: 2041543-6
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  • 7
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    Transfer-Learning Deep Radiomics and Hand-Crafted Radiomics for Classifying Lymph Nodes from Contrast-Enhanced Computed Tomography in Lung Cancer
    MDPI AG ; 2023
    In:  Cancers Vol. 15, No. 10 ( 2023-05-21), p. 2850-
    Details
    In: Cancers, MDPI AG, Vol. 15, No. 10 ( 2023-05-21), p. 2850-
    Abstract: Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. Methods: In this study, 100 lung cancer patients underwent a contrast-enhanced 18F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional “hand-crafted” radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). Results: In total, 2734 lymph nodes [555 (20.3%) PET-positive] from 100 patients [49% female; mean age 65, SD: 14] with lung cancer (60% adenocarcinoma, 21% plate epithelial carcinoma, 8% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865–0.878), SBS 35.8 (34.2–37.2)] and had significantly higher model performance than both approaches alone (AUC: p 〈 0.001, z = 8.8 and z = 22.4; SBS: p 〈 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). Conclusion: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers15102850
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2527080-1
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  • 8
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    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
    Springer Science and Business Media LLC ; 2022
    In:  European Radiology Vol. 32, No. 5 ( 2022-05), p. 2901-2911
    Details
    In: European Radiology, Springer Science and Business Media LLC, Vol. 32, No. 5 ( 2022-05), p. 2901-2911
    Abstract: 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).
    Type of Medium: Online Resource
    ISSN: 1432-1084
    URL: Article
    DOI: 10.1007/s00330-021-08419-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1472718-3
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  • 9
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    AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study
    Springer Science and Business Media LLC ; 2022
    In:  European Radiology Vol. 33, No. 6 ( 2022-12-16), p. 4280-4291
    Details
    In: European Radiology, Springer Science and Business Media LLC, Vol. 33, No. 6 ( 2022-12-16), p. 4280-4291
    Type of Medium: Online Resource
    ISSN: 1432-1084
    URL: Article
    DOI: 10.1007/s00330-022-09335-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1472718-3
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  • 10
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    DataSheet_1.docx
    Frontiers Media SA ; 2023
    In:  Frontiers in Endocrinology Vol. 14 ( 2023-5-19)
    Details
    In: Frontiers in Endocrinology, Frontiers Media SA, Vol. 14 ( 2023-5-19)
    Abstract: 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 (β) 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 95 th 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 & lt;0.001, mean β=0.50) and portal vein iodine blood pool (p & lt;0.001, mean β=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.
    Type of Medium: Online Resource
    ISSN: 1664-2392
    URL: Article
    URL: Article
    DOI: 10.3389/fendo.2023.1098898
    DOI: 10.3389/fendo.2023.1098898.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2592084-4
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