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
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.
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).
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.
In this study, 100 lung cancer patients underwent a contrast-enhanced
F-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).
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:
< 0.001, z = 8.8 and z = 22.4; SBS:
< 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively).
Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.
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.
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.
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
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 β=0.50) and portal vein iodine blood pool (p <0.001, mean β=0.43) mediated the strongest impact. Based thereon, adjusted reference values were calculated.
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.
Background
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.
Background
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
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.
OBJECTIVESDual-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 METHODSA 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.
RESULTSThe 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).
CONCLUSIONSOur 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.
Objectives
Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). ...The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence.
Methods
We included
n
= 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (
n
= 991 CT scans;
n
= 462 COVID-19, and
n
= 529 CAP) from three centers in China. An independent Chinese and German test dataset of
n
= 600 CT scans from six centers (COVID-19 / CAP;
n
= 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans;
n
= 90 each) was used to evaluate the radiologists’ performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence.
Results
The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists’ diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores.
Conclusion
This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence.
Key Points
• AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence.
• The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.
•Correct determination of the extent of cancer at the time of diagnosis is a key factor for treatment and prognosis.•Detection of lymph node metastases and their assignment to specific levels is ...crucial for an accurate TNM classification.•The proposed AI approach for automated LN levels mapping in chest CT showed mean classification accuracies of up to 96.10 %.•A N-staging simulated clinical use case scenario assuming lung cancer showed mean classification accuracies of up to 96.14 %.
To develop a deep-learning (DL)-based approach for thoracic lymph node (LN) mapping based on their anatomical location.
The training-and validation-dataset included 89 contrast-enhanced computed tomography (CT) scans of the chest. 4201 LNs were semi-automatically segmented and then assigned to LN levels according to their anatomical location. The LN level classification task was addressed by a multi-class segmentation procedure using a fully convolutional neural network. Mapping was performed by firstly determining potential level affiliation for each voxel and then performing majority voting over all voxels belonging to each LN. Mean classification accuracies on the validation data were calculated separately for each level and overall Top-1, Top-2 and Top-3 scores were determined, where a Top-X score describes how often the annotated class was within the top-X predictions. To demonstrate the clinical applicability of our model, we tested its N-staging capabilities in a simulated clinical use case scenario assuming a patient diseased with lung cancer.
The artificial intelligence(AI)-based assignment revealed mean classification accuracies of 86.36 % (Top-1), 94.48 % (Top-2) and 96.10 % (Top-3). Best accuracies were achieved for LNs in the subcarinal level 7 (98.31 %) and axillary region (98.74 %). The highest misclassification rates were observed among LNs in adjacent levels. The proof-of-principle application in a simulated clinical use case scenario for automated tumor N-staging showed a mean classification accuracy of up to 96.14 % (Top-1).
The proposed AI approach for automatic classification of LN levels in chest CT as well as the proof-of-principle-experiment for automatic N-staging, revealed promising results, warranting large-scale validation for clinical application.
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