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شراكات المدرسة والأسرة والمجتمع : دليلك للعمل =
كتاب ثري وعملي يشجعك على البدء في بناء شراكة فاعلة بين المدرسة والأسرة والمجتمع ويقدم لك مختلف الأدوات التي تحتاجها لتبدأ ستجد نموذجا مميزا لمساعدة كل مدرسة على بناء خطة العام للشراكة مع الأسر والمجتمع بالتفصيل ستحصل على خطوات تكوين فرق الشراكة في داخل المدرسة ستحصل على نماذج جاهزة للعمل أيضا قوائم بأنشطة هادفة يمكن تنفيذها وأثبتت جدواها عبر دراسات.
BOOK
Advanced image-based virtual monoenergetic dual-energy CT angiography of the abdomen: optimization of kiloelectron volt settings to improve image contrast
Objectives
To compare quantitative image quality parameters in abdominal dual-energy computed tomography angiography (DE-CTA) using an advanced image-based (Mono+) reconstruction algorithm for virtual monoenergetic imaging and standard DE-CTA.
Methods
Fifty-five patients (36 men; mean age, 64.2 ± 12.7 years) who underwent abdominal DE-CTA were retrospectively included. Mono + images were reconstructed at 40, 50, 60, 70, 80, 90 and 100 keV levels and as standard linearly blended M_0.6 images (60 % 100 kV, 40 % 140 kV). The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the common hepatic (CHA), splenic (SA), superior mesenteric (SMA) and left renal arteries (LRA) were objectively measured.
Results
Mono+ DE-CTA series showed a statistically superior CNR for 40, 50, 60, 70 and 80 keV (
P
< 0.031) compared to M_0.6 images for all investigated arteries except SMA at 80 keV (
P
= 0.08). CNR at 40 keV revealed a mean relative increase of 287.7 % compared to linearly blended images among all assessed arteries (
P
< 0.001). SNR of Mono+ images was consistently significantly higher at 40, 50, 60 and 70 keV compared to M_0.6 for CHA and SA (
P
< 0.009).
Conclusions
Compared to linearly blended images, Mono+ reconstructions at low keV levels of abdominal DE-CTA datasets significantly improve quantitative image quality.
Key Points
•
Mono+ combines increased attenuation with reduced image noise compared to standard DE-CTA.
•
Mono+ shows superior contrast-to-noise ratios at low keV compared to linearly-blended images.
•
Contrast-to-noise ratio in monoenergetic DE-CTA peaks at 40 keV.
•
Mono+ reconstructions significantly improve quantitative image quality at low keV levels.
Journal Article
Dual-energy CT in early acute pancreatitis: improved detection using iodine quantification
ObjectivesTo evaluate the diagnostic performance of a dual-energy computed tomography (DECT)-based technique using iodine quantification and fat fraction analysis for the diagnosis of early acute pancreatitisMethodsIn this retrospective study, 45 patients (35 men and 10 women; mean age, 54.9 ± 14.0 years) with early acute pancreatitis were included. Serum lipase levels and follow-up examinations served as the reference standard. A matched control group (n = 45) was assembled for evaluation of material decomposition values of normal pancreatic parenchyma. Three blinded radiologists independently interpreted all cases on conventional grayscale DECT series. In addition, readers re-evaluated all cases by manually performing region-of-interest (ROI) measurements on pancreatic-phase DECT material density images of the head, body, and tail of each patient’s pancreas. Receiver operating characteristic (ROC) curve analysis was performed to estimate the optimal threshold for discriminating between inflammatory and normal pancreas parenchyma.ResultsDECT-based iodine density values showed significant differences between inflammatory (1.8 ± 0.3 mg/mL) and normal pancreatic parenchyma (2.7 ± 0.7 mg/mL) (p ≤ 0.01). Fat fraction measurements showed no significant differences (p = 0.08). The optimal iodine density threshold for the diagnosis of acute pancreatitis was 2.1 mg/mL with a sensitivity of 96% and specificity of 77%. Iodine quantification revealed an area under the curve (AUC) of 0.86, significantly higher compared to standard image evaluation of the radiologists (AUC, 0.80; sensitivity, 78%; specificity, 82%) (p < 0.01).ConclusionDECT using iodine quantification allows for diagnosis of early acute pancreatitis with higher sensitivity compared to standard image evaluation.Key Points• Iodine density values showed significant differences between inflammatory and normal pancreatic parenchyma.• DECT using iodine quantification allows for diagnosis of early acute pancreatitis.• An iodine density of ≤ 2.1 mg/mL optimizes the diagnosis of acute pancreatitis.
Journal Article
CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients
We aimed to identify hepatocellular carcinoma (HCC) patients who will respond to repetitive transarterial chemoembolization (TACE) to improve the treatment algorithm. Retrospectively, 61 patients (mean age, 65.3 years ± 10.0 [SD]; 49 men) with 94 HCC mRECIST target-lesions who had three consecutive TACE between 01/2012 and 01/2020 were included. Robust and non-redundant radiomics features were extracted from the 24 h post-embolization CT. Five different clinical TACE-scores were assessed. Seven different feature selection methods and machine learning models were used. Radiomics, clinical and combined models were built to predict response to TACE on a lesion-wise and patient-wise level as well as its impact on overall-survival prognostication. 29 target-lesions of 19 patients were evaluated in the test set. Response rates were 37.9% (11/29) on the lesion-level and 42.1% (8/19) on the patient-level. Radiomics top lesion-wise response prognostications was AUC 0.55–0.67. Clinical scores revealed top AUCs of 0.65–0.69. The best working model combined the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical score mHAP_II_score_group with AUC = 0.70, accuracy = 0.72. We transferred this model on a patient-level to achieve AUC = 0.62, CI = 0.41–0.83. The two radiomics-clinical features revealed overall-survival prognostication of C-index = 0.67. In conclusion, a random forest model using the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical mHAP-II-score-group seems promising for TACE response prognostication.
Journal Article
Differential radiological features of patients infected or colonised with slow-growing non-tuberculous mycobacteria
Non-tuberculous mycobacterial pulmonary disease (NTM-PD) is considered a growing health concern. The majority of NTM-PD cases in Europe are caused by slow-growing mycobacteria (SGM). However, distinct radiological features of different SGM remain largely uninvestigated. We applied a previously described radiological score to a patient cohort consisting of individuals with isolation of different SGM. Correlations between clinical data, species and computed tomography (CT) features were examined by logistic and linear regression analyses, as well as over the course of time. Overall, 135 pulmonary CT scans from 84 patients were included. The isolated NTM-species were mainly
Mycobacterium avium
complex (MAC, n = 49), as well as 35 patients with non-MAC-species. Patients with isolation of
M. intracellulare
had more extensive CT findings compared to all other SGM species (coefficient 3.53, 95% Cl − 0.37 to 7.52, p = 0.075) while patients meeting the ATS criteria and not undergoing therapy exhibited an increase in CT scores over time. This study provides insights into differential radiological features of slow-growing NTM. While
M. intracellulare
exhibited a tendency towards higher overall CT scores, the radiological features were similar across different SGM. The applied CT score might be a useful instrument for monitoring patients and could help to guide antimycobacterial therapy.
Journal Article
Value of a noise-optimized virtual monoenergetic reconstruction technique in dual-energy CT for planning of transcatheter aortic valve replacement
Objectives
To evaluate objective and subjective image quality of a noise-optimized virtual monoenergetic imaging (VMI+) reconstruction technique in dual-energy computed tomography (DECT) angiography prior to transcatheter aortic valve replacement (TAVR).
Methods
Datasets of 47 patients (35 men; 64.1 ± 10.9 years) who underwent DECT angiography of heart and vascular access prior to TAVR were reconstructed with standard linear blending (F_0.5), VMI+, and traditional monoenergetic (VMI) algorithms in 10-keV intervals from 40–100 keV. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of 564 arterial segments were evaluated. Subjective analysis was rated by three blinded observers using a Likert scale.
Results
Mean SNR and CNR were highest in 40 keV VMI+ series (SNR, 27.8 ± 13.0; CNR, 26.3 ± 12.7), significantly (all
p
< 0.001) superior to all VMI series, which showed highest values at 70 keV (SNR, 18.5 ± 7.6; CNR, 16.0 ± 7.4), as well as linearly-blended F_0.5 series (SNR, 16.8 ± 7.3; CNR, 13.6 ± 6.9). Highest subjective image quality scores were observed for 40, 50, and 60 keV VMI+ reconstructions (all
p
> 0.05), significantly superior to all VMI and standard linearly-blended images (all
p
< 0.01).
Conclusions
Low-keV VMI+ reconstructions significantly increase CNR and SNR compared to VMI and standard linear-blending image reconstruction and improve subjective image quality in preprocedural DECT angiography in the context of TAVR planning.
Key Points
•
VMI+ combines increased contrast with reduced image noise
.
•
VMI+ shows substantially less image noise than traditional VMI
.
•
40-keV reconstructions show highest SNR/CNR of the aortic and iliofemoral access route
.
•
Observers overall prefer 60 keV VMI+ images
.
•
VMI+ DECT imaging helps improve image quality for TAVR planning
.
Journal Article
Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
Background
The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer.
Methods
In this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student’s t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used.
Results
Malignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall
P
< .001, respectively) and iodine uptake (overall
P
< .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955–1.0;
P
< .001) for radiomics features, ≥ 0.852 (95% CI, 0.767–0.914;
P
< .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587–0.780;
P
= .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10–44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697–0.864],
P
= .01).
Conclusions
Our reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality.
Journal Article
Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT
Purpose
The radiologists’ workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes.
Methods
Retrospectively, 72 patients [
m
, 47; age, 63.5 (27–87) years] with nodal lymphoma (
n
= 27) or benign abdominal lymph nodes (
n
= 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test.
Results
About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435–0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000–1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly (
p
= 0.011, DeLong) superior to the DECT model.
Conclusions
Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment.
Journal Article
Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method
Background
Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method.
Methods
Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method.
Results
Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years;
p
< 0.001). The correlation between AI-derived BA and reference BA (
r
= 0.99) was significantly higher than between reader-calculated BA and reference BA (
r
= 0.90;
p
< 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (
p =
0.241). Mean reading times were reduced by 87% using the AI system.
Conclusions
A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.
Journal Article
Diagnostic Value of DECT-Based Collagen Mapping for Assessing the Distal Tibiofibular Syndesmosis in Patients with Acute Trauma
Background: Injury to the distal tibiofibular syndesmosis (DTFS) is common in patients with trauma to the ankle, but diagnostic accuracy of conventional X-ray and CT is insufficient. A novel dual energy CT (DECT) post-processing algorithm enables color-coded mapping of collagenous structures, which can be utilized to assess the integrity of the DTFS. Methods: Patients were included in this retrospective study if they underwent third-generation dual-source DECT followed by 3T-MRI or ankle joint surgery within 14 days between January 2016 and December 2021. Three radiologists blinded to all patient data independently evaluated grayscale images and, after 8 weeks, grayscale and collagen mapping images for the presence of ligamentous injury or avulsion fractures of the DTFS. MRI and surgery provided the reference standard. Diagnostic accuracy parameters were calculated for all ratings, and a comparison of ROC curve analysis was performed to evaluate the incremental diagnostic value of color-coded images over grayscale images. Results: A total of 49 patients (median age 49 years; 32 males) were evaluated. Application of collagen mapping significantly increased sensitivity (25/30 [83%] vs. 20/30 [67%]), specificity (110/118 [93%] vs. 70/118 [60%]), positive predictive value (25/33 [76%] vs. 20/67 [30%]), negative predictive value (110/115 [96%] vs. 70/80 [88%]), and accuracy (134/147 [91%] vs. 90/147 [61%]) for the detection of injury to the DTFS (all parameters, p < 0.001). Collagen mapping achieved higher diagnostic confidence, image quality, and noise scores compared to grayscale CT (all parameters, p < 0.001). Conclusions: Collagen mapping yields substantially higher diagnostic accuracy and confidence for assessing the integrity of the distal tibiofibular syndesmosis compared to grayscale CT in patients with acute trauma. The application of this algorithm can accelerate the adequate diagnosis and treatment of DTFS injury in clinical routine.
Journal Article