Checkpoint-inhibitor induced Polyserositis with Edema.

BACKGROUND: As immune checkpoint inhibitors (ICI) are increasingly being used due to effectiveness in various tumor entities, rare side effects occur more frequently. Pericardial effusion has been reported in patients with advanced non-small cell lung cancer (NSCLC) after or under treatment with immune checkpoint inhibitors. However, knowledge about serositis and edemas induced by checkpoint inhibitors in other tumor entities is scarce. METHODS AND RESULTS: Four cases with sudden onset of checkpoint inhibitor induced serositis (irSerositis) are presented including one patient with metastatic cervical cancer, two with metastatic melanoma and one with non-small cell lung cancer (NSCLC). In all cases treatment with steroids was successful in the beginning, but did not lead to complete recovery of the patients. All patients required multiple punctures. Three of the patients presented with additional peripheral edema; in one patient only the lower extremities were affected, whereas the entire body, even face and eyelids were involved in the other patients. In all patients serositis was accompanied by other immune-related adverse events (irAEs). CONCLUSION: ICI-induced serositis and effusions are complex to diagnose and treat and might be underdiagnosed. For differentiation from malignant serositis pathology of the punctured fluid can be helpful (lymphocytes vs. malignant cells). Identifying irSerositis as early as possible is essential since steroids can improve symptoms.

Cost-effectiveness of short-protocol emergency brain MRI after negative non-contrast CT for minor stroke detection.

OBJECTIVES: To investigate the cost-effectiveness of supplemental short-protocol brain MRI after negative non-contrast CT for the detection of minor strokes in emergency patients with mild and unspecific neurological symptoms. METHODS: The economic evaluation was centered around a prospective single-center diagnostic accuracy study validating the use of short-protocol brain MRI in the emergency setting. A decision-analytic Markov model distinguished the strategies "no additional imaging" and "additional short-protocol MRI" for evaluation. Minor stroke was assumed to be missed in the initial evaluation in 40% of patients without short-protocol MRI. Specialized post-stroke care with immediate secondary prophylaxis was assumed for patients with detected minor stroke. Utilities and quality-of-life measures were estimated as quality-adjusted life years (QALYs). Input parameters were obtained from the literature. The Markov model simulated a follow-up period of up to 30 years. Willingness to pay was set to $100,000 per QALY. Cost-effectiveness was calculated and deterministic and probabilistic sensitivity analysis was performed. RESULTS: Additional short-protocol MRI was the dominant strategy with overall costs of $26,304 (CT only: $27,109). Cumulative calculated effectiveness in the CT-only group was 14.25 QALYs (short-protocol MRI group: 14.31 QALYs). In the deterministic sensitivity analysis, additional short-protocol MRI remained the dominant strategy in all investigated ranges. Probabilistic sensitivity analysis results from the base case analysis were confirmed, and additional short-protocol MRI resulted in lower costs and higher effectiveness. CONCLUSION: Additional short-protocol MRI in emergency patients with mild and unspecific neurological symptoms enables timely secondary prophylaxis through detection of minor strokes, resulting in lower costs and higher cumulative QALYs. KEY POINTS: • Short-protocol brain MRI after negative head CT in selected emergency patients with mild and unspecific neurological symptoms allows for timely detection of minor strokes. • This strategy supports clinical decision-making with regard to immediate initiation of secondary prophylactic treatment, potentially preventing subsequent major strokes with associated high costs and reduced QALY. • According to the Markov model, additional short-protocol MRI remained the dominant strategy over wide variations of input parameters, even when assuming disproportionally high costs of the supplemental MRI scan.

The relevance of CT-based geometric and radiomics analysis of whole liver tumor burden to predict survival of patients with metastatic colorectal cancer.

OBJECTIVES: To investigate the prediction of 1-year survival (1-YS) in patients with metastatic colorectal cancer with use of a systematic comparative analysis of quantitative imaging biomarkers (QIBs) based on the geometric and radiomics analysis of whole liver tumor burden (WLTB) in comparison to predictions based on the tumor burden score (TBS), WLTB volume alone, and a clinical model. METHODS: A total of 103 patients (mean age: 61.0 ± 11.2 years) with colorectal liver metastases were analyzed in this retrospective study. Automatic segmentations of WLTB from baseline contrast-enhanced CT images were used. Established biomarkers as well as a standard radiomics model building were used to derive 3 prognostic models. The benefits of a geometric metastatic spread (GMS) model, the Aerts radiomics prior model of the WLTB, and the performance of TBS and WLTB volume alone were assessed. All models were analyzed in both statistical and predictive machine learning settings in terms of AUC. RESULTS: TBS showed the best discriminative performance in a statistical setting to discriminate 1-YS (AUC = 0.70, CI: [0.56, 0.90]). For the machine learning-based prediction for unseen patients, both a model of the GMS of WLTB (0.73, CI: [0.60, 0.84]) and the Aerts radiomics prior model (0.76, CI: [0.65, 0.86]) applied on the WLTB showed a numerically higher predictive performance than TBS (0.68, CI: [0.54, 0.79]), radiomics (0.65, CI: [0.55, 0.78]), WLTB volume alone (0.53, CI: [0.40. 0.66]), or the clinical model (0.56, CI: [0.43, 0.67]). CONCLUSIONS: The imaging-based GMS model may be a first step towards a more fine-grained machine learning extension of the TBS concept for risk stratification in mCRC patients without the vulnerability to technical variance of radiomics. KEY POINTS: • CT-based geometric distribution and radiomics analysis of whole liver tumor burden in metastatic colorectal cancer patients yield prognostic information. • Differences in survival are possibly attributable to the spatial distribution of metastatic lesions and the geometric metastatic spread analysis of all liver metastases may serve as robust imaging biomarker invariant to technical variation. • Imaging-based prediction models outperform clinical models for 1-year survival prediction in metastatic colorectal cancer patients with liver metastases.

Evaluation of patients with respiratory infections during the first pandemic wave in Germany: characteristics of COVID-19 versus non-COVID-19 patients.

BACKGROUND: Characteristics of COVID-19 patients have mainly been reported within confirmed COVID-19 cohorts. By analyzing patients with respiratory infections in the emergency department during the first pandemic wave, we aim to assess differences in the characteristics of COVID-19 vs. Non-COVID-19 patients. This is particularly important regarding the second COVID-19 wave and the approaching influenza season. METHODS: We prospectively included 219 patients with suspected COVID-19 who received radiological imaging and RT-PCR for SARS-CoV-2. Demographic, clinical and laboratory parameters as well as RT-PCR results were used for subgroup analysis. Imaging data were reassessed using the following scoring system: 0 - not typical, 1 - possible, 2 - highly suspicious for COVID-19. RESULTS: COVID-19 was diagnosed in 72 (32,9%) patients. In three of them (4,2%) the initial RT-PCR was negative while initial CT scan revealed pneumonic findings. 111 (50,7%) patients, 61 of them (55,0%) COVID-19 positive, had evidence of pneumonia. Patients with COVID-19 pneumonia showed higher body temperature (37,7 ± 0,1 vs. 37,1 ± 0,1 °C; p = 0.0001) and LDH values (386,3 ± 27,1 vs. 310,4 ± 17,5 U/l; p = 0.012) as well as lower leukocytes (7,6 ± 0,5 vs. 10,1 ± 0,6G/l; p = 0.0003) than patients with other pneumonia. Among abnormal CT findings in COVID-19 patients, 57 (93,4%) were evaluated as highly suspicious or possible for COVID-19. In patients with negative RT-PCR and pneumonia, another third was evaluated as highly suspicious or possible for COVID-19 (14 out of 50; 28,0%). The sensitivity in the detection of patients requiring isolation was higher with initial chest CT than with initial RT-PCR (90,4% vs. 79,5%). CONCLUSIONS: COVID-19 patients show typical clinical, laboratory and imaging parameters which enable a sensitive detection of patients who demand isolation measures due to COVID-19.

Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer.

BACKGROUND: Bladder cancer (BC) treatment algorithms depend on accurate tumor staging. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited. OBJECTIVE: To evaluate the performance of quantitative radiomics signatures for detection of LN metastases in BC. DESIGN, SETTING, AND PARTICIPANTS: Of 1354 patients with BC who underwent radical cystectomy (RC) with lymphadenectomy who were screened, 391 with pathological nodal staging (pN0: n = 297; pN+: n = 94) were included and randomized into training (n = 274) and test (n = 117) cohorts. Pelvic LNs were segmented manually and automatically. A total of 1004 radiomics features were extracted from each LN and a machine learning model was trained to assess pN status using histopathology labels as the ground truth. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Radiologist assessment was compared to radiomics-based analysis using manual and automated LN segmentations for detection of LN metastases in BC. Statistical analysis was performed using the receiver operating characteristics curve method and evaluated in terms of sensitivity, specificity, and area under the curve (AUC). RESULTS AND LIMITATIONS: In total, 1845 LNs were manually segmented. Automated segmentation correctly located 361/557 LNs in the test cohort. Manual and automatic masks achieved an AUC of 0.80 (95% confidence interval [CI] 0.69-0.91; p = 0.64) and 0.70 (95% CI: 0.58-0.82; p = 0.17), respectively, in the test cohort compared to radiologist assessment, with an AUC of 0.78 (95% CI 0.67-0.89). A combined model of a manually segmented radiomics signature and radiologist assessment reached an AUC of 0.81 (95% CI 0.71-0.92; p = 0.63). CONCLUSIONS: A radiomics signature allowed discrimination of nodal status with high diagnostic accuracy. The model based on manual LN segmentation outperformed the fully automated approach. PATIENT SUMMARY: For patients with bladder cancer, evaluation of computed tomography (CT) scans before surgery using a computer-based method for image analysis, called radiomics, may help in standardizing and improving the accuracy of assessment of lymph nodes. This could be a valuable tool for optimizing treatment options.

Prostatic Artery Embolization for Treatment of Lower Urinary Tract Symptoms: A Markov Model-Based Cost-Effectiveness Analysis.

PURPOSE: The aim of this study was to investigate whether prostatic artery embolization (PAE) can be considered a long-term cost-effective treatment option in patients with lower urinary tract symptoms secondary to benign prostatic hyperplasia in comparison to transurethral resection of the prostate (TURP). METHODS: The in-hospital costs of PAE and TURP in the United States were obtained from a recent cost analysis. Clinical outcomes including nature and rate of adverse events for TURP and PAE along with rates of retreatment because of complications or clinical failure were obtained from peer-reviewed literature. A decision tree-based Markov model was created, analyzing long-term cost-effectiveness for TURP and PAE from a US health care sector perspective. Cost-effectiveness over a time frame of 5 years was estimated while assuming a willingness to pay of $50,000 per quality-adjusted life-year (QALY). The primary outcome was incremental cost-effectiveness ratio. RESULTS: PAE resulted in overall cost of $6,464.92 and an expected outcome of 4.566 QALYs. In comparison, TURP cost $9,221.09 and resulted in expected outcome of 4.577 QALYs per treatment. The incremental cost-effectiveness ratio for TURP was $247,732.65 per QALY. On the basis of the willingness-to-pay threshold, PAE is cost effective compared with TURP. CONCLUSIONS: On the basis of our model, PAE in comparison with TURP can be regarded as a cost-effective treatment option for patients with lower urinary tract symptoms within the US health care system.

Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy.

(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.

Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets.

Background: Radiomics promises to enhance the discriminative performance for clinically significant prostate cancer (csPCa), but still lacks validation in real-life scenarios. This study investigates the classification performance and robustness of machine learning radiomics models in heterogeneous MRI datasets to characterize suspicious prostate lesions for non-invasive prediction of prostate cancer (PCa) aggressiveness compared to conventional imaging biomarkers. Methods: A total of 142 patients with clinical suspicion of PCa underwent 1.5T or 3T biparametric MRI (7 scanner types, 14 institutions) and exhibited suspicious lesions [prostate Imaging Reporting and Data System (PI-RADS) score ≥3] in peripheral or transitional zones. Whole-gland and index-lesion segmentations were performed semi-automatically. A total of 1,482 quantitative morphologic, shape, texture, and intensity-based radiomics features were extracted from T2-weighted and apparent diffusion coefficient (ADC)-images and assessed using random forest and logistic regression models. Five-fold cross-validation performance in terms of area under the ROC curve was compared to mean ADC (mADC), PI-RADS and prostate-specific antigen density (PSAD). Bias mitigation techniques targeting the high-dimensional feature space and inherent class imbalance were applied and robustness of results was systematically evaluated. Results: Trained models showed mean area under the curves (AUCs) ranging from 0.78 to 0.83 in csPCa classification. Despite using mitigation techniques, high performance variability of results could be demonstrated. Trained models achieved on average numerically higher classification performance compared to clinical parameters PI-RADS (AUC =0.78), mADC (AUC =0.71) and PSAD (AUC =0.63). Conclusions: Radiomics models' classification performance of csPCa was numerically but not significantly higher than PI-RADS scoring. Overall, clinical applicability in heterogeneous MRI datasets is limited because of high variability of results. Performance variability, robustness and reproducibility of radiomics-based measures should be addressed more transparently in future research to enable broad clinical application.

REDUCE - Indication catalogue based ordering of chest radiographs in intensive care units.

PURPOSE: To advance a transition towards an indication-based chest radiograph (CXR) ordering in intensive care units (ICUs) without compromising patient safety. MATERIALS AND METHODS: Single-center prospective cohort study with a retrospective reference group including 857 ICU patients. The routine group (n = 415) received CXRs at the discretion of the ICU physician, the restrictive group (n = 442) if specified by an indication catalogue. Documented data include number of CXRs per day and CXR radiation dose as primary outcomes, re-intubation and re-admission rates, hours of mechanical ventilation and ICU length of stay. RESULTS: CXR numbers were reduced in the restrictive group (964 CXRs in 2479 days vs. 1281 CXRs in 2318 days) and median radiation attributed to CXR per patient was significantly lowered in the restrictive group (0.068 vs. 0.076 Gy x cm2, P = 0.003). For patients staying ≥24 h, median number of CXRs per day was significantly reduced in the restrictive group (0.41 (IQR 0.21-0.61) vs. 0.55 (IQR 0.34-0.83), P < 0.001). Survival analysis proved non-inferiority. Secondary outcome parameters were not significantly different between the groups. CXR reduction was significant even for patients in most critical conditions. CONCLUSIONS: A substantial reduction of the number of CXRs on ICUs was feasible and safe using an indication catalogue thereby improving resource management. TRIAL REGISTRATION: DRKS00015621, German Clinical Trials Register.

Phantom study for comparison between computed tomography- and C-Arm computed tomography-guided puncture applied by residents in radiology. / Phantomstudie zum Vergleich zwischen Computertomografie- und C-Arm-Computertomografie-gesteuertem Punktionsverfahren bei Anwendung durch Weiterbildungsassistenten in der Radiologie.

PURPOSE: Comparison of puncture deviation and puncture duration between computed tomography (CT)- and C-arm CT (CACT)-guided puncture performed by residents in training (RiT). METHODS: In a cohort of 25 RiTs enrolled in a research training program either CT- or CACT-guided puncture was performed on a phantom. Prior to the experiments, the RiT's level of training, experience playing a musical instrument, video games, and ball sports, and self-assessed manual skills and spatial skills were recorded. Each RiT performed two punctures. The first puncture was performed with a transaxial or single angulated needle path and the second with a single or double angulated needle path. Puncture deviation and puncture duration were compared between the procedures and were correlated with the self-assessments. RESULTS: RiTs in both the CT guidance and CACT guidance groups did not differ with respect to radiologic experience (p = 1), angiographic experience (p = 0.415), and number of ultrasound-guided puncture procedures (p = 0.483), CT-guided puncture procedures (p = 0.934), and CACT-guided puncture procedures (p = 0.466). The puncture duration was significantly longer with CT guidance (without navigation tool) than with CACT guidance with navigation software (p < 0.001). There was no significant difference in the puncture duration between the first and second puncture using CT guidance (p = 0.719). However, in the case of CACT, the second puncture was significantly faster (p = 0.006). Puncture deviations were not different between CT-guided and CACT-guided puncture (p = 0.337) and between the first and second puncture of CT-guided and CACT-guided puncture (CT: p = 0.130; CACT: p = 0.391). The self-assessment of manual skills did not correlate with puncture deviation (p = 0.059) and puncture duration (p = 0.158). The self-assessed spatial skills correlated positively with puncture deviation (p = 0.011) but not with puncture duration (p = 0.541). CONCLUSION: The RiTs achieved a puncture deviation that was clinically adequate with respect to their level of training and did not differ between CT-guided and CACT-guided puncture. The puncture duration was shorter when using CACT. CACT guidance with navigation software support has a potentially steeper learning curve. Spatial skills might accelerate the learning of image-guided puncture. KEY POINTS: · The CT-guided and CACT-guided puncture experience of the RiTs selected as part of the program "Researchers for the Future" of the German Roentgen Society was adequate with respect to the level of training.. · Despite the lower collective experience of the RiTs with CACT-guided puncture with navigation software assistance, the learning curve regarding CACT-guided puncture may be faster compared to the CT-guided puncture technique.. · If the needle path is complex, CACT guidance with navigation software assistance might have an advantage over CT guidance.. CITATION FORMAT: · Meine TC, Hinrichs JB, Werncke T et al. Phantom study for comparison between computed tomography- and C-Arm computed tomography-guided puncture applied by residents in radiology. Fortschr Röntgenstr 2022; 194: 272 - 280.

Risk Stratification for ECMO Requirement in COVID-19 ICU Patients Using Quantitative Imaging Features in CT Scans on Admission.

(1) Background: Extracorporeal membrane oxygenation (ECMO) therapy in intensive care units (ICUs) remains the last treatment option for Coronavirus disease 2019 (COVID-19) patients with severely affected lungs but is highly resource demanding. Early risk stratification for the need of ECMO therapy upon admission to the hospital using artificial intelligence (AI)-based computed tomography (CT) assessment and clinical scores is beneficial for patient assessment and resource management; (2) Methods: Retrospective single-center study with 95 confirmed COVID-19 patients admitted to the participating ICUs. Patients requiring ECMO therapy (n = 14) during ICU stay versus patients without ECMO treatment (n = 81) were evaluated for discriminative clinical prediction parameters and AI-based CT imaging features and their diagnostic potential to predict ECMO therapy. Reported patient data include clinical scores, AI-based CT findings and patient outcomes; (3) Results: Patients subsequently allocated to ECMO therapy had significantly higher sequential organ failure (SOFA) scores (p < 0.001) and significantly lower oxygenation indices on admission (p = 0.009) than patients with standard ICU therapy. The median time from hospital admission to ECMO placement was 1.4 days (IQR 0.2-4.0). The percentage of lung involvement on AI-based CT assessment on admission to the hospital was significantly higher in ECMO patients (p < 0.001). In binary logistic regression analyses for ECMO prediction including age, sex, body mass index (BMI), SOFA score on admission, lactate on admission and percentage of lung involvement on admission CTs, only SOFA score (OR 1.32, 95% CI 1.08-1.62) and lung involvement (OR 1.06, 95% CI 1.01-1.11) were significantly associated with subsequent ECMO allocation. Receiver operating characteristic (ROC) curves showed an area under the curve (AUC) of 0.83 (95% CI 0.73-0.94) for lung involvement on admission CT and 0.82 (95% CI 0.72-0.91) for SOFA scores on ICU admission. A combined parameter of SOFA on ICU admission and lung involvement on admission CT yielded an AUC of 0.91 (0.84-0.97) with a sensitivity of 0.93 and a specificity of 0.84 for ECMO prediction; (4) Conclusions: AI-based assessment of lung involvement on CT scans on admission to the hospital and SOFA scoring, especially if combined, can be used as risk stratification tools for subsequent requirement for ECMO therapy in patients with severe COVID-19 disease to improve resource management in ICU settings.

Sequential Organ Failure Assessment Outperforms Quantitative Chest CT Imaging Parameters for Mortality Prediction in COVID-19 ARDS.

(1) Background: Respiratory insufficiency with acute respiratory distress syndrome (ARDS) and multi-organ dysfunction leads to high mortality in COVID-19 patients. In times of limited intensive care unit (ICU) resources, chest CTs became an important tool for the assessment of lung involvement and for patient triage despite uncertainties about the predictive diagnostic value. This study evaluated chest CT-based imaging parameters for their potential to predict in-hospital mortality compared to clinical scores. (2) Methods: 89 COVID-19 ICU ARDS patients requiring mechanical ventilation or continuous positive airway pressure mask ventilation were included in this single center retrospective study. AI-based lung injury assessment and measurements indicating pulmonary hypertension (PA-to-AA ratio) on admission CT, oxygenation indices, lung compliance and sequential organ failure assessment (SOFA) scores on ICU admission were assessed for their diagnostic performance to predict in-hospital mortality. (3) Results: CT severity scores and PA-to-AA ratios were not significantly associated with in-hospital mortality, whereas the SOFA score showed a significant association (p < 0.001). In ROC analysis, the SOFA score resulted in an area under the curve (AUC) for in-hospital mortality of 0.74 (95%-CI 0.63-0.85), whereas CT severity scores (0.53, 95%-CI 0.40-0.67) and PA-to-AA ratios (0.46, 95%-CI 0.34-0.58) did not yield sufficient AUCs. These results were consistent for the subgroup of more critically ill patients with moderate and severe ARDS on admission (oxygenation index <200, n = 53) with an AUC for SOFA score of 0.77 (95%-CI 0.64-0.89), compared to 0.55 (95%-CI 0.39-0.72) for CT severity scores and 0.51 (95%-CI 0.35-0.67) for PA-to-AA ratios. (4) Conclusions: Severe COVID-19 disease is not limited to lung (vessel) injury but leads to a multi-organ involvement. The findings of this study suggest that risk stratification should not solely be based on chest CT parameters but needs to include multi-organ failure assessment for COVID-19 ICU ARDS patients for optimized future patient management and resource allocation.

Cost-Effectiveness Analysis of 68Ga DOTA-TATE PET/CT, 111In-Pentetreotide SPECT/CT and CT for Diagnostic Workup of Neuroendocrine Tumors.

Neuroendocrine tumors (NETs) are relatively rare neoplasms arising from the hormone-producing neuroendocrine system that can occur in various organs such as pancreas, small bowel, stomach and lung. As the majority of these tumors express somatostatin receptors (SSR) on their cell membrane, utilization of SSR analogs in nuclear medicine is a promising, but relatively costly approach for detection and localization. The aim of this study was to analyze the cost-effectiveness of 68Ga-DOTA-TATE PET/CT (Gallium-68 DOTA-TATE Positron emission tomography/computed tomography) compared to 111In-pentetreotide SPECT/CT (Indium-111 pentetreotide Single Photon emission computed tomography/computed tomography) and to CT (computed tomography) alone in detection of NETs. A decision model on the basis of Markov simulations evaluated lifetime costs and quality-adjusted life years (QALYs) related to either a CT, SPECT/CT or PET/CT. Model input parameters were obtained from publicized research projects. The analysis is grounded on the US healthcare system. Deterministic sensitivity analysis of diagnostic parameters and probabilistic sensitivity analysis predicated on a Monte Carlo simulation with 30,000 reiterations was executed. The willingness-to-pay (WTP) was determined to be $ 100,000/QALY. In the base-case investigation, PET/CT ended up with total costs of $88,003.07 with an efficacy of 4.179, whereas CT ended up with total costs of $88,894.71 with an efficacy of 4.165. SPECT/CT ended up with total costs of $89,973.34 with an efficacy of 4.158. Therefore, the strategies CT and SPECT/CT were dominated by PET/CT in the base-case scenario. In the sensitivity analyses, PET/CT remained a cost-effective strategy. This result was due to reduced therapy costs of timely detection. The additional costs of 68Ga-DOTA-TATE PET/CT when compared to CT alone are justified in the light of potential savings in therapy costs and better outcomes.

The Cerebral Thrombin System Is Activated after Intracerebral Hemorrhage and Contributes to Secondary Lesion Growth and Poor Neurological Outcome in C57Bl/6 Mice.

With increasing evidence for the existence of a cerebral thrombin system, coagulation factor IIa (thrombin) is suspected to influence the pathogenesis of secondary injury progression after intracerebral hemorrhage (ICH). We hypothesized that mechanisms associated with local volume expansion after ICH, rather than blood constituents, activate the cerebral thrombin system and are responsible for detrimental neurological outcome. To test this hypothesis, we examine the local thrombin expression after ICH in a C57BL/6N mouse model in the presence and absence of blood constituents. ICH was established using stereotaxic orthotopic injection of utologous blood (n = 10) or silicone oil as inert volume substance (n = 10) into the striatum. Intracranial pressure (ICP), cerebral blood flow (CBF), and mean arterial blood pressure (MAP) were monitored during and 30 min after the procedure. No significant differences between ICP, CBF, and MAP were found between both groups. Prothrombin messenger RNA expression was upregulated early after ICH. Immunohistochemistry showed an increase of perilesional thrombin in both groups (blood, 4.24-fold; silicone, 3.10-fold), whereas prothrombin fragment (F1.2) was elevated only in the absence of whole blood. Thrombin expression is colocalized with neuronal antigen expression. After 24 h, lesion size and neuronal loss were similar. Perihematomal thrombin correlated with increased neuronal loss and detrimental neurological outcome in vivo. In our study, we demonstrate, for the first time, that the local cerebral thrombin system is activated after ICH and that this activation is independent of the presence of whole-blood constituents. In our study, neuronal damage is driven by local thrombin expression and leads to an adverse clinical outcome.

Follow-Up 18F-FDG PET/CT versus Contrast-Enhanced CT after Ablation of Liver Metastases of Colorectal Carcinoma-A Cost-Effectiveness Analysis.

PURPOSE: After a percutaneous ablation of colorectal liver metastases (CRLM), follow-up investigations to evaluate potential tumor recurrence are necessary. The aim of this study was to analyze whether a combined 18F-Fluordesoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) scan is cost-effective compared to a contrast-enhanced computed tomography (CE-CT) scan for detecting local tumor progression. MATERIALS AND METHODS: A decision model based on Markov simulations that estimated lifetime costs and quality-adjusted life years (QALYs) was developed. Model input parameters were obtained from the recent literature. Deterministic sensitivity analysis of diagnostic parameters based on a Monte-Carlo simulation with 30,000 iterations was performed. The willingness-to-pay (WTP) was set to $100,000/QALY. RESULTS: In the base-case scenario, CE-CT resulted in total costs of $28,625.08 and an efficacy of 0.755 QALYs, whereas 18F-FDG PET/CT resulted in total costs of $29,239.97 with an efficacy of 0.767. Therefore, the corresponding incremental cost-effectiveness ratio (ICER) of 18F-FDG PET/CT was $50,338.96 per QALY indicating cost-effectiveness based on the WTP threshold set above. The results were stable in deterministic and probabilistic sensitivity analyses. CONCLUSION: Based on our model, 18F-FDG PET/CT can be considered as a cost-effective imaging alternative for follow-up investigations after percutaneous ablation of colorectal liver metastases.

Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters.

Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist's evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

COVID-19 Pandemic and Upcoming Influenza Season-Does an Expert's Computed Tomography Assessment Differentially Identify COVID-19, Influenza and Pneumonias of Other Origin?

(1) Background: Time-consuming SARS-CoV-2 RT-PCR suffers from limited sensitivity in early infection stages whereas fast available chest CT can already raise COVID-19 suspicion. Nevertheless, radiologists' performance to differentiate COVID-19, especially from influenza pneumonia, is not sufficiently characterized. (2) Methods: A total of 201 pneumonia CTs were identified and divided into subgroups based on RT-PCR: 78 COVID-19 CTs, 65 influenza CTs and 62 Non-COVID-19-Non-influenza (NCNI) CTs. Three radiology experts (blinded from RT-PCR results) raised pathogen-specific suspicion (separately for COVID-19, influenza, bacterial pneumonia and fungal pneumonia) according to the following reading scores: 0-not typical/1-possible/2-highly suspected. Diagnostic performances were calculated with RT-PCR as a reference standard. Dependencies of radiologists' pathogen suspicion scores were characterized by Pearson's Chi2 Test for Independence. (3) Results: Depending on whether the intermediate reading score 1 was considered as positive or negative, radiologists correctly classified 83-85% (vs. NCNI)/79-82% (vs. influenza) of COVID-19 cases (sensitivity up to 94%). Contrarily, radiologists correctly classified only 52-56% (vs. NCNI)/50-60% (vs. COVID-19) of influenza cases. The COVID-19 scoring was more specific than the influenza scoring compared with suspected bacterial or fungal infection. (4) Conclusions: High-accuracy COVID-19 detection by CT might expedite patient management even during the upcoming influenza season.

Prognostic Value of Admission Chest CT Findings for Invasive Ventilation Therapy in COVID-19 Pneumonia.

(1) Background: To assess the value of chest CT imaging features of COVID-19 disease upon hospital admission for risk stratification of invasive ventilation (IV) versus no or non-invasive ventilation (non-IV) during hospital stay. (2) Methods: A retrospective single-center study was conducted including all patients admitted during the first three months of the pandemic at our hospital with PCR-confirmed COVID-19 disease and admission chest CT scans (n = 69). Using clinical information and CT imaging features, a 10-point ordinal risk score was developed and its diagnostic potential to differentiate a severe (IV-group) from a more moderate course (non-IV-group) of the disease was tested. (3) Results: Frequent imaging findings of COVID-19 pneumonia in both groups were ground glass opacities (91.3%), consolidations (53.6%) and crazy paving patterns (31.9%). Characteristics of later stages such as subpleural bands were observed significantly more often in the IV-group (52.2% versus 26.1%, p = 0.032). Using information directly accessible during a radiologist's reporting, a simple risk score proved to reliably differentiate between IV- and non-IV-groups (AUC: 0.89 (95% CI 0.81-0.96), p < 0.001). (4) Conclusions: Information accessible from admission CT scans can effectively and reliably be used in a scoring model to support risk stratification of COVID-19 patients to improve resource and allocation management of hospitals.

Advanced Fusion Imaging and Contrast-Enhanced Imaging (CT/MRI-CEUS) in Oncology.

Fusion imaging depicts an innovative technique that facilitates combining assets and reducing restrictions of advanced ultrasound and cross-sectional imaging. The purpose of the present retrospective study was to evaluate the role of fusion imaging for assessing hepatic and renal lesions. Between 02/2011-08/2020, 92 patients in total were included in the study, of which 32 patients had hepatic lesions, 60 patients had renal lesions. Fusion imaging was technically successful in all patients. No adverse side effects upon intravenous (i.v.) application of SonoVue® (Bracco, Milan, Italy) were registered. Fusion imaging could clarify all 11 (100%) initially as indeterminate described hepatic lesions by computed tomography/magnetic resonance imaging (CT/MRI). Moreover, 5/14 (36%) initially suspicious hepatic lesions could be validated by fusion imaging, whereas in 8/14 (57%), malignant morphology was disproved. Moreover, fusion imaging allowed for the clarification of 29/30 (97%) renal lesions initially characterized as suspicious by CT/MRI, of which 19/30 (63%) underwent renal surgery, histopathology revealed malignancy in 16/19 (84%), and benignity in 3/19 (16%). Indeterminate findings could be elucidated by fusion imaging in 20/20 (100%) renal lesions. Its accessibility and repeatability, even during pregnancy and in childhood, its cost-effectiveness, and its excellent safety profile, make fusion imaging a promising instrument for the thorough evaluation of hepatic and renal lesions in the future.

Systematic use of cardiac magnetic resonance imaging in MINOCA led to a five-fold increase in the detection rate of myocarditis: a retrospective study.

BACKGROUND: Systematic work-up of patients with myocardial infarction and non-obstructive coronary artery disease (MINOCA) using cardiac magnetic resonance imaging (CMR) led to a more than six-fold increase in the detection rate of myocarditis. In this study, we expanded on our prior two-year analysis by including preceding and subsequent years. METHODS: We performed a retrospective chart review of patients with angina-like symptoms and elevated high-sensitivity troponin T (TnT-hs ≥14 ng/l) but without significant coronary artery disease, from 2011 to 2017. Patients underwent CMR to test for myocarditis. From 2011 to 2015, only patients with elevated TnT-hs, no significant coronary artery disease and moderate to high clinical likelihood of suffering from myocarditis, underwent CMR. In 2016 and 2017, CMR images were obtained from all patients with MINOCA, independent of the clinical likelihood that patients were suffering from myocarditis. RESULTS: A total of 556 patients who underwent CMR (70.5% male, 57 ± 17 years, with an average left ventricular ejection fraction of 51 ± 15%) qualified for inclusion in this study’s analysis. From 2011 to 2015, 240 CMR examinations were performed, with the number increasing to 316 between 2016 and 2017. In total, myocarditis was diagnosed in 76 out of the 556 patients (13.7%). Between 2011 and 2015, the detection rate of myocarditis was 12.7 per 100,000 hospitalisations and increased 4.9-fold (p <0.0001), to 62.5 per 100,000 hospitalisations, between 2016 and 2017. CONCLUSION: A novel diagnostic algorithm led to an average 4.9-fold increase in the rate of myocarditis detection in our hospital over the two subsequent years. This highlights that myocarditis continues to be underdiagnosed when CMR is not systematically used in patients with MINOCA.  .