MRI-Based Radiomics as a Promising Noninvasive Diagnostic Technique for Adenomyosis.

Background: MRI diagnostics are important for adenomyosis, especially in cases with inconclusive ultrasound. This study assessed the potential of MRI-based radiomics as a novel tool for differentiating between uteri with and without adenomyosis. Methods: This retrospective proof-of-principle single-center study included nine patients with and six patients without adenomyosis. All patients had preoperative T2w MR images and histological findings served as the reference standard. The uterus of each patient was segmented in 3D using dedicated software, and 884 radiomics features were extracted. After dimension reduction and feature selection, the diagnostic yield of individual and combined features implemented in the machine learning models were assessed by means of receiver operating characteristics analyses. Results: Eleven relevant radiomics features were identified. The diagnostic performance of individual features in differentiating adenomyosis from the control group was high, with areas under the curve (AUCs) ranging from 0.78 to 0.98. The performance of ML models incorporating several features was excellent, with AUC scores of 1 and an area under the precision-recall curve of 0.4. Conclusions: The set of radiomics features derived from routine T2w MRI enabled accurate differentiation of uteri with adenomyosis. Radiomics could enhance diagnosis and furthermore serve as an imaging biomarker to aid in personalizing therapies and monitoring treatment responses.

Evaluating the biodistribution for [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET/CT with an inter- and intrapatient based analysis.

BACKGROUND: Liver uptake in [68Ga]Ga-PSMA-11 PET is used as an internal reference in addition to clinical parameters to select patients for [177Lu]Lu-PSMA-617 radioligand therapy (RLT). Due to increased demand, [68Ga]Ga-PSMA-11 was replaced by [18F]F-PSMA-1007, a more lipophilic tracer with different biodistribution and splenic uptake was suggested as a new internal reference. We compared the intra-patient tracer distribution between [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007. METHODS: Fifty patients who underwent PET examinations in two centers with both [18F]F-PSMA-1007 and [68Ga]Ga-PSMA-11 within one year were included. Mean standardized uptake values (SUVmean) were obtained for liver, spleen, salivary glands, blood pool, and bone. Primary tumor, local recurrence, lymph node, bone or visceral metastasis were also assessed for intra- and inter-individual comparison. RESULTS: Liver SUVmean was significantly higher with [18F]F-PSMA-1007 (11.7 ± 3.9) compared to [68Ga]Ga-PSMA-11 (5.4 ± 1.7, p < .05) as well as splenic SUVmean (11.2 ± 3.5 vs.8.1 ± 3.5, p < .05). The blood pool was comparable between the two scans. Malignant lesions did not show higher SUVmean on [18F]F-PSMA-1007. Intra-individual comparison of liver uptake between the two scans showed a linear association for liver uptake with SUVmean [68Ga]Ga-PSMA-11 = 0.33 x SUVmean [18F]F-PSMA-1007 + 1.52 (r = .78, p < .001). CONCLUSION: Comparing biodistribution of [68Ga]Ga and [18F]F tracers, liver uptake on [68Ga]Ga-PSMA-11 PET is the most robust internal reference value. Liver uptake of [18F]F-PSMA-1007 was significantly higher, but so was the splenic uptake. The strong intra-individual association of hepatic accumulation between the two scans may allow using of a conversion factor for [18F]F-PSMA-1007 as a basis for RLT selection.

Quality of Life and Independent Factors Associated with Poor Digestive Function after Ivor Lewis Esophagectomy.

Transthoracic esophagectomy results in a radical change in foregut anatomy with multiple consequences for digestive physiology. The aim of this study was to identify factors associated with poor functional outcomes by assessing multiple dimensions of digestive performance and health-related quality of life (HRQL). Patients with cancer-free survival after Ivor Lewis esophagectomy were included. Four functional syndromes (dysphagia, gastroesophageal reflux disease (GERD), delayed gastric conduit emptying (DGCE), and dumping syndrome (DS)) and HRQL were assessed using specifically designed questionnaires. Patient outcomes were compared with healthy controls. Independent factors associated with poor digestive performance were identified through multivariable analysis. Sixty-five postoperative patients and 50 healthy volunteers participated in this study. Compared with controls, patients had worse outcomes for dysphagia, GERD, DS, and HRQL, but not for DGCE. A multivariate analysis showed a significant correlation of reduced digestive performance with ASA score, squamous cell carcinoma, open or hybrid surgical approach, and (neo)adjuvant therapy. In contrast, no individual patient factor was found to be associated with dumping syndrome. Digestive function and HRQL are substantially impaired after Ivor Lewis esophagectomy for cancer. Comorbid patients undergoing multimodal treatment and open access surgery for squamous cell carcinoma have the highest risk of poor functional outcome.

Smoking trends and health equity in Switzerland between 1992 and 2017: dependence of smoking prevalence on educational level and social determinants.

Background: Switzerland ranks among the top three healthcare systems in the world with regards to healthcare access, suggesting a high degree of health equity. However, Switzerland has few preventive strategies against smoking abuse. The aim of this study is to clarify whether educational level and citizenship status have an influence on the prevalence of smoking in Switzerland and whether there is health inequity related to a lack of preventive strategies. Methods: We based our analysis on publicly available health data published in the Swiss government's Swiss health survey (1992-2017). We compared the prevalence of smoking across the years and correlated these data with levels of educational attainment, citizenship status and age. Results: A continuous significant decline in smokers is observed in the highest education group (TERT). Over time, prevalence was reduced from 29% in 1992 to 23% in 2017 (p < 0.001). The intermediate-level educational group (SEK 2) showed smaller but also significant decline on a 0.05 sigificance level over the same period, from 31% to 29% (p = 0.003). The lowest educational group showed a nonsignificant decline from 28% to 27% (p = 0.6). The population who holds Swiss citizenship showed a decrease in smoking from 28% to 26% within the time frame (p < 0.001). People without Swiss citizenship had a much higher prevalence of smokers, at 38% in 1992 and declining to 32% in 2017 (p < 0.001). All cohorts from age 15 to age 64 have a far higher prevalence of smokers than cohorts at an older age, with the highest prevalence in the 25-34 age group. Conclusion: In Switzerland, individuals with lower levels of education and non-Swiss populations are more susceptible to health risk of smoking. This is despite the existence of a high-quality healthcare system that has nevertheless failed to negated health inequities.

Sinonasal mucosal melanoma treatment response assessment to immune checkpoint inhibitors using hybrid positron emission tomography imaging.

The purpose of this retrospective study was to investigate response of sinonasal mucosal melanoma (SMM) patients to treatment with immune checkpoint inhibitors (ICI), using hybrid PET imaging. Fifteen SMM patients underwent hybrid PET imaging before and three months after initiation of ICI. The disease-specific survival (DSS) was calculated. Quantitative PET parameters of the primary tumor and their association with DSS and therapy response were investigated. Nine of the fifteen (60%) patients responded to ICI therapy. Patients with therapy response depicted on hybrid PET imaging had better DSS than those without (p = 0.0058). Quantitative PET parameters of the initial PET harbored no association with DSS or therapy response. However, these findings lack of sufficient statistical power and must be interpreted with caution. The first restaging PET-imaging after ICI initiation can help stratify patients with regard to DSS.

Understanding the needs of women undergoing breast ultrasound: Are male radiologists still needed?

INTRODUCTION: A trend towards less male radiologists specializing in breast ultrasound was observed. A common notion in the field of breast radiology is, that female patients feel more comfortable being treated by female radiologists. The aim of the study was to understand and report the needs of women undergoing breast ultrasound with regards to the sex of the radiologist performing the investigation. METHODS: Informed consent was obtained from all patients prior to inclusion in a prospective bi-center quality study. At center 1 (72 patients), the women were examined exclusively by female radiologists, at center 2 (100 patients) only by male radiologists. After the examination the patients were asked about their experiences and their wishes for the future. RESULTS: Overall, women made no distinction between female and male radiologists; 25% of them wanted a female radiologist and 1.2% wanted a male radiologist. The majority (74%) stated that it made no difference whether a female or male radiologist performed the examination. The majority of women in group 2, who were investigated exclusively by male radiologists, stated that they had no preferences with regard to the sex of the radiologist (93%); 5% of the women wished to be investigated solely by a female radiologist and 2% exclusively by a male radiologist. DISCUSSION: The majority of women undergoing breast ultrasound are unconcerned about the radiologist's sex. It would appear that women examined by male radiologists are less selective about the sex of the examining radiologist. TRIAL REGISTRATION: Written informed consent was obtained from all patients. All patient data were anonymized. The physicians had no access to any further personal data. National regulations did not require dedicated ethics approval with anonymized lists or retrospective questionnaires.

Characterization of hypermetabolic lymph nodes after SARS-CoV-2 vaccination using PET-CT derived node-RADS, in patients with melanoma.

This study aimed to evaluate the diagnostic accuracy of Node Reporting and Data System (Node-RADS) in discriminating between normal, reactive, and metastatic axillary LNs in patients with melanoma who underwent SARS-CoV-2 vaccination. Patients with proven melanoma who underwent a 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]-FDG PET/CT) between February and April 2021 were included in this retrospective study. Primary melanoma site, vaccination status, injection site, and 2-[18F]-FDG PET/CT were used to classify axillary LNs into normal, inflammatory, and metastatic (combined classification). An adapted Node-RADS classification (A-Node-RADS) was generated based on LN anatomical characteristics on low-dose CT images and compared to the combined classification. 108 patients were included in the study (54 vaccinated). HALNs were detected in 42 patients (32.8%), of whom 97.6% were vaccinated. 172 LNs were classified as normal, 30 as inflammatory, and 14 as metastatic using the combined classification. 152, 22, 29, 12, and 1 LNs were classified A-Node-RADS 1, 2, 3, 4, and 5, respectively. Hence, 174, 29, and 13 LNs were deemed benign, equivocal, and metastatic. The concordance between the classifications was very good (Cohen's k: 0.91, CI 0.86-0.95; p-value < 0.0001). A-Node-RADS can assist the classification of axillary LNs in melanoma patients who underwent 2-[18F]-FDG PET/CT and SARS-CoV-2 vaccination.

Evolution of CT radiation dose in pediatric patients undergoing hybrid 2-[18F]FDG PET/CT between 2007 and 2021.

OBJECTIVES: To evaluate the evolution of CT radiation dose in pediatric patients undergoing hybrid 2-[18F]fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET/CT between 2007 and 2021. METHODS AND MATERIALS: Data from all pediatric patients aged 0-18 years who underwent hybrid 2-[18F]FDG PET/CT of the body between January 2007 and May 2021 were reviewed. Demographic and imaging parameters were collected. A board-certified radiologist reviewed all CT scans and measured image noise in the brain, liver, and adductor muscles. RESULTS: 294 scans from 167 children (72 females (43%); median age: 14 (IQR 10-15) years; BMI: median 17.5 (IQR 15-20.4) kg/m2) were included. CT dose index-volume (CTDIvol) and dose length product (DLP) both decreased significantly from 2007 to 2021 (both p < 0.001, Spearman's rho coefficients -0.46 and -0.35, respectively). Specifically, from 2007 to 2009 to 2019-2021 CTDIvol and DLP decreased from 2.94 (2.14-2.99) mGy and 309 (230-371) mGy*cm, respectively, to 0.855 (0.568-1.11) mGy and 108 (65.6-207) mGy*cm, respectively. From 2007 to 2021, image noise in the brain and liver remained constant (p = 0.26 and p = 0.06), while it decreased in the adductor muscles (p = 0.007). Peak tube voltage selection (in kilovolt, kV) of CT scans shifted from high kV imaging (140 or 120kVp) to low kV imaging (100 or 80kVp) (p < 0.001) from 2007 to 2021. CONCLUSION: CT radiation dose in pediatric patients undergoing hybrid 2-[18F]FDG PET/CT has decreased in recent years equaling approximately one-third of the initial amount. ADVANCES IN KNOWLEDGE: Over the past 15 years, CT radiation dose decreased considerably in pediatric patients undergoing hybrid imaging, while objective image quality may not have been compromised.

Impact of radiation dose on the detection of interstitial lung changes and image quality in low-dose chest CT - Assessment in multiple dose levels from a single patient scan.

PURPOSE: To assess image quality and detectability of interstitial lung changes using multiple radiation doses from the same chest CT scan of patients with suspected interstitial lung disease (ILD). METHOD: Retrospective study of consecutive adult patients with suspected ILD receiving unenhanced chest CT as single-energy dual-source acquisition at 100 kVp (Dual-split mode). 67% and 33% of the overall tube current time product were assigned to tube A and B, respectively. 100%-dose was 2.34 ± 0.97 mGy. Five different radiation doses (100%, 67%, 45%, 39%, 33%) were reconstructed from this single acquisition using linear-blending technique. Two blinded radiologists assessed reticulations, ground-glass opacities (GGO) and honeycombing as well as subjective image noise. Percentage agreement (PA) as compared to 100%-dose were calculated. Non-parametric statistical tests were used. RESULTS: A total of 228 patients were included (61.2 ± 14.6 years,146 female). PA was highest for honeycombing (>96%) and independent of dose reduction (P > 0.8). PA for reticulations and GGO decreased when reducing the radiation dose from 100% to 67% for both readers (reticulations: 83.3% and 93.9%; GGO: 87.7% and 79.8% for reader 1 and 2, respectively). Additional dose reduction did not significantly change PA for both readers (all P > 0.05). Subjective image noise increased with decreasing radiation dose (Spearman Rho of ρ = 0.34 and ρ = 0.53 for reader 1 and 2, respectively, P < 0.001). CONCLUSIONS: Radiation dose reduction had a stronger impact on subtle interstitial lung changes. Detectability decreased with initial dose reduction indicating that a minimum dose is needed to maintain diagnostic accuracy in chest CT for suspected ILD.

Photon-Counting Detector CT Angiography for Endoleak Detection After Endovascular Aortic Repair: Triphasic CT With True Noncontrast Versus Biphasic CT With Virtual Noniodine Imaging.

OBJECTIVES: The aim of this study was to compare image quality and endoleak detection after endovascular abdominal aortic aneurysm repair between a triphasic computed tomography (CT) with true noncontrast (TNC) and a biphasic CT with virtual noniodine (VNI) images on photon-counting detector CT (PCD-CT). MATERIALS AND METHODS: Adult patients after endovascular abdominal aortic aneurysm repair who received a triphasic examination (TNC, arterial, venous phase) on a PCD-CT between August 2021 and July 2022 were retrospectively included. Endoleak detection was evaluated by 2 blinded radiologists on 2 different readout sets (triphasic CT with TNC-arterial-venous vs biphasic CT with VNI-arterial-venous). Virtual noniodine images were reconstructed from the venous phase. The radiologic report with additional confirmation by an expert reader served as reference standard for endoleak presence. Sensitivity, specificity, and interreader agreement (Krippendorf α) were calculated. Image noise was assessed subjectively in patients using a 5-point scale and objectively calculating the noise power spectrum in a phantom. RESULTS: One hundred ten patients (7 women; age, 76 ± 8 years) with 41 endoleaks were included. Endoleak detection was comparable between both readout sets with a sensitivity and specificity of 0.95/0.84 (TNC) versus 0.95/0.86 (VNI) for reader 1 and 0.88/0.98 (TNC) versus 0.88/0.94 (VNI) for reader 2. Interreader agreement for endoleak detection was substantial (TNC: 0.716, VNI: 0.756). Subjective image noise was comparable between TNC and VNI (4; IQR [4, 5] vs 4; IQR [4, 5], P = 0.44). In the phantom, noise power spectrum peak spatial frequency was similar between TNC and VNI (both f peak = 0.16 mm -1 ). Objective image noise was higher in TNC (12.7 HU) as compared with VNI (11.5 HU). CONCLUSIONS: Endoleak detection and image quality were comparable using VNI images in biphasic CT as compared with TNC images in triphasic CT offering the possibility to reduce scan phases and radiation exposure.

Pseudoenhancement in Cystic Renal Lesions - Impact of Virtual Monoenergetic Images of Photon-Counting Detector CT on Lesion Classification.

RATIONALE AND OBJECTIVES: To investigate the impact of virtual monoenergetic images (VMI) from photon-counting detector CT (PCD-CT) on the enhancement and classification of renal cysts. MATERIALS AND METHODS: Adults with renal cysts (≥7 mm) who received a triphasic examination on a clinical PCD-CT (120 kVp; IQ level 68) between July 2021 and March 2022 were retrospectively identified. Only non-enhancing cysts (enhancement<10 HU between unenhanced and venous phase at 70 keV) were included. VMI from 40 to 190 keV with increments of 10 keV were reconstructed from the venous phase. Enhancement was measured to classify each lesion as non-enhancing (<10 HU), equivocally enhancing (10-19 HU), and definitely enhancing (≥20 HU). Classification changes as a function of VMI were assessed. Pearson correlation coefficient, the Kruskal-Wallis and the Chi-square test were used. RESULTS: A total of 86 patients (mean age, 74 ± 9 years; 74 male) with 160 non-enhancing renal cysts (17.6 ± 10 mm) were included. CT attenuation of the cysts increased from higher to lower VMI levels with a mean attenuation of 4 ± 11 HU at 190 keV to 36 ± 17 HU at 40 keV. Mean attenuation of the renal parenchyma was 43 ± 4 HU at 190 keV and 414 ± 71 HU at 40 keV. No cyst exhibited enhancement from 70 keV to 190 keV. At 40, 50, and 60 keV, 35% (56/160), 29% (47/160) and 9% (15/160) of cysts showed equivocal and 46% (74/160), 10% (16/160), and 0% (0/160) definite enhancement, respectively. There was no significant influence of size (P=.13), cyst location (P=.9) and BMI (P=.19) on enhancement classification. CONCLUSION: VMI has a relevant impact on enhancement and classification of renal cysts with misclassification in a large number of cases at energy levels below 70 keV.

Dual-energy CT kidney stone characterization-can diagnostic accuracy be achieved at low radiation dose?

OBJECTIVES: To assess the accuracy of low-dose dual-energy computed tomography (DECT) to differentiate uric acid from non-uric acid kidney stones in two generations of dual-source DECT with stone composition analysis as the reference standard. METHODS: Patients who received a low-dose unenhanced DECT for the detection or follow-up of urolithiasis and stone extraction with stone composition analysis between January 2020 and January 2022 were retrospectively included. Collected stones were characterized using X-ray diffraction. Size, volume, CT attenuation, and stone characterization were assessed using DECT post-processing software. Characterization as uric acid or non-uric acid stones was compared to stone composition analysis as the reference standard. Sensitivity, specificity, and accuracy of stone classification were computed. Dose length product (DLP) and effective dose served as radiation dose estimates. RESULTS: A total of 227 stones in 203 patients were analyzed. Stone composition analysis identified 15 uric acid and 212 non-uric acid stones. Mean size and volume were 4.7 mm × 2.8 mm and 114 mm3, respectively. CT attenuation of uric acid stones was significantly lower as compared to non-uric acid stones (p < 0.001). Two hundred twenty-five of 227 kidney stones were correctly classified by DECT. Pooled sensitivity, specificity, and accuracy were 1.0 (95%CI: 0.97, 1.00), 0.93 (95%CI: 0.68, 1.00), and 0.99 (95%CI: 0.97, 1.00), respectively. Eighty-two of 84 stones with a diameter of ≤ 3 mm were correctly classified. Mean DLP was 162 ± 57 mGy*cm and effective dose was 2.43 ± 0.86 mSv. CONCLUSIONS: Low-dose dual-source DECT demonstrated high accuracy to discriminate uric acid from non-uric acid stones even at small stone sizes. KEY POINTS: • Two hundred twenty-five of 227 stones were correctly classified as uric acid vs. non-uric acid stones by low-dose dual-energy CT with stone composition analysis as the reference standard. • Pooled sensitivity, specificity, and accuracy for stone characterization were 1.0, 0.93, and 0.99, respectively. • Low-dose dual-energy CT for stone characterization was feasible in the majority of small stones < 3 mm.

A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma.

The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen's Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.

Liver Iodine Quantification With Photon-Counting Detector CT: Accuracy in an Abdominal Phantom and Feasibility in Patients.

RATIONALE AND OBJECTIVES: To compare the accuracy of iodine quantification in liver parenchyma and lesions between dual-source photon-counting detector CT (PCD-CT) and dual-source energy-integrating detector CT (EID-CT) in a phantom and to demonstrate the feasibility of iodine quantification with PCD-CT in liver parenchyma and lesions in patients. MATERIALS AND METHODS: An anthropomorphic abdominal phantom with a liver insert containing parenchyma and lesions was imaged on a clinical PCD-CT at 120kV and in the dual-energy mode on an EID-CT with kV-combinations of 80/Sn150kV, 90/Sn150kV, and 100/Sn150kV. Three patient sizes were imaged at three different radiation doses (CTDIvol: 5, 10, 15mGy). Thirty patients with liver cysts, hemangiomas or metastases imaged with PCD-CT were retrospectively included. Iodine maps were reconstructed and iodine concentrations were measured in liver parenchyma and lesions. For the phantom, iodine error was quantified as the absolute difference to the vendor's specifications as reference. RESULTS: Overall iodine error was 0.33 ± 0.29, 0.34 ± 0.32, 0.39 ± 0.37, 0.35 ± 0.39 mgI/mL for 80/Sn150kV, 90/Sn150kV, 100/Sn150kV of EID-CT, and PCD-CT, respectively, without significant differences between PCD-CT and EID-CT (p > 0.05). Radiation dose did not significantly influence error of PCD-CT (p > 0.05) nor EID-CT (p > 0.05). For both scanners, smaller patient sizes were associated with lower errors (p < 0.05). Iodine concentration and base material attenuation significantly influenced quantification for EID-CT (p < 0.05) but not PCD-CT (p > 0.05). In patients, iodine quantification was feasible in liver parenchyma, cysts, hemangiomas, and metastases. CONCLUSION: Iodine quantification with PCD-CT is accurate in simulated liver parenchyma and lesions irrespective of radiation dose, iodine concentration, and base attenuation and is feasible in common liver lesions in patients.

Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT.

Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled: 50 patients with Agatston scores ≥ 1000 (high CACS group), 50 patients with Agatston scores < 1000 (negative control group). All patients underwent oncological 18F-FDG-PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on dedicated non-contrast ECG-gated CT scans obtained from SPECT-MPI (reference standard). Additionally, CACS was performed fully automatically with a user-independent DL-CACS tool on non-contrast, free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations. Image quality and noise of CT scans was assessed. Agatston scores obtained by manual CACS and DL tool were compared. The high CACS group had Agatston scores of 2200 ± 1620 (reference standard) and 1300 ± 1011 (DL tool, average underestimation of 38.6 ± 26%) with an intraclass correlation of 0.714 (95% CI 0.546, 0.827). Sufficient image quality significantly improved the DL tool's capability of correctly assigning Agatston scores ≥ 1000 (p = 0.01). In the control group, the DL tool correctly assigned Agatston scores < 1000 in all cases. In conclusion, DL-based CACS performed on non-contrast free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations of patients with known very high (≥ 1000) CAC underestimates CAC load, but correctly assigns an Agatston scores ≥ 1000 in over 70% of cases, provided sufficient CT image quality. Subgroup analyses of the control group showed that the DL tool does not generate false-positives.

Frequency and temporal evolution of COVID-19 vaccination rate among oncological patients undergoing 18F-FDG-PET.

PURPOSE: To evaluate the temporal evolution of vaccination against COVID-19 in a Swiss oncological cohort. METHODS: History of complete vaccination (i.e. at least two vaccine doses) against COVID-19 of patients undergoing oncological 18F-FDG PET/CT between February and September 2021 (n = 2613) was taken. Vaccination rate was compared with age-matched national data from the Swiss Federal Office of Public Health. Subgroup differences in temporal evolution of vaccination rate were analyzed by fitting a generalized linear model and determined by significant interaction between, sex, oncological diagnosis, and month of examination. RESULTS: Rate of complete vaccination against COVID-19 steadily increased and reached 81 % in September 2021. The fraction of vaccinated patients in the oncological cohort was higher in the beginning and approached the fraction in the age-matched general Swiss population at the end of the study period. Month of exam (p < 0.001) was the only significant predictor of the vaccination rate. CONCLUSION: Vaccination rate against COVID-19 in a Swiss oncological cohort increased steadily from February to September 2021. Compared to the age-matched general population it was higher in the beginning and similar by the end of the study period. Ethics approval: Trial registration: BASEC 2021-00444, Ethikkommission Zürich (Cantonal Ethics Committee Zurich), Switzerland, registered February 24th 2021.

Humor in radiological breast cancer screening: a way of improving patient service?

BACKGROUND: Breast cancer screening is essential in detecting breast tumors, however, the examination is stressful. In this study we analyzed whether humor enhances patient satisfaction. METHODS: In this prospective randomized study 226 patients undergoing routine breast cancer screening at a single center during October 2020 to July 2021 were included. One hundred thirty-two were eligible for the study. Group 1 (66 patients) received an examination with humorous intervention, group 2 (66 patients) had a standard breast examination. In the humor group, the regular business card was replaced by a self-painted, humorous business card, which was handed to the patient at the beginning of the examination. Afterwards, patients were interviewed with a standardized questionnaire. Scores between the two study groups were compared with the Mann-Whitney U test or Fisher's exact test. P-values were adjusted with the Holm's method. Two-sided p-values < 0.05 were considered significant. RESULTS: One hundred thirty-two patients, 131 female and 1 male, (mean age 59 ± 10.6 years) remained in the final study cohort. Patients in the humor group remembered the radiologist's name better (85%/30%, P < .001), appreciated the final discussion with the radiologist more (4.67 ± 0.73-5;[5, 5] vs. 4.24 ± 1.1-5;[4, 5], P = .017), felt the radiologist was more empathetic (4.94 ± 0.24-5;[5, 5] vs.4.59 ± 0.64-5;[4, 5], P < .001), and rated him as a humorous doctor (4.91 ± 0.29-5;[5, 5] vs. 2.26 ± 1.43-1;[1, 4], P < .001). Additionally, patients in the humor group tended to experience less anxiety (p = 0.166) and felt the doctor was more competent (p = 0.094). CONCLUSION: Humor during routine breast examinations may improve patient-radiologist relationship because the radiologist is considered more empathetic and competent, patients recall the radiologist's name more easily, and value the final discussion more. TRIAL REGISTRATION: We have a general approval from our ethics committee because it is a retrospective survey, the patient lists for the doctors were anonymized and it is a qualitative study, since the clinical processes are part of the daily routine examinations and are used independently of the study. The patients have given their consent to this study and survey.

Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent.

Objective: Our aim is to define the capabilities of radiomics in predicting pseudoprogression from pre-treatment MR images in patients diagnosed with high-grade gliomas using T1 non-contrast-enhanced and contrast-enhanced images. Material & methods: In this retrospective IRB-approved study, image segmentation of high-grade gliomas was semi-automatically performed using 3D Slicer. Non-contrast-enhanced T1-weighted images and contrast-enhanced T1-weighted images were used prior to surgical therapy or radio-chemotherapy. Imaging data was split into a training sample and an independent test sample at random. We extracted 107 radiomic features by use of PyRadiomics. Feature selection and model construction were performed using Generalized Boosted Regression Models (GBM). Results: Our cohort included 124 patients (female: n = 53), diagnosed with progressive (n = 61) and pseudoprogressive disease (n = 63) of primary high-grade gliomas. Based on non-contrast-enhanced T1-weighted images of the independent test sample, the mean area under the curve (AUC), mean sensitivity, mean specificity and mean accuracy of our model were 0.651 [0.576, 0.761], 0.616 [0.417, 0.833], 0.578 [0.417, 0.750] and 0.597 [0.500, 0.708] to predict the development of pseudoprogression. In comparison, the independent test data of contrast-enhanced T1-weighted images yielded significantly higher values of AUC = 0.819 [0.760, 0.872], sensitivity = 0.817 [0.750, 0.833], specificity = 0.723 [0.583, 0.833] and accuracy = 0.770 [0.687, 0.833]. Conclusion: Our findings show that it is possible to predict pseudoprogression of high-grade gliomas with a Radiomics model using contrast-enhanced T1-weighted images with comparatively good discriminatory power. The use of a contrast agent results in a clear added value.

Assessing preoperative risk of STR in skull meningiomas using MR radiomics and machine learning.

Our aim is to predict possible gross total and subtotal resections of skull meningiomas from pre-treatment T1 post contrast MR-images using radiomics and machine learning in a representative patient cohort. We analyse the accuracy of our model predictions depending on the tumor location within the skull and the postoperative tumor volume. In this retrospective, IRB-approved study, image segmentation of the contrast enhancing parts of the tumor was semi-automatically performed using the 3D Slicer open-source software platform. Imaging data were split into training data and independent test data at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest on T1 post contrast MR images. Feature preselection and model construction were performed with eight different machine learning algorithms. Each model was estimated 100 times on new training data and then tested on a previously unknown, independent test data set to avoid possible overfitting. Our cohort included 138 patients. A gross total resection of the meningioma was performed in 107 cases and a subtotal resection in the remaining 31 cases. Using the training data, the mean area under the curve (AUC), mean accuracy, mean kappa, mean sensitivity and mean specificity were 0.901, 0.875, 0.629, 0.675 and 0.933 respectively. We obtained very similar results with the independent test data: mean AUC = 0.900, mean accuracy = 0.881, mean kappa = 0.644, mean sensitivity = 0.692 and mean specificity = 0.936. Thus, our model exposes good and stable predictive performance with both training and test data. Our radiomics approach shows that with machine learning algorithms and comparatively few explanatory factors such as the location of the tumor within the skull as well as its shape, it is possible to make accurate predictions about whether a meningioma can be completely resected by surgery. Complete resections and resections with larger postoperative tumor volumes can be predicted with very high accuracy. However, cases with very small postoperative tumor volumes are comparatively difficult to predict correctly.