European Radiology Experimental最新文献

Background: Dark-field radiography imaging exploits the wave character of x-rays to measure small-angle scattering on material interfaces, providing structural information with low radiation exposure. We explored the potential of dark-field imaging of bone microstructure to improve the assessment of bone strength in osteoporosis.

Methods: We prospectively examined 14 osteoporotic/osteopenic and 21 non-osteoporotic/osteopenic human cadaveric vertebrae (L2-L4) with a clinical dark-field radiography system, micro-computed tomography (CT), and spectral CT. Dark-field images were obtained in both vertical and horizontal sample positions. Bone microstructural parameters (trabecular number, Tb.N; trabecular thickness, Tb.Th; bone volume fraction, BV/TV; degree of anisotropy, DA) were measured using standard ex vivo micro-CT, while hydroxyapatite density was measured using spectral CT. Correlations were assessed using Spearman rank correlation coefficients.

Results: The measured dark-field signal was lower in osteoporotic/osteopenic vertebrae (vertical position, 0.23 ± 0.05 versus 0.29 ± 0.04, p < 0.001; horizontal position, 0.28 ± 0.06 versus 0.34 ± 0.04, p = 0.003). The dark-field signal from the vertical position correlated significantly with Tb.N (ρ = 0.46, p = 0.005), BV/TV (ρ = 0.45, p = 0.007), DA (ρ = -0.43, p = 0.010), and hydroxyapatite density (ρ = 0.53, p = 0.010). The calculated ratio of vertical/horizontal dark-field signal correlated significantly with Tb.N (ρ = 0.43, p = 0.011), BV/TV (ρ = 0.36, p = 0.032), DA (ρ = -0.51, p = 0.002), and hydroxyapatite density (ρ = 0.42, p = 0.049).

Conclusion: Dark-field radiography is a feasible modality for drawing conclusions on bone microarchitecture in human cadaveric vertebral bone.

Relevance statement: Gaining knowledge of the microarchitecture of bone contributes crucially to predicting bone strength in osteoporosis. This novel radiographic approach based on dark-field x-rays provides insights into bone microstructure at a lower radiation exposure than that of CT modalities.

Key points: Dark-field radiography can give information on bone microstructure with low radiation exposure. The dark-field signal correlated positively with bone microstructure parameters. Dark-field signal correlated negatively with the degree of anisotropy. Dark-field radiography helps to determine the directionality of trabecular loss.

Background: To assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o.

Methods: A radiologist with 20 years' experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with "Explain this medical report to a patient using simple language". Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach's α, and the Kruskal-Wallis test were used.

Results: Mammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3-6 reports were challenging.

Conclusion: The model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care.

Relevance statement: Simplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice.

Key points: AI simplifies complex breast imaging reports, enhancing patient understanding. Simplified reports from AI maintain accuracy, improving patient comprehension significantly. Implementing AI reports enhances patient engagement and communication in breast imaging.

Background: Our aim was to train and test a deep learning-based algorithm for automatically segmenting kidneys and renal cysts in patients with autosomal dominant polycystic kidney disease (ADPKD).

Methods: We retrospectively selected all ADPKD patients who underwent renal MRI with coronal T2-weighted imaging at our institution from 2008 to 2022. The 20 most recent examinations constituted the test dataset, to mimic pseudoprospective enrolment. The remaining ones constituted the training dataset to which eight normal renal MRIs were added. Kidneys and cysts ground truth segmentations were performed on coronal T2-weighted images by a junior radiologist supervised by an experienced radiologist. Kidneys and cysts of the 20 test MRIs were segmented by the algorithm and three independent human raters. Segmentations were compared using overlap metrics. The total kidney volume (TKV), total cystic volume (TCV), and cystic index (TCV divided by TKV) were compared using Bland-Altman analysis.

Results: We included 164 ADPKD patients. Dice similarity coefficients ranged from 85.9% to 87.4% between the algorithms and the raters' segmentations and from 84.2% to 86.2% across raters' segmentations. For TCV assessment, the biases ± standard deviations (SD) were 3-19 ± 137-151 mL between the algorithm and the raters, and 22-45 ± 49-57 mL across raters. The algorithm underestimated TKV and TCV in two outliers with TCV > 2800 mL. For cystic index assessment, the biases ± SD were 2.5-6.9% ± 6.7-8.3% between the algorithm and the raters, and 2.1-9.4 ± 7.4-11.6% across raters.

Conclusion: The algorithm's performance fell within the range of inter-rater variability, but large TKV and TCV were underestimated.

Relevance statement: Accurate automated segmentation of the renal cysts will enable the large-scale evaluation of the prognostic value of TCV and cystic index in ADPKD patients. If these biomarkers are prognostic, then automated segmentation will facilitate their use in daily routine.

Key points: Cystic volume is an emerging biomarker in ADPKD. The algorithm's performance in segmenting kidneys and cysts fell within interrater variability. The segmentation of very large cysts, under-represented in the training dataset, needs improvement.

Background: Extensive glioblastoma infiltration justifies a 15-mm margin around the gross tumor volume (GTV) to define the radiotherapy clinical target volume (CTV). Amide proton transfer (APT)-weighted imaging could enable visualization of tumor infiltration, allowing more accurate GTV delineation. We quantified the impact of integrating APT-weighted imaging into GTV delineation of glioblastoma and compared two APT-weighted quantification methods-magnetization transfer ratio asymmetry (MTR_asym) and Lorentzian difference (LD) analysis-for target delineation.

Methods: Nine glioblastoma patients underwent an extended imaging protocol prior to radiotherapy, yielding APT-weighted MTR_asym and LD maps. From both maps, biological tumor volumes were generated (BTV_MTRasym and BTV_LD) and added to the conventional GTV to generate biological GTVs (GTV_bio,MTRasym and GTV_bio,LD). Wilcoxon signed-rank tests were performed for comparisons.

Results: The GTV_bio,MTRasym and GTV_bio,LD were significantly larger than the conventional GTV (p ≤ 0.022), with a median volume increase of 9.3% and 2.1%, respectively. The GTV_bio,MTRasym and GTV_bio,LD were significantly smaller than the CTV (p = 0.004), with a median volume reduction of 72.1% and 70.9%, respectively. There was no significant volume difference between the BTV_MTRasym and BTV_LD (p = 0.074). In three patients, BTV_MTRasym delineation was affected by elevated signals at the brain periphery due to residual motion artifacts; this elevation was absent on the APT-weighted LD maps.

Conclusion: Larger biological GTVs compared to the conventional GTV highlight the potential of APT-weighted imaging for radiotherapy target delineation of glioblastoma. APT-weighted LD mapping may be advantageous for target delineation as it may be more robust against motion artifacts.

Relevance statement: The introduction of APT-weighted imaging may, ultimately, enhance visualization of tumor infiltration and eliminate the need for the substantial 15-mm safety margin for target delineation of glioblastoma. This could reduce the risk of radiation toxicity while still effectively irradiating the tumor.

Trial registration: NCT05970757 (ClinicalTrials.gov).

Key points: Integration of APT-weighted imaging into target delineation for radiotherapy is feasible. The integration of APT-weighted imaging yields larger GTVs in glioblastoma. APT-weighted LD mapping may be more robust against motion artifacts than APT-weighted MTR_asym.

Background: Breast cancer (BC) is the most common malignancy in women and the second cause of cancer death. In recent years, there has been a strong development in artificial intelligence (AI) applications in medical imaging for several tasks. Our aim was to evaluate the potential of transfer learning with convolutional neural networks (CNNs) in discriminating suspicious breast lesions on ultrasound images.

Methods: Transfer learning performances of five different CNNs (Inception V3, Xception, Densenet121, VGG 16, and ResNet50) were evaluated on a public and on an institutional dataset (526 and 392 images, respectively), customizing the top layers for the specific task. Institutional images were contoured by an expert radiologist and processed to feed the CNNs for training and testing. Postimaging biopsies were used as a reference standard for classification. The area under the receiver operating curve (AUROC) was used to assess diagnostic performance.

Results: Networks performed very well on the public dataset (AUROC 0.938-0.996). The direct generalization to the institutional dataset resulted in lower performances (max AUROC 0.676); however, when tested on BI-RADS 3 and BI-RADS 5 only, results were improved (max AUROC 0.792). Good results were achieved on the institutional dataset (AUROC 0.759-0.818) and, when selecting a threshold of 2% for classification, a sensitivity of 0.983 was obtained for three of five CNNs, with the potential to spare biopsy in 15.3%-18.6% of patients.

Conclusion: In conclusion, transfer learning with CNNs may achieve high sensitivity and might be used as a support tool in managing suspicious breast lesions on ultrasound images.

Relevance statement: Transfer learning is a powerful technique to exploit the performances of well-trained CNNs for image classification. In a clinical scenario, it might be useful for the management of suspicious breast lesions on breast ultrasound, potentially sparing biopsy in a non-negligible number of patients.

Key points: Properly trained CNNs with transfer learning are highly effective in differentiating benign and malignant lesions on breast ultrasound. Setting clinical thresholds increased sensitivity. CNNs might be useful as support tools in managing suspicious lesions on breast ultrasound.

Background: This pilot prospective study aimed to evaluate ultrasound-guided cryoablation of breast cancer (BC) by assessing: (i) technical efficacy as the presence of necrosis in surgical specimens and rate of complete tumor ablation; (ii) safety as incidence and severity of complications; and (iii) patients' satisfaction using a dedicated questionnaire. In addition, (iv) we tested the capability of magnetic resonance imaging (MRI) or contrast-enhanced mammography (CEM) to predict cryoablation efficacy.

Methods: From 07/2022 to 01/2023, we enrolled 20 patients with early-stage BC scheduled for breast surgery. Ten of them, with a cryo-feasible cancer location, were sent to cryoablation (cryo-group) and ten to routine surgical practice (control group). Both groups underwent surgery and were asked to answer a satisfaction questionnaire.

Results: Of eleven patients screened for cryoablation, only one refused to be treated at another hospital (acceptance rate 10/11, 91%). Surgery was quadrantectomy in 19 cases and mastectomy in 1. In the cryo-group, the procedure was completed and steatonecrosis was observed in 10/10 cases, with complete tumor ablation in nine of them. The post-procedural status was evaluated with MRI in five patients, with CEM in four patients, and with ultrasound in one patient who refused MRI and CEM. MRI or CEM correctly predicted complete cryoablation in eight patients and incomplete cryoablation in one patient. Patients in both groups did not have serious complications and responded positively to satisfaction questionnaires.

Conclusion: Ultrasound-guided cryoablation of early-stage BC is well accepted by patients, effective, and safe. MRI and CEM were able to predict the procedure's technical efficacy.

Trial registration: https://clinicaltrials.gov/study/NCT05727813 updated February 14, 2023.

Relevance statement: Our pilot study showed that ultrasound-guided cryoablation is a promising nonsurgical alternative for treating early-stage BC.

Key points: Ultrasound-guided cryoablation was effective and safe in early BC patients. The procedure was well-tolerated, with low morbidity and high patient satisfaction. MRI and CEM predicted cryoablation efficacy, in accordance with histopathologic findings. Cryoablation can be considered a potential alternative to surgery in selected patients.

Background: To prospectively evaluate ultrasound backscatter coefficients (BSCs) of the supraspinatus and infraspinatus muscles and compare with Goutallier classification on magnetic resonance imaging (MRI).

Methods: Fifty-six participants had shoulder MRI exams and ultrasound exams of the supraspinatus and infraspinatus muscles. Goutallier MRI grades were determined and BSCs were measured. Group means were compared and the strength of relationships between the measures were determined. Using binarized Goutallier groups (0-2 versus 3-4), areas under the receiver operating characteristic curves (AUROCs) were calculated. The nearest integer cutoff value was determined using Youden's index.

Results: BSC values were significantly different among most Goutallier grades for the supraspinatus and infraspinatus muscles (both p < 0.001). Strong correlations were found between the BSC values and Goutallier grades for the supraspinatus (τ_b = 0.72, p < 0.001) and infraspinatus (τ_b = 0.79, p < 0.001) muscles. BSC showed excellent performance for classification of the binarized groups (0-2 versus 3-4) for both supraspinatus (AUROC = 0.98, p < 0.0001) and infraspinatus (AUROC = 0.98, p < 0.0001) muscles. Using a cutoff BSC value of -17 dB, sensitivity, specificity, and accuracy for severe fatty infiltration were 87.0%, 90.0%, and 87.5% for the supraspinatus muscle, and 93.6%, 87.5%, and 92.7% for the infraspinatus muscle.

Conclusion: BSC can be applied to the rotator cuff muscles for assessment of fatty infiltration. For both the supraspinatus and infraspinatus muscles, BSC values significantly increased with higher Goutallier grades and showed strong performance in distinguishing low versus high Goutallier grades.

Relevance statement: Fatty infiltration of the rotator cuff muscles can be quantified using BSC values, which are higher with increasing Goutallier grades.

Key points: Ultrasound BSC measurements are reliable for the quantification of muscle fatty infiltration. BCS values increased with higher Goutallier MRI grades. BCS values demonstrated high performance for distinguishing muscle fatty infiltration groups.

Background: Coronary computed tomography angiography (CCTA) offers non-invasive diagnostics of the coronary arteries. Vessel evaluation requires the administration of intravenous contrast. The purpose of this study was to evaluate the utility of gadolinium-based contrast agent (GBCA) as an alternative to iodinated contrast for CCTA on a first-generation clinical dual-source photon-counting-detector (PCD)-CT system.

Methods: A dynamic circulating phantom containing a three-dimensional-printed model of the thoracic aorta and the coronary arteries were used to evaluate injection protocols using gadopentetate dimeglumine at 50%, 100%, 150%, and 200% of the maximum approved clinical dose (0.3 mmol/kg). Virtual monoenergetic image (VMI) reconstructions ranging from 40 keV to 100 keV with 5 keV increments were generated on a PCD-CT. Contrast-to-noise ratio (CNR) was calculated from attenuations measured in the aorta and coronary arteries and noise measured in the background tissue. Attenuation of at least 350 HU was deemed as diagnostic.

Results: The highest coronary attenuation (441 ± 23 HU, mean ± standard deviation) and CNR (29.5 ± 1.5) was achieved at 40 keV and at the highest GBCA dose (200%). There was a systematic decline of attenuation and CNR with higher keV reconstructions and lower GBCA doses. Only reconstructions at 40 and 45 keV at 200% and 40 keV at 150% GBCA dose demonstrated sufficient attenuation above 350 HU.

Conclusion: Current PCD-CT protocols and settings are unsuitable for the use of GBCA for CCTA at clinically approved doses. Future advances to the PCD-CT system including a 4-threshold mode, as well as multi-material decomposition may add new opportunities for k-edge imaging of GBCA.

Relevance statement: Patients allergic to iodine-based contrast media and the future of multicontrast CT examinations would benefit greatly from alternative contrast media, but the utility of GBCA for coronary photon-counting-dector-CT angiography remains limited without further optimization of protocols and scanner settings.

Key points: GBCA-enhanced coronary PCD-CT angiography is not feasible at clinically approved doses. GBCAs have potential applications for the visualization of larger vessels, such as the aorta, on PCD-CT angiography. Higher GBCA doses and lower keV reconstructions achieved higher attenuation values and CNR.