European Radiology Experimental最新文献

Background: Accurate assessment of treatment response to neoadjuvant systemic therapy (NAST) in breast cancer is important prior to surgery. We aimed at evaluating the feasibility of thoracic photon-counting detector computed tomography (PCCT) in assessing treatment response in breast cancers following NAST.

Methods: We retrospectively included patients with newly diagnosed breast cancer who received contrast-enhanced thoracic PCCT in prone position before and after NAST. Three experienced radiologists measured tumor size, tumor area, iodine uptake within tumors, number of suspicious breast lesions and of suspicious axillary lymph nodes before and after NAST. We compared the initial tumor size to contrast-enhanced magnetic resonance imaging (MRI), the residual tumor size after NAST to histopathology.

Results: Eighteen PCCT exams in nine patients aged 58 ± 14 years (mean ± standard deviation) were analyzed. After NAST, PCCT correctly identified a reduction in tumor burden in 9 of 9 cases and a complete response in 2 of 2 cases, with a significant reduction in tumor size, area, T-stage, number of suspicious breast lesions and of suspicious lymph nodes (p < 0.001 for all) as well as reduction in cutaneous infiltration (p = 0.010). Mean and maximum iodine uptake showed a nonsignificant reduction in cases with residual tumor after NAST (p = 0.092 and 0.363).

Conclusion: These preliminary findings suggest that thoracic PCCT can accurately detect local changes in breast cancer after NAST.

Relevance statement: Thoracic PCCT offers promising potential for accurately assessing breast cancer response to NAST.

Trial registration: German Clinical Trials Register DRKS00028997.

Key points: Prone thoracic contrast-enhanced photon-counting detector computed tomography (PCCT) can accurately detect reductions in tumor size, area, and T-stage. Prone PCCT can identify a decrease in the number of suspicious axillary lymph nodes. This technique shows promising results in identifying breast cancer response to neoadjuvant systemic therapy (NAST).

Background: Dark-field chest radiography is sensitive to the lung alveolar structure. We evaluated the change of dark-field signal between inspiration and expiration.

Methods: From 2018 to 2020, patients who underwent chest computed tomography (CT) were prospectively enrolled, excluding those with any lung condition besides emphysema visible on CT. Participants were imaged in both inspiration and expiration with a prototype dark-field chest radiography system. We calculated the total dark-field signal ∑DF and the dark-field coefficient ϵ, assumed to be proportional to the total number of alveoli and the alveolar density, respectively.

Results: Eighty-eight subjects, aged 64 years ± 11 (mean ± standard deviation), 55 males, were enrolled. Dark-field signal in the lung projection appeared higher in expiration compared to inspiration. Over all participants, ∑DF was higher in inspiration (1.6 × 10^-2 ± 0.4 × 10^-2 m²) compared to expiration (1.5 × 10^-2 ± 0.4 m²) (p < 0.001), with its expiration-to-inspiration not ratio being different for any emphysema subgroup. The dark-field coefficient ϵ was lower in inspiration (2.3 ± 0.6 m^-1) compared to expiration (3.1 ± 1.1 m^-1) (p < 0.001) over all participants. The dark-field coefficient in inspiration and expiration, as well as their ratio, was lower for at least moderate emphysema when compared to the control group (e.g., ϵ = 2.5 ± 1.0 m^-1 for moderate emphysema in expiration versus ϵ = 3.6 ± 0.7 m^-1 for participants without emphysema (p = 0.003).

Conclusion: The dark-field signal depends on the breathing state. Differences between breathing states are influenced by emphysema severity.

Relevance statement: The patient's breathing state influences the dark-field chest radiograph, potentially impacting its diagnostic value.

Key points: Signal characteristics in dark-field chest radiography change between inspiration and expiration. The total dark-field signal decreases slightly from inspiration to expiration, while the dark-field coefficient increases substantially. The ratio of the total dark-field signal between expiration and inspiration is independent of emphysema severity, whereas the ratio of the dark-field coefficient depends on emphysema severity.

Background: We investigated inflammation-induced changes in femoral hematopoietic bone marrow using advanced magnetic resonance imaging (MRI) techniques, including T2-weighted imaging, scalar T2 mapping, and machine learning-enhanced T2 distribution analysis to improve the detection of bone marrow microstructural alterations. Findings were correlated with histological markers and systemic inflammation.

Methods: Using a 9.4-T magnet, T2-weighted and multislice multiecho sequences were applied to evaluate bone marrow in female C57BL/6J mice divided into three groups: (1) controls; (2) lipopolysaccharide-induced acute inflammation (LPS); and (3) streptozotocin (STZ)- and LPS-induced diabetic inflammation (STZ + LPS). T2 relaxation times and their distributions with scalar mapping and model-informed machine learning (MIML) were analyzed. Correlations with histological iron levels and blood neutrophil counts were assessed.

Results: T2-weighted imaging showed a reduced signal-to-noise ratio in inflamed bone marrow (p = 0.034). Scalar T2 mapping identified decreased T2 relaxation times (p = 0.042), moderately correlating with neutrophil counts (ρ = 0.027) and iron levels (ρ = 0.016). MIML-enhanced T2 distribution analysis exhibited superior sensitivity than scalar T2 mapping, revealing significant reductions in the first T2 distribution peak (p = 0.0025), which strongly correlated with neutrophil counts (ρ = 0.0016) and iron sequestration (ρ = 0.0002). Histology confirmed elevated iron deposits in inflamed marrow, aligning with systemic inflammation.

Conclusion: Combining T2-weighted imaging, scalar T2 mapping, and MIML-enhanced T2 distribution analysis offers complementary insights into inflammation-induced bone marrow remodeling. T2 distribution analysis emerged as a more sensitive tool for detecting microstructural changes, such as iron sequestration, supporting its potential as a noninvasive biomarker for diagnosing and monitoring inflammatory diseases.

Relevance statement: This study highlights the potential of advanced MRI T2 analysis and machine learning methods for noninvasive detection of inflammation-induced microstructural changes in bone marrow, offering promising diagnostic tools for inflammatory diseases.

Key points: This study investigated inflammation-induced changes in bone marrow with T2 MRI and MIML. MIML outperformed quantitative scalar T2 analysis, increasingly detecting inflammation and iron sequestration in the hematopoietic bone marrow. T2 MRI with MIML analysis could aid in the early diagnosis and management of inflammatory diseases.

Over the past decades, computed tomography (CT) imaging has profited from various technical innovations. Besides improvements such as higher temporal and spatial resolutions, lower radiation dose, and the introduction of dual- and multi-energy imaging, the development and recent clinical introduction of photon-counting detector CT (PCD-CT) represents a milestone with the potential to substantially change clinical CT imaging and expand its indications. This thematic series of European Radiology Experimental comprises a collection of original research papers and review articles demonstrating the benefits and challenges of this cutting-edge technology. The thematic series includes a wide range of relevant topics spanning from initial clinical experiences using PCD-CT to original research papers covering potential applications in various body regions.

Background: Reducing radiation and contrast media (CM) doses in computed tomography angiography (CTA) is especially relevant for potentially vulnerable populations. Low tube voltage photon-counting detector CT (PCD-CT) offers an improved iodine contrast-to-noise ratio (CNR) as compared to conventional CT scanners. We investigated optimized radiation and CM doses of PCD-CT angiography at low tube voltage in an animal model.

Methods: Six minipigs (median weight: 32.5 kg; IQR: 29.8-34.6 kg) underwent thoracoabdominal CTA using a clinical dual-source PCD-CT at 70 kVp with three scan protocols: (A) reference (100% CM and radiation dose), (B) increased radiation (233%) and reduced CM (56%) dose, and (C) reduced radiation (50%) and increased CM (141%) dose. CNR, subjective image quality, and radiation doses were assessed, with statistical analysis including Mann-Whitney U-test and Kruskal-Wallis tests.

Results: CTDI_vol was 1.7 mGy (IQR: 1.5-1.8) for scan A, 4.3 mGy (IQR: 3.8-4.7) for scan B, and 0.9 mGy (IQR: 0.8-1.0) for scan C (p < 0.001). CM volumes were 16 mL (IQR: 15-17) for scan A, 10 mL (IQR: 8-10) for scan B, and 23 mL (IQR: 21-24) for scan C. No significant differences in CNR were found between scans, with medians of 26 (IQR: 24-28) for scan A, 23 (IQR: 22-26) for scan B, and 26 (IQR: 24-30) for scan C (p = 0.276). Subjective image quality was similar across scans (p = 0.342).

Conclusion: Low tube voltage PCD-CT angiography allows substantial reductions in radiation and CM dose while maintaining stable and improved CNR, which allows further dose flexibility for individualized CTA protocols.

Relevance statement: PCD-CT at low tube voltage provides a high CNR and great flexibility in dose optimization, making it particularly effective for applications where minimizing radiation and CM exposure is a priority.

Key points: Low tube voltage imaging with photon counting detector (PCD)-CT enables flexible contrast and radiation dose optimization strategies in thoracoabdominal CT angiography (CTA). The CNR for thoracoabdominal CTA remains stable with appropriate contrast and radiation dose adjustments at low tube voltage PCD-CT. Low tube voltage PCD-CT consistently yields diagnostic image quality in thoracoabdominal angiography even at reduced contrast or radiation doses.

Background: To assess the potential of virtual monoenergetic images (VMIs) on a photon-counting computed tomography (PCCT) for reducing the amount of injected iodine contrast media compared to an energy-integrating CT (EICT).

Methods: A multienergy phantom was scanned with a PCCT and EICT at 11 mGy with abdomen-pelvis examination parameters. VMIs were generated at 40 keV, 50 keV, 60 keV, and 70 keV. For all VMIs, the contrast-to-noise ratio (CNR) of iodine inserts with concentrations of 1 mg/mL, 2 mg/mL, 5 mg/mL, 10 mg/mL, and 15 mg/mL was calculated by dividing the signal difference between HU in iodine inserts versus solid water by the noise value assessed on solid water. The potential reduction in iodine media was calculated by the rate of reduction in iodine concentration with PCCT while maintaining the same CNR obtained with EICT for the reference concentration.

Results: Significantly higher CNR values were found with PCCT at all VMI energy levels for iodine concentrations above 1 mg/mL. The highest reduction was observed at 40 keV, with a value of 48.9 ± 1.6% (mean ± standard deviation). It decreased as the energy level increased, by 38.5 ± 0.5%, and 30.8 ± 0.8% for 50 and 60 keV, respectively. For 70 keV, the potential reduction of 24.4 ± 1.1% was found for iodine concentrations above 1 mg/mL. This reduction reached 57 ± 2.3% at 40 keV with PCCT compared to 60 keV with EICT.

Conclusion: For abdomen-pelvis protocols, the use of VMIs with PCCT significantly improved the CNR of iodine, offering the potential to reduce the required contrast medium.

Relevance statement: The use of VMIs with PCCT may reduce the quantity of iodine contrast medium to be injected compared with EICT, limiting costs, the risk of adverse effects, and the amount of contrast agent released into the wastewater.

Key points: PCCT improves the image quality of VMIs. PCCT offers the potential for reducing the amount of injected contrast medium. PCCT potential for reducing the injected contrast medium depends on energy level.

Background: Nonenlarged lymph node metastasis (NELNM) of rectal cancer is easily overlooked because these apparently normal lymph nodes are sometimes too small to measure directly using imaging techniques. Radiomic-based multiparametric imaging sequences could predict NELNM based on the primary lesion of rectal cancer. We aimed to study the performance of magnetic resonance imaging (MRI) radiomics derived from reduced field-of-view diffusion-weighted imaging (rDWI) and conventional DWI (cDWI) for the prediction of NELNM.

Methods: A total of 86 rectal cancer patients (60 and 26 patients in training and test cohorts, respectively), underwent multiparametric MRI. Radiomic features were extracted from the whole primary lesion of rectal cancer segmented on T2-weighted imaging (T2WI), rDWI, and cDWI, both with b-value of 800 s/mm² and apparent diffusion coefficient (ADC) maps from both DWI sequences (rADC and cADC). The radiomic models based on the above imaging methods were built for the assessment of NELNM status. Their diagnostic performances were evaluated in comparison with subjective evaluation by radiologists.

Results: rADC demonstrated a significant advantage over subjective assessment in predicting NELNM in both training and test cohorts (p ≤ 0.002). In the test cohort, rADC exhibited a significantly higher area under the receiver operating characteristics curve than cADC, cDWIb800, and T2WI (p ≤ 0.020) in assessing NELNM for region-of-interest (ROI) delineation while excelling over rDWIb800 for prediction of NELNM (p = 0.0498).

Conclusion: Radiomic features based on rADC outperformed those derived from T2WI and fDWI in predicting the NELNM status of rectal cancer, rADC was more advantageous than rDWIb800 in assessing NELNM.

Relevance statement: Advanced rDWI excelled over cDWI in radiomic assessment of NELNM of rectal cancer, with the best performance observed for rADC, in contrast to rDWIb800, cADC, cDWIb800, and T2WI.

Key points: rDWI, cDWI, and T2WI radiomics could help assess NELNM of rectal cancer. Radiomic features based on rADC outperformed those based on rDWIb800, cADC, cDWIb800, and T2WI in predicting NELNM. For rDWI radiomics, the ADC map was more accurate and reliable than DWI to assess NELNM for region of interest delineation.

Background: Upfront combination chemoradiotherapy (CRT) represents the standard of care for patients affected by stage III squamous cell carcinoma (SCC) of the anal canal, achieving satisfactory results both in terms of overall survival and local disease control. However, a non-negligible fraction of patients obtain incomplete responses, highlighting the need for innovative prognostic tools. We report the preliminary results of a customized radiomic algorithm designed to predict tumor response to CRT in patients affected by SCC of the anal canal.

Methods: We manually annotated pretreatment T2-weighted turbo spin-echo images of 26 consecutive patients with stage III SCC of the anal canal treated with CRT at our institution from 2012 to 2022. Each patient was classified as complete response (CR, 17 patients), or non-complete response (non-CR, 9 patients) based on the absence or presence of residual disease at imaging and endoscopy after treatment. A total of 132 three-dimensional radiomic features were extracted for each patient and fed to a dedicated machine-learning classifier.

Results: Models trained with gray-level co-occurrence matrix features achieved the best performances (accuracy 0.846 ± 0.064, sensitivity 0.900 ± 0.122, specificity 0.833 ± 0.175, area under receiver operating characteristics curve 0.867 ± 0.055), highlighting a more homogeneous distribution of voxel intensities and lower spatial complexity in non-CR patients.

Conclusion: Our radiomic tool accurately predicted tumor response to CRT in patients with stage III SCC of the anal canal, highlighting a more homogeneous tissue composition in poor responders.

Relevance statement: The more homogeneous radiomic texture observed in non-CR patients may be imputable to a dominant neoplastic clone with a relatively low mitotic index (therefore, limited tissue necrosis), intrinsically more resistant to CRT than faster-proliferating tumors.

Key point: A non-negligible fraction of patients with anal SCC respond unsatisfactorily to CRT. Our radiomic model predicted response to CRT based on pretreatment MRI. We observed a more homogeneous tissue composition in poor responders. The slow proliferation of a dominant clone may explain non-CR to CRT.

Background: Early detection and treatment of tendinopathy may prevent progression to partial tears or complete rupture. Shear wave elastography (SWE) may help address the need for better tendon pathology characterization. This study aimed to quantify the effect of structural damage in an ex vivo animal tendinopathy model using SWE.

Methods: Forty-two porcine flexor tendons were injected with a 0.05-mL bolus of 1.5% collagenase solution to induce focal structural damage without surface tears. Control tendons were injected with saline (n = 42). Twenty-one tendons from each group were incubated at 37 °C for 3.5 h, while the remaining 21 from each group were incubated for 7 h. Each group was then divided into three groups of seven, and tendon incisions were made at 25%, 50%, and 75% of the tendon thickness. Tendons were mechanically stretched axially during simultaneous collection of SWE at the injection site.

Results: There were significant differences in shear wave speed (SWS) (saline > collagenase) at 3.5-h incubation (p < 0.001) and 7-h incubation (p < 0.001). Additionally, there was a significant difference in SWS between tendons cut at 25% and tendons cut at 50% and 75% (p = 0.040 and p = 0.001, respectively). Collagenase-treated tendons ruptured at a lower force than saline-treated tendons at both incubation times (both p < 0.001) when controlling for cut depth. Tendons treated with collagenase ruptured at a lower force than the saline control group at each cut thickness (all p < 0.001) controlling for incubation time.

Conclusion: In a controlled ex vivo porcine model, SWE can be used to detect structural damage associated with tendinopathy.

Relevance statement: Shear wave elastography can be used to show differences in abnormal tendons that may be translatable to clinical use as an adjunctive measure of tendon elasticity and injury.

Key points: Tendon abnormality was quantitatively characterized using shear wave elastography in an ex vivo porcine experimental model. Shear wave speed was an accurate imaging biomarker for tendon health. Shear wave elastography was effective at detecting the extent of tendon damage. Tendons with decreased shear wave speed measurements rupture at smaller applied mechanical force.

Background: The cingulum bundle is a brain white matter fasciculus associated with the cingulate gyrus. It connects areas from the temporal to the frontal lobe. It is composed of fibers with different terminations, lengths, and structural properties, related to specific brain functions. We aimed to automatically reconstruct this fasciculus in patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and to assess whether trajectories have different microstructural properties in relation to dementia progression.

Methods: Multi-shell high angular resolution diffusion imaging-HARDI image datasets from the "Alzheimer's Disease Neuroimaging Initiative"-ADNI repository of 10 AD, 18 MCI, and 21 cognitive normal (CN) subjects were used to reconstruct three subdivisions of the cingulum bundle, using a probabilistic approach, combined with measurements of diffusion tensor and neurite orientation dispersion and density imaging metrics in each subdivision.

Results: The subdivisions exhibit different pathways, terminations, and structural characteristics. We found differences in almost all the diffusivity metrics among the subdivisions (p < 0.001 for all the metrics) and between AD versus CN and MCI versus CN subjects for mean diffusivity (p = 0.007-0.038), radial diffusivity (p = 0.008-0.049) and neurite dispersion index (p = 0.005-0.049).

Conclusion: Results from tractography analysis of the subdivisions of the cingulum bundle showed an association in the role of groups of fibers with their functions and the variance of their properties in relation to dementia progression.

Relevance statement: The cingulum bundle is a complex tract with several pathways and terminations related to many cognitive functions. A probabilistic automatic approach is proposed to reconstruct its subdivisions, showing different microstructural properties and variations. A larger sample of patients is needed to confirm results and elucidate the role of diffusion parameters in characterizing alterations in brain function and progression to dementia.

Key points: The microstructure of the cingulum bundle is related to brain cognitive functions. A probabilistic automatic approach is proposed to reconstruct the subdivisions of the cingulum bundle by diffusion-weighted images. The subdivisions showed different microstructural properties and variations in relation to the progression of dementia.