European Radiology Experimental最新文献

Background: Anthropogenic gadolinium (Gd), originating from Gd-based contrast agents (GBCAs) used in magnetic resonance imaging (MRI), is widely identified in the aquatic environment with concerns about toxicity and accumulation. We aimed to present new data on anthropogenic Gd in the Tone River, which has the largest drainage area in Japan, and then to compare the current data with those obtained in 1996.

Methods: The water samples were collected on August 9-10, 2020, at 15 different locations of the Tone River in Japan. The concentrations of the rare earth elements (REEs) were measured by inductively coupled plasma-mass spectrometry and normalized to Post-Archean Australian Shale to construct shale-normalized REE patterns. The degree of Gd-anomaly was defined as the percentage of anthropogenic Gd to the geogenic background and used to compare the water samples from different locations. Pearson's correlation coefficients were calculated.

Results: All the samples displayed positive Gd anomalies. The Gd-anomaly ranged from 121 to 6,545% and displayed a repeating decrease-and-increase trend. The Gd-anomaly showed strong positive correlations to the number of hospitals (r = 0.88; p < 0.001) and their MRI units (r = 0.89; p < 0.001).

Conclusions: Our study revealed notable anomalies of Gd concentrations in river water in Japan, with strong positive correlations to the number of major hospitals and their MRI units. Compared with the previous report in 2000, the Gd-anomaly in Tone River increased from 851% (sampled in 1996) to 6,545%, i.e., 7.7 times, reflecting the increased use of GBCAs in hospitals.

Relevance statement: Notable Gd concentration anomalies in river water in Japan were observed. This result underlines the importance of more extensive research on anthropogenic gadolinium, and investigations of risks to human health as well as the development of effective removal technologies may be necessary.

Key points: • All water samples from Tone River displayed positive Gd anomalies. • The Gd anomalies increased to 7.7 times higher over the past 24 years. • Correlations between Gd values and the number of hospitals and MRI units were observed.

Background: Clinical magnetic resonance imaging (MRI) studies often use Cartesian gradient-echo (GRE) sequences with ~2-ms echo times (TEs) to monitor apparent total sodium concentration (aTSC). We compared Cartesian GRE and ultra-short echo time three-dimensional (3D) radial-readout sequences for measuring skeletal muscle aTSC.

Methods: We retrospectively evaluated 211 datasets from 112 volunteers aged 62.3 ± 12.1 years (mean ± standard deviation), acquired at 3 T from the lower leg. For ²³Na MRI acquisitions, we used a two-dimensional Cartesian GRE sequence and a density-adapted 3D radial readout sequence with cuboid field-of-view (DA-3D-RAD-C). We calibrated the ²³Na MR signal using reference tubes either with or without agarose and subsequently performed a relaxation correction. Additionally, we employed a six-echo ¹H GRE sequence and a multi-echo spin-echo sequence to calculate proton density fat fraction (PDFF) and water T2. Paired Wilcoxon signed-rank test, Cohen d_z for paired samples, and Spearman correlation were used.

Results: Relaxation correction effectively reduced the differences in muscle aTSC between the two acquisition and calibration methods (DA-3D-RAD-C using NaCl/agarose references: 20.05 versus 19.14 mM; d_z = 0.395; Cartesian GRE using NaCl/agarose references: 19.50 versus 18.82 mM; d_z = 0.427). Both aTSC of the DA-3D-RAD-C and Cartesian GRE acquisitions showed a small but significant correlation with PDFF as well as with water T2.

Conclusions: Different ²³Na MRI acquisition and calibration approaches affect aTSC values. Applying relaxation correction is advised to minimize the impact of sequence parameters on quantification, and considering additional fat correction is advisable for patients with increased fat fractions.

Relevance statement: This study highlights relaxation correction's role in improving sodium MRI accuracy, paving the way for better disease assessment and comparability of measured sodium signal in patients.

Key points: • Differences in MRI acquisition methods hamper the comparability of sodium MRI measurements. • Measured sodium values depend on used MRI sequences and calibration method. • Relaxation correction during postprocessing mitigates these discrepancies. • Thus, relaxation correction enhances accuracy of sodium MRI, aiding its clinical use.

Background: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR).

Methods: Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm³ and 101-300 mm³ were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups.

Results: Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm³ nodules in non-emphysema (p = 0.009).

Conclusions: AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR.

Relevance statement: In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs.

Key points: • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.

Background: We investigated the potential of an imaging-aware GPT-4-based chatbot in providing diagnoses based on imaging descriptions of abdominal pathologies.

Methods: Utilizing zero-shot learning via the LlamaIndex framework, GPT-4 was enhanced using the 96 documents from the Radiographics Top 10 Reading List on gastrointestinal imaging, creating a gastrointestinal imaging-aware chatbot (GIA-CB). To assess its diagnostic capability, 50 cases on a variety of abdominal pathologies were created, comprising radiological findings in fluoroscopy, MRI, and CT. We compared the GIA-CB to the generic GPT-4 chatbot (g-CB) in providing the primary and 2 additional differential diagnoses, using interpretations from senior-level radiologists as ground truth. The trustworthiness of the GIA-CB was evaluated by investigating the source documents as provided by the knowledge-retrieval mechanism. Mann-Whitney U test was employed.

Results: The GIA-CB demonstrated a high capability to identify the most appropriate differential diagnosis in 39/50 cases (78%), significantly surpassing the g-CB in 27/50 cases (54%) (p = 0.006). Notably, the GIA-CB offered the primary differential in the top 3 differential diagnoses in 45/50 cases (90%) versus g-CB with 37/50 cases (74%) (p = 0.022) and always with appropriate explanations. The median response time was 29.8 s for GIA-CB and 15.7 s for g-CB, and the mean cost per case was $0.15 and $0.02, respectively.

Conclusions: The GIA-CB not only provided an accurate diagnosis for gastrointestinal pathologies, but also direct access to source documents, providing insight into the decision-making process, a step towards trustworthy and explainable AI. Integrating context-specific data into AI models can support evidence-based clinical decision-making.

Relevance statement: A context-aware GPT-4 chatbot demonstrates high accuracy in providing differential diagnoses based on imaging descriptions, surpassing the generic GPT-4. It provided formulated rationale and source excerpts supporting the diagnoses, thus enhancing trustworthy decision-support.

Key points: • Knowledge retrieval enhances differential diagnoses in a gastrointestinal imaging-aware chatbot (GIA-CB). • GIA-CB outperformed the generic counterpart, providing formulated rationale and source excerpts. • GIA-CB has the potential to pave the way for AI-assisted decision support systems.

Background: This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion-diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability.

Methods: Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland-Altman analysis. Feature importance was determined for explainability.

Results: In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6-157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1-144.9 mL). These PVs had a volume similarity of 0.88 (0.79-0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland-Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature.

Conclusions: Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings.

Relevance statement: The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting.

Key points: • Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Background: Chondrosarcomas are rare malignant bone tumors diagnosed by analyzing radiological images and histology of tissue biopsies and evaluating features such as matrix calcification, cortical destruction, trabecular penetration, and tumor cell entrapment.

Methods: We retrospectively analyzed 16 cartilaginous tumor tissue samples from three patients (51-, 54-, and 70-year-old) diagnosed with a dedifferentiated chondrosarcoma at the femur, a moderately differentiated chondrosarcoma in the pelvis, and a predominantly moderately differentiated chondrosarcoma at the scapula, respectively. We combined a hematein-based x-ray staining with high-resolution three-dimensional (3D) microscopic x-ray computed tomography (micro-CT) for nondestructive 3D tumor assessment and tumor margin evaluation.

Results: We detected trabecular entrapment on 3D micro-CT images and followed bone destruction throughout the volume. In addition to staining cell nuclei, hematein-based staining also improved the visualization of the tumor matrix, allowing for the distinction between the tumor and the bone marrow cavity. The hematein-based staining did not interfere with further conventional histology. There was a 5.97 ± 7.17% difference between the relative tumor area measured using micro-CT and histopathology (p = 0.806) (Pearson correlation coefficient r = 0.92, p = 0.009). Signal intensity in the tumor matrix (4.85 ± 2.94) was significantly higher in the stained samples compared to the unstained counterparts (1.92 ± 0.11, p = 0.002).

Conclusions: Using nondestructive 3D micro-CT, the simultaneous visualization of radiological and histopathological features is feasible.

Relevance statement: 3D micro-CT data supports modern radiological and histopathological investigations of human bone tumor specimens. It has the potential for being an integrative part of clinical preoperative diagnostics.

Key points: • Matrix calcifications are a relevant diagnostic feature of bone tumors. • Micro-CT detects all clinically diagnostic relevant features of x-ray-stained chondrosarcoma. • Micro-CT has the potential to be an integrative part of clinical diagnostics.

Background: We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques.

Methods: Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used.

Results: Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001).

Conclusions: Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations.

Trial registration: ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes.

Key points: • Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.

Objective: Guyon's canal syndrome is caused by compression of the ulnar nerve at the wrist, occasionally requiring decompression surgery. In recent times, minimally invasive approaches have gained popularity. The aim of this study was to assess the efficacy and safety of ultrasound-guided thread release for transecting the palmar ligament in Guyon's canal without harming surrounding structures, in a cadaveric specimen model.

Methods: After ethical approval, thirteen ultrasound-guided thread releases of Guyon's canal were performed on the wrists of softly embalmed anatomic specimens. Cadavers showing injuries or prior operations at the hand were excluded. Subsequently, the specimens were dissected, and the outcome of the interventions and potential damage to adjacent anatomical structures as well as ultrasound visibility were evaluated with a score from one to three.

Results: Out of 13 interventions, a complete transection was achieved in ten cases (76.9%), and a partial transection was documented in three cases (23.1%). Irrelevant lesions on the flexor tendons were observed in two cases (15.4%), and an arterial branch was damaged in one (7.7%). Ultrasound visibility varied among specimens, but essential structures were delineated in all cases.

Conclusion: Ultrasound-guided thread release of Guyon's canal has shown promising first results in anatomic specimens. However, further studies are required to ensure the safety of the procedure.

Relevance statement: Our study showed that minimally invasive ultrasound-guided thread release of Guyon's canal is a feasible approach in the anatomical model. The results may provide a basis for further research and refinement of this technique.

Key points: • In Guyon's canal syndrome, the ulnar nerve is compressed at the wrist, often requiring surgical release. • We adapted and tested a minimally invasive ultrasound-guided thread release technique in anatomic specimens. • The technique was effective; however, in one specimen, a small anatomic branch was damaged.

Background: To evaluate the reproducibility of a vessel-specific minimum cost path (MCP) technique used for lobar segmentation on noncontrast computed tomography (CT).

Methods: Sixteen Yorkshire swine (49.9 ± 4.7 kg, mean ± standard deviation) underwent a total of 46 noncontrast helical CT scans from November 2020 to May 2022 using a 320-slice scanner. A semiautomatic algorithm was employed by three readers to segment the lung tissue and pulmonary arterial tree. The centerline of the arterial tree was extracted and partitioned into six subtrees for lobar assignment. The MCP technique was implemented to assign lobar territories by assigning lung tissue voxels to the nearest arterial tree segment. MCP-derived lobar mass and volume were then compared between two acquisitions, using linear regression, root mean square error (RMSE), and paired sample t-tests. An interobserver and intraobserver analysis of the lobar measurements was also performed.

Results: The average whole lung mass and volume was 663.7 ± 103.7 g and 1,444.22 ± 309.1 mL, respectively. The lobar mass measurements from the initial (MLobe1) and subsequent (MLobe2) acquisitions were correlated by MLobe1 = 0.99 MLobe2 + 1.76 (r = 0.99, p = 0.120, RMSE = 7.99 g). The lobar volume measurements from the initial (VLobe1) and subsequent (VLobe2) acquisitions were correlated by VLobe1 = 0.98VLobe2 + 2.66 (r = 0.99, p = 0.160, RSME = 15.26 mL).

Conclusions: The lobar mass and volume measurements showed excellent reproducibility through a vessel-specific assignment technique. This technique may serve for automated lung lobar segmentation, facilitating clinical regional pulmonary analysis.

Relevance statement: Assessment of lobar mass or volume in the lung lobes using noncontrast CT may allow for efficient region-specific treatment strategies for diseases such as pulmonary embolism and chronic thromboembolic pulmonary hypertension.

Key points: • Lobar segmentation is essential for precise disease assessment and treatment planning. • Current methods for segmentation using fissure lines are problematic. • The minimum-cost-path technique here is proposed and a swine model showed excellent reproducibility for lobar mass measurements. • Interobserver agreement was excellent, with intraclass correlation coefficients greater than 0.90.