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Objectives: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies.Design: Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients.Patients: One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between.Measurements and Main Results: Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78-0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist's maximum sensitivity up to 0.87 (95% CI, 0.76-0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659-0.815) versus radiologist's area under the receiver-operating characteristic curve of 0.779 (0.723-0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist's and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662-0.817) versus radiologist's area under the receiver-operating characteristic curve of 0.698 (0.646-0.749) with similar diagnostic metrics for receiver-operating characteristic operating points.Conclusions: Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence-assisted reading setting. [ABSTRACT FROM AUTHOR]

Purpose: Imaging is crucial for management of patients with possible cerebral venous thrombosis (CVT). To evaluate the cost-effectiveness of different noninvasive imaging strategies in patients with possible CVT. Methods: A decision model based on Markov simulations estimated lifetime costs and quality-adjusted life years (QALY) associated with the following imaging strategies: non-contrast CT (NCCT), NCCT plus CT venography (CTV), routine MRI without vascular imaging (R-MRI), and MRI with venography (MRV). The analysis was performed from a US healthcare perspective. Model input was based on best available and most recent evidence, including outcome data from the International Study on Cerebral Vein and Dural Sinus Thrombosis (ISCVT). Starting age was 37 years; both high and low pre-test probabilities of CVT were evaluated. Probabilistic sensitivity analyses (PSA) estimated model uncertainty. Results: In the base-case analysis, NCCT and CTV were dominant over R-MRI and MRV. CTV led to incremental lifetime QALYs compared with NCCT (23.385 QALYs vs. 23.374 QALYs) at slightly higher lifetime costs ($5210 vs. $5057). In PSA, CTV was the strategy with the highest percentage of cost-effective iterations if willingness-to-pay (WTP) thresholds were higher than $13,750/QALY. Complying with contemporary WTP thresholds, CTV was thus identified as the most cost-effective strategy. When the pre-test probability was set to 50%, CTV was also preferred. Conclusion: In patients at the peak age of CVT incidence yet low clinical pre-test probability, diagnostic imaging with CTV is the most cost-effective strategy. [ABSTRACT FROM AUTHOR]

Purpose: The proPSMA trial at ten Australian centers demonstrated increased sensitivity and specificity for PSMA PET/CT compared to conventional imaging regarding metastatic status in primary high-risk prostate cancer patients. A cost-effectiveness analysis showed benefits of PSMA PET/CT over conventional imaging for the Australian setting. However, comparable data for other countries are lacking. Therefore, we aimed to verify the cost-effectiveness of PSMA PET/CT in several European countries as well as the USA. Methods: Clinical data on diagnostic accuracy were derived from the proPSMA trial. Costs for PSMA PET/CT and conventional imaging were taken from reimbursements of national health systems and individual billing information of selected centers in Belgium, Germany, Italy, the Netherlands, and the USA. For comparability, scan duration and the decision tree of the analysis were adopted from the Australian cost-effectiveness study. Results: In contrast to the Australian setting, PSMA PET/CT was primarily associated with increased costs in the studied centers in Europe and the USA. Mainly, the scan duration had an impact on the cost-effectiveness. However, costs for an accurate diagnosis using PSMA PET/CT seemed reasonably low compared to the potential consequential costs of an inaccurate diagnosis. Conclusion: We assume that the use of PSMA PET/CT is appropriate from a health economic perspective, but this will need to be verified by a prospective evaluation of patients at initial diagnosis. [ABSTRACT FROM AUTHOR]

Background: Somatostatin-receptor (SSTR)-targeted PET/CT provides important clinical information in addition to standard imaging in meningioma patients. [18F]SiTATE is a novel, 18F-labeled SSTR-targeting peptide with superior imaging properties according to preliminary data. We provide the first [18F]SiTATE PET/CT data of a large cohort of meningioma patients. Methods: Patients with known or suspected meningioma undergoing [18F]SiTATE PET/CT were included. Uptake intensity (SUV) of meningiomas, non-meningioma lesions, and healthy organs were assessed using a 50% isocontour volume of interest (VOI) or a spherical VOI, respectively. Also, trans-osseous extension on PET/CT was assessed. Results: A total of 107 patients with 117 [18F]SiTATE PET/CT scans were included. Overall, 231 meningioma lesions and 61 non-meningioma lesions (e.g., post-therapeutic changes) were analyzed. Physiological uptake was lowest in healthy brain tissue, followed by bone marrow, parotid, and pituitary (SUVmean 0.06 ± 0.04 vs. 1.4 ± 0.9 vs. 1.6 ± 1.0 vs. 9.8 ± 4.6; p < 0.001). Meningiomas showed significantly higher uptake than non-meningioma lesions (SUVmax 11.6 ± 10.6 vs. 4.0 ± 3.3, p < 0.001). Meningiomas showed significantly higher uptake than non-meningioma lesions (SUVmax 11.6±10.6 vs. 4.0±3.3, p<0.001). 93/231 (40.3%) meningiomas showed partial trans-osseous extension and 34/231 (14.7%) predominant intra-osseous extension. 59/231 (25.6%) meningioma lesions found on PET/CT had not been reported on previous standard imaging. Conclusion: This is the first PET/CT study using an 18F-labeled SSTR-ligand in meningioma patients: [18F]SiTATE provides extraordinary contrast in meningioma compared to healthy tissue and non-meningioma lesions, which leads to a high detection rate of so far unknown meningioma sites and osseous involvement. Having in mind the advantageous logistic features of 18F-labeled compared to 68Ga-labeled compounds (e.g., longer half-life and large-badge production), [18F]SiTATE has the potential to foster a widespread use of SSTR-targeted imaging in neuro-oncology. [ABSTRACT FROM AUTHOR]

Purpose: The advent of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of advanced NSCLC, leading to a string of approvals in recent years. Herein, a narrative review on the role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in the ever-evolving treatment landscape of advanced NSCLC is presented. Methods: This comprehensive review will begin with an introduction into current treatment paradigms incorporating ICIs; the evolution of CT-based criteria; moving onto novel phenomena observed with ICIs and the current state of hybrid imaging for diagnosis, treatment planning, evaluation of treatment efficacy and toxicity in advanced NSCLC, also taking into consideration its limitations and future directions. Conclusions: The advent of ICIs marks the dawn of a new era bringing forth new challenges particularly vis-à-vis treatment response assessment and observation of novel phenomena accompanied by novel systemic side effects. While FDG PET/CT is widely adopted for tumor volume delineation in locally advanced disease, response assessment to immunotherapy based on current criteria is of high clinical value but has its inherent limitations. In recent years, modifications of established (PET)/CT criteria have been proposed to provide more refined approaches towards response evaluation. Not only a comprehensive inclusion of PET-based response criteria in prospective randomized controlled trials, but also a general harmonization within the variety of PET-based response criteria is pertinent to strengthen clinical implementation and widespread use of hybrid imaging for response assessment in NSCLC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Correction to: Next-generation PET/CT imaging in meningioma - first clinical experiences using the novel SSTR-targeting peptide [18F]SiTATE Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The original article can be found online at https://doi.org/10.1007/s00259-023-06315-z. [Extracted from the article]

Objectives: To assess the prognostic value of pre-therapeutic computed tomography (CT) attenuation of liver metastases for overall survival (OS) in metastatic colorectal cancer (mCRC).Methods: In the open-label, randomised, prospective phase-III FIRE-3 trial, patients with histologically confirmed mCRC received fluorouracil (5-FU), leucovorin and irinotecan (FOLFIRI) with either cetuximab or bevacizumab. Participating patients gave written informed consent prior to study entry. In CT at baseline (portal venous phase, slice thickness ≤5 mm), mean attenuation [Hounsfield units (HU)] of liver metastases was retrospectively assessed by semi-automated volumetry. Its prognostic influence on OS was analysed in Kaplan-Meier-analysis and Cox proportional hazard regression and an optimal threshold was determined.Results: In FIRE-3, 592 patients were enrolled between 2007 and 2012. Among the 347 patients eligible for liver volumetry, median baseline CT attenuation of liver metastases was 59.67 HU [interquartile range (IQR), 49.13, 68.85]. Increased attenuation was associated with longer OS {per 10 HU: hazard ratio (HR), 0.85 [95% confidence interval (CI), 0.78, 0.93], p < 0.001}. The optimised threshold (≥61.62 HU) was a strong predictor for increased OS [median, 21.3 vs 30.6 months; HR, 0.61 (95% CI, 0.47, 0.80), p < 0.001]. Multivariate regression controlling for correlated and further prognostic factors confirmed this [HR, 0.60 (95% CI, 0.45, 0.81), p = 0.001]. Furthermore, mean attenuation ≥61.62 HU was significantly associated with increased early tumour shrinkage (p = 0.002) and increased depth of response (p = 0.012).Conclusions: Increased mean baseline CT attenuation of liver metastases may identify mCRC patients with prolonged OS and better tumour response.Key Points: • In colorectal cancer, increased attenuation of liver metastases in baseline computed tomography is a prognostic factor for prolonged OS (p < 0.001). • A threshold of ≥61.62 HU was determined as optimal cut-off to identify patients with prolonged OS (p < 0.001), early tumour shrinkage (p = 0.002) and increased depth of response (p = 0.012). [ABSTRACT FROM AUTHOR]

(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. [ABSTRACT FROM AUTHOR]

Simple Summary: Finding prognostic biomarkers and associated models with high accuracy in patients with pancreatic cancer remains a challenge. The aim of this study was to analyze whether the combination of quantitative imaging biomarkers based on geometric and radiomics analysis of whole liver tumor burden and established clinical parameters improves the prediction of survival in patients with metastatic pancreatic cancer. In this retrospective study a total of 75 patients with pancreatic cancer and liver metastases were analyzed. Segmentations of whole liver tumor burden from baseline contrast-enhanced CT images were used to derive different quantitative imaging biomarkers. For comparison, we chose two clinical prognostic models from the literature. We found that a combined clinical and imaging-based model has a significantly higher predictive performance to discriminate survival than the underlying clinical models alone (p < 0.003). Finding prognostic biomarkers with high accuracy in patients with pancreatic cancer (PC) remains a challenging problem. To improve the prediction of survival and to investigate the relevance of quantitative imaging biomarkers (QIB) we combined QIB with established clinical parameters. In this retrospective study a total of 75 patients with metastatic PC and liver metastases were analyzed. Segmentations of whole liver tumor burden (WLTB) from baseline contrast-enhanced CT images were used to derive QIBs. The benefits of QIBs in multivariable Cox models were analyzed in comparison with two clinical prognostic models from the literature. To discriminate survival, the two clinical models had concordance indices of 0.61 and 0.62 in a statistical setting. Combined clinical and imaging-based models achieved concordance indices of 0.74 and 0.70 with WLTB volume, tumor burden score (TBS), and bilobar disease being the three WLTB parameters that were kept by backward elimination. These combined clinical and imaging-based models have significantly higher predictive performance in discriminating survival than the underlying clinical models alone (p < 0.003). Radiomics and geometric WLTB analysis of patients with metastatic PC with liver metastases enhances the modeling of survival compared with models based on clinical parameters alone. [ABSTRACT FROM AUTHOR]

Background: Neurologic symptom severity and deterioration at 24 hours (h) predict long-term outcomes in patients with acute large vessel occlusion (LVO) stroke of the anterior circulation. We aimed to examine the association of baseline multiparametric CT imaging and clinical factors with the course of neurologic symptom severity in the first 24 h after endovascular treatment (EVT). Methods: Patients with LVO stroke of the anterior circulation were selected from a prospectively acquired consecutive cohort of patients who underwent multiparametric CT, including non-contrast CT, CT angiography and CT perfusion before EVT. The symptom severity was assessed on admission and after 24 h using the 42-point National Institutes of Health Stroke Scale (NIHSS). Clinical and imaging data were compared between patients with and without early neurological deterioration (END). END was defined as an increase in ≥4 points, and a significant clinical improvement as a decrease in ≥4 points, compared to NIHSS on admission. Multivariate regression analyses were used to determine independent associations of imaging and clinical parameters with NIHSS score increase or decrease in the first 24 h. Results: A total of 211 patients were included, of whom 38 (18.0%) had an END. END was significantly associated with occlusion of the internal carotid artery (odds ratio (OR), 4.25; 95% CI, 1.90–9.47) and the carotid T (OR, 6.34; 95% CI, 2.56–15.71), clot burden score (OR, 0.79; 95% CI, 0.68–0.92) and total ischemic volume (OR, 1.01; 95% CI, 1.00–1.01). In a comprehensive multivariate analysis model including periprocedural parameters and complications after EVT, carotid T occlusion remained independently associated with END, next to reperfusion status and intracranial hemorrhage. Favorable reperfusion status and small ischemic core volume were associated with clinical improvement after 24 h. Conclusions: The use of imaging parameters as a surrogate for early NIHSS progression in an acute LVO stroke after EVT reached limited performance with only carotid T occlusion as an independent predictor of END. Reperfusion status and early complications in terms of intracranial hemorrhage are critical factors that influence patient outcome in the acute stroke phase after EVT. [ABSTRACT FROM AUTHOR]

(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. [ABSTRACT FROM AUTHOR]

The sarcoid-like reaction represents an autoinflammatory cause of mediastinal and hilar lymphadenopathy but may also involve other lymph node regions and organs. This rare phenomenon has mainly been reported in patients with Hodgkin's lymphoma (HL) or solid tumors (particularly melanoma) undergoing immunotherapy and chemotherapy. Cases in non-Hodgkin's lymphoma (NHL) are very uncommon. We present an uncommon case of a patient with primarily mediastinal diffuse large B-cell lymphoma (DLBCL) who showed a CT-based partial response in interim staging, whereas at end-of-treatment multiple newly enlarged and hypermetabolic mediastinal and bilateral hilar lymph nodes were detected by 18F-FDG PET/CT imaging. A subsequent histological workup determined a sarcoid-like reaction without any lymphomatous tissue. Therefore, sarcoid-like reactions should be considered as a potential pitfall in Deauville staging with 18F-FDG PET/CT imaging for patients with NHL. [ABSTRACT FROM AUTHOR]

(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. [ABSTRACT FROM AUTHOR]

(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. [ABSTRACT FROM AUTHOR]