Comparative accuracy and cost-effectiveness of dynamic contrast-enhanced CT and positron emission tomography in the characterisation of solitary pulmonary nodules.

INTRODUCTION: Dynamic contrast-enhanced CT (DCE-CT) and positron emission tomography/CT (PET/CT) have a high reported accuracy for the diagnosis of malignancy in solitary pulmonary nodules (SPNs). The aim of this study was to compare the accuracy and cost-effectiveness of these. METHODS: In this prospective multicentre trial, 380 participants with an SPN (8-30 mm) and no recent history of malignancy underwent DCE-CT and PET/CT. All patients underwent either biopsy with histological diagnosis or completed CT follow-up. Primary outcome measures were sensitivity, specificity and overall diagnostic accuracy for PET/CT and DCE-CT. Costs and cost-effectiveness were estimated from a healthcare provider perspective using a decision-model. RESULTS: 312 participants (47% female, 68.1±9.0 years) completed the study, with 61% rate of malignancy at 2 years. The sensitivity, specificity, positive predictive value and negative predictive values for DCE-CT were 95.3% (95% CI 91.3 to 97.5), 29.8% (95% CI 22.3 to 38.4), 68.2% (95% CI 62.4% to 73.5%) and 80.0% (95% CI 66.2 to 89.1), respectively, and for PET/CT were 79.1% (95% CI 72.7 to 84.2), 81.8% (95% CI 74.0 to 87.7), 87.3% (95% CI 81.5 to 91.5) and 71.2% (95% CI 63.2 to 78.1). The area under the receiver operator characteristic curve (AUROC) for DCE-CT and PET/CT was 0.62 (95% CI 0.58 to 0.67) and 0.80 (95% CI 0.76 to 0.85), respectively (p<0.001). Combined results significantly increased diagnostic accuracy over PET/CT alone (AUROC=0.90 (95% CI 0.86 to 0.93), p<0.001). DCE-CT was preferred when the willingness to pay per incremental cost per correctly treated malignancy was below £9000. Above £15 500 a combined approach was preferred. CONCLUSIONS: PET/CT has a superior diagnostic accuracy to DCE-CT for the diagnosis of SPNs. Combining both techniques improves the diagnostic accuracy over either test alone and could be cost-effective. TRIAL REGISTRATION NUMBER: NCT02013063.

Uterine Artery Embolization of Uterine Leiomyomas: Predictive MRI Features of Volumetric Response.

OBJECTIVE. The purpose of this article was to evaluate MRI features of uterine leiomyomas that predict volumetric response after uterine artery embolization (UAE). MATERIALS AND METHODS. This retrospective study included 75 patients with 212 uterine leiomyomas who were successfully treated between August 2013 and December 2018. To predict uterine volumetric response, age, number of lesions, and baseline uterine volume were assessed. To predict leiomyoma volumetric response, a multivariate regression analysis was performed to evaluate six predictive factors: location, baseline leiomyoma volume, signal intensity on T1-weighted and T2-weighted MRI, heterogeneity of signal intensity on T2-weighted MRI, and vascularity on subtraction imaging (SI). A five-variable predictive ROC model was developed to evaluate the diagnostic accuracy of the signal intensity ratio on T2-weighted MRI, enhancement ratio, heterogeneity ratio on T2-weighted MRI, location, and baseline leiomyoma volume in predicting at least 40% leiomyoma volumetric response. RESULTS. Age, number of leiomyomas, and baseline uterine volume were not predictive of uterine volumetric response. A submucosal location was the best predictive factor of leiomyoma volumetric response, and it showed 32.2% more leiomyoma volumetric response compared with a nonsubmucosal location (p < .001). Hyperintensity on T2-weighted MRI was the second best predictive factor of leiomyoma volumetric response, and it showed 16.9% more volumetric response compared with hypointense leiomyomas (p = .013). A small baseline leiomyoma volume (< 58 cm3) was associated with 10.2% more leiomyoma volumetric response compared with larger leiomyomas (p = .01). Leiomyomas that were hyperintense on SI showed 7.9% more leiomyoma volumetric response compared with those that were hypointense (p = .014). The five-variable ROC model showed high diagnostic accuracy with an AUC of 0.85, sensitivity of 82%, and specificity of 71%. CONCLUSION. A submucosal location, hyperintensity on T2-weighted MRI, small baseline leiomyoma volume (< 58 cm3), and hyperintense leiomyoma on subtraction imaging are the main independent favorable predictors of leiomyoma volumetric response after UAE. An accurate predictive ROC model was developed that may help in selecting patients suitable for UAE. Quantitative assessment of heterogeneity on T2-weighted MRI showed promising results as a predictor of volumetric response, and further research in this area using texture analysis and radiomics is suggested.

Predicting Severe COVID-19 Infection on Initial Diagnosis: Comparison and Validation of CT Imaging Triage Tools.

BACKGROUND: Developing a reliable predictive tool of disease severity in COVID-19 infection is important to help triage patients and ensure the appropriate utilization of health-care resources. OBJECTIVE: To develop, validate, and compare three CT scoring systems (CTSS) to predict severe disease on initial diagnosis of COVID-19 infection. METHODS: One hundred and twenty and 80 symptomatic adults with confirmed COVID-19 infection who presented to emergency department were evaluated retrospectively in the primary and validation groups, respectively. All patients had non-contrast CT chest within 48 hours of admission. Three lobarbased CTSS were assessed and compared. The simple lobar system was based on the extent of pulmonary infiltration. Attenuation corrected lobar system (ACL) assigned further weighting factor based on attenuation of pulmonary infiltrates. Attenuation and volume-corrected lobar system incorporated further weighting factor based on proportional lobar volume. The total CT severity score (TSS) was calculated by adding individual lobar scores. The disease severity assessment was based on Chinese National Health Commission guidelines. Disease severity discrimination was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS: The ACL CTSS demonstrated the best predictive and consistent accuracy of disease severity with an AUC of 0.93(95%CI:0.88-0.97) in the primary cohort and 0.97 (95%CI:0.91.5-1) in the validation group. Applying a TSS cut-off value of 9.25, the sensitivities were 96.4% and 100% and the specificities were 75% and 91% in the primary and validation groups, respectively. CONCLUSION: The ACL CTSS showed the highest accuracy and consistency in predicting severe disease on initial diagnosis of COVID-19. This scoring system may provide frontline physicians with a triage tool to guide admission, discharge, and early detection of severe illness.

Optimized serum ferritin prediction of iron overload in transfusion-dependent thalassemia: likelihood ratio and age-adjustment approach.

BACKGROUND: Early detection of iron overload in transfusion-dependent thalassemia (TDT) patients is critical to prevent complications and improve survival. OBJECTIVES: Evaluate the utility of serum ferritin (SF) in the prediction of hepatic and myocardial iron overload (HIO and MIO) compared to T2*-MRI. DESIGN: Retrospective SETTINGS: Governmental hospitals. PATIENTS AND METHODS: Patients with TDT who had T2*-MRI examinations between January 2016 to October 2019 were included. The predictive value of SF for detection of HIO and MIO was assessed by measuring area under the curve (AUC). A sample size of 123 cases was calculated to detect a correlation of 0.25 with 90% power and a two-sided type I error of 0.05. MAIN OUTCOME MEASURES: The correlation between SF and estimated hepatic iron concentration. SAMPLE SIZE: 137 TDT patients who required regular blood transfusions. RESULTS: The predictive value of SF was excellent for detection of HIO (AUC=0.83-0.87) but fair for detection of MIO (AUC=0.67). The two independent predictors of MIO were age and SF. The log of (age × SF) enhanced the SF predictive value for MIO (AUC=0.78). SF values of 700 and 1250 mg/L effectively excluded mild and moderate HIO with a sensitivity of 97.8% and 94.2%, respectively (LR-=0.1). While SF values of 1640 and 2150 mg/L accurately diagnosed mild and moderate HIO with a specificity of 95.55% and 96.4%, respectively (LR+>10). A log of (age × SF) cut-off value of 4.15 effectively excluded MIO (LR-=0.1), while a value of 4.65 moderately confirmed MIO (LR+=3.2). CONCLUSIONS: SF is an excellent predictor of hepatic IO in TDT. Age adjustment enhanced its myocardial IO predictive accuracy. Likelihood ratio-based SF cut-off values may help clinicians in risk stratification and treatment decision-making. LIMITATIONS: The laboratory data were gathered retrospectively and although the risk of selection bias for T2*-MRI examination is thought to be low, it cannot be ignored. CONFLICT OF INTEREST: None.

Predicting Severe Disease and Critical Illness on Initial Diagnosis of COVID-19: Simple Triage Tools.

RATIONALE: This study was conducted to develop, validate, and compare prediction models for severe disease and critical illness among symptomatic patients with confirmed COVID-19. METHODS: For development cohort, 433 symptomatic patients diagnosed with COVID-19 between April 15th 2020 and June 30th, 2020 presented to Tawam Public Hospital, Abu Dhabi, United Arab Emirates were included in this study. Our cohort included both severe and non-severe patients as all cases were admitted for purpose of isolation as per hospital policy. We examined 19 potential predictors of severe disease and critical illness that were recorded at the time of initial assessment. Univariate and multivariate logistic regression analyses were used to construct predictive models. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration and goodness of fit of the models were assessed. A cohort of 213 patients assessed at another public hospital in the country during the same period was used to validate the models. RESULTS: One hundred and eighty-six patients were classified as severe while the remaining 247 were categorized as non-severe. For prediction of progression to severe disease, the three independent predictive factors were age, serum lactate dehydrogenase (LDH) and serum albumin (ALA model). For progression to critical illness, the four independent predictive factors were age, serum LDH, kidney function (eGFR), and serum albumin (ALKA model). The AUC for the ALA and ALKA models were 0.88 (95% CI, 0.86-0.89) and 0.85 (95% CI, 0.83-0.86), respectively. Calibration of the two models showed good fit and the validation cohort showed excellent discrimination, with an AUC of 0.91 (95% CI, 0.83-0.99) for the ALA model and 0.89 (95% CI, 0.80-0.99) for the ALKA model. A free web-based risk calculator was developed. CONCLUSIONS: The ALA and ALKA predictive models were developed and validated based on simple, readily available clinical and laboratory tests assessed at presentation. These models may help frontline clinicians to triage patients for admission or discharge, as well as for early identification of patients at risk of developing critical illness.

Dynamic contrast-enhanced CT compared with positron emission tomography CT to characterise solitary pulmonary nodules: the SPUtNIk diagnostic accuracy study and economic modelling.

BACKGROUND: Current pathways recommend positron emission tomography-computerised tomography for the characterisation of solitary pulmonary nodules. Dynamic contrast-enhanced computerised tomography may be a more cost-effective approach. OBJECTIVES: To determine the diagnostic performances of dynamic contrast-enhanced computerised tomography and positron emission tomography-computerised tomography in the NHS for solitary pulmonary nodules. Systematic reviews and a health economic evaluation contributed to the decision-analytic modelling to assess the likely costs and health outcomes resulting from incorporation of dynamic contrast-enhanced computerised tomography into management strategies. DESIGN: Multicentre comparative accuracy trial. SETTING: Secondary or tertiary outpatient settings at 16 hospitals in the UK. PARTICIPANTS: Participants with solitary pulmonary nodules of ≥ 8 mm and of ≤ 30 mm in size with no malignancy in the previous 2 years were included. INTERVENTIONS: Baseline positron emission tomography-computerised tomography and dynamic contrast-enhanced computer tomography with 2 years' follow-up. MAIN OUTCOME MEASURES: Primary outcome measures were sensitivity, specificity and diagnostic accuracy for positron emission tomography-computerised tomography and dynamic contrast-enhanced computerised tomography. Incremental cost-effectiveness ratios compared management strategies that used dynamic contrast-enhanced computerised tomography with management strategies that did not use dynamic contrast-enhanced computerised tomography. RESULTS: A total of 380 patients were recruited (median age 69 years). Of 312 patients with matched dynamic contrast-enhanced computer tomography and positron emission tomography-computerised tomography examinations, 191 (61%) were cancer patients. The sensitivity, specificity and diagnostic accuracy for positron emission tomography-computerised tomography and dynamic contrast-enhanced computer tomography were 72.8% (95% confidence interval 66.1% to 78.6%), 81.8% (95% confidence interval 74.0% to 87.7%), 76.3% (95% confidence interval 71.3% to 80.7%) and 95.3% (95% confidence interval 91.3% to 97.5%), 29.8% (95% confidence interval 22.3% to 38.4%) and 69.9% (95% confidence interval 64.6% to 74.7%), respectively. Exploratory modelling showed that maximum standardised uptake values had the best diagnostic accuracy, with an area under the curve of 0.87, which increased to 0.90 if combined with dynamic contrast-enhanced computerised tomography peak enhancement. The economic analysis showed that, over 24 months, dynamic contrast-enhanced computerised tomography was less costly (£3305, 95% confidence interval £2952 to £3746) than positron emission tomography-computerised tomography (£4013, 95% confidence interval £3673 to £4498) or a strategy combining the two tests (£4058, 95% confidence interval £3702 to £4547). Positron emission tomography-computerised tomography led to more patients with malignant nodules being correctly managed, 0.44 on average (95% confidence interval 0.39 to 0.49), compared with 0.40 (95% confidence interval 0.35 to 0.45); using both tests further increased this (0.47, 95% confidence interval 0.42 to 0.51). LIMITATIONS: The high prevalence of malignancy in nodules observed in this trial, compared with that observed in nodules identified within screening programmes, limits the generalisation of the current results to nodules identified by screening. CONCLUSIONS: Findings from this research indicate that positron emission tomography-computerised tomography is more accurate than dynamic contrast-enhanced computerised tomography for the characterisation of solitary pulmonary nodules. A combination of maximum standardised uptake value and peak enhancement had the highest accuracy with a small increase in costs. Findings from this research also indicate that a combined positron emission tomography-dynamic contrast-enhanced computerised tomography approach with a slightly higher willingness to pay to avoid missing small cancers or to avoid a 'watch and wait' policy may be an approach to consider. FUTURE WORK: Integration of the dynamic contrast-enhanced component into the positron emission tomography-computerised tomography examination and the feasibility of dynamic contrast-enhanced computerised tomography at lung screening for the characterisation of solitary pulmonary nodules should be explored, together with a lower radiation dose protocol. STUDY REGISTRATION: This study is registered as PROSPERO CRD42018112215 and CRD42019124299, and the trial is registered as ISRCTN30784948 and ClinicalTrials.gov NCT02013063. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 17. See the NIHR Journals Library website for further project information.

A nodule found on a lung scan can cause concern as it may be a sign of cancer. Finding lung cancer nodules when they are small (i.e. < 3 cm) is very important. Most nodules are not cancerous. Computerised tomography (cross-sectional images created from multiple X-rays) and positron emission tomography­computerised tomography (a technique that uses a radioactive tracer combined with computerised tomography) are used to see whether or not a nodule is cancerous; although they perform well, improvements are required. This study compared dynamic contrast-enhanced computerised tomography with positron emission tomography­computerised tomography scans to find out which test is best. Dynamic contrast-enhanced computerised tomography involves injection of a special dye into the bloodstream, followed by repeated scans of the nodule over several minutes. We assessed the costs to the NHS of undertaking the different scans, relative to their benefits, to judge which option was the best value for money. We recruited 380 patients from 16 hospitals across England and Scotland, of whom 312 had both dynamic contrast-enhanced computerised tomography and positron emission tomography­computerised tomography scans. We found that current positron emission tomography­computerised tomography is more accurate, providing a correct diagnosis in 76% of cases, than the new dynamic contrast-enhanced computerised tomography, which provides a correct diagnosis in 70% of cases. Although dynamic contrast-enhanced computerised tomography cannot replace positron emission tomography­computerised tomography, it may represent good-value use of NHS resources, especially if it is performed before positron emission tomography­computerised tomography and they are used in combination. Although more research is required, it may be possible in the future to perform dynamic contrast-enhanced computerised tomography at the same time as positron emission tomography­computerised tomography in patients with suspected lung cancer or if a lung nodule is found on a lung screening programme at the time of the computerised tomography examination. This may reduce the need for some people to have positron emission tomography­computerised tomography.

Pathological nature of renal tumors - does size matter?

INTRODUCTION: We examined the relationship between the size and nature of renal masses in term of malignant potential, histological grading, pathological staging and presence of necrosis and sarcomatoid changes. MATERIALS AND METHODS: Retrospectively, we reviewed 323 consecutive nephrectomies between 2000 and 2010. Final pathology was correlated with tumour size. The renal tumours were stratified into three groups according to the largest diameter, defined as 4 cm or smaller, greater than 4 cm to 7 cm, and greater than 7 cm. We recorded the proportion of benign tumours, tumour grade and stage, presence of necrosis and sarcomatoid change. RESULTS: Small renal masses ≤4 cm (SRMs) were more likely to be localised to the kidney (90%) and of lower histological grade (75%). The proportion of benign tumours in SRMs (15%) was higher than other two groups with the majority of benign tumours being oncocytomas. There was a statistically significant trend with greater necrosis and sarcomatoid change for the large size group. CONCLUSIONS: SRMs are likely to be low grade and organ confined with little or no adverse pathological features. There is increased likelihood of benignity in SRTs with the majority of benign tumours being oncocytomas.

An unusual cause of a mediastinal mass.

Our case report describes an unusual cause of a mediastinal mass. The patient is a current smoker with a background of neurofibromatosis (NF) type 1 who presented with a right apical mass. Initial investigations suggested a probable malignant cause. The final diagnosis was one of a haematoma from a ruptured thyrocervical aneurysm. The association between neurofibromatosis and vascular aneurysms is an often unrecognised but documented phenomenon. We would like to raise an awareness of this infrequent presentation, as it is associated with a high mortality and may be prevented by early diagnosis.

Isolated tuberculosis of the liver: a diagnostic challenge.

A 46-year-old gentleman, being investigated for symptoms of generalised weakness, low-grade fever and weight loss, was found to have a large, infiltrative mass of the liver on CT scan. The radiological impression was that of advanced hepatic malignancy with involvement of lesser curve of the stomach and regional lymph nodes. Multiple biopsy attempts failed to yield an adequate tissue sample for histopathological diagnosis. Surgery was planned for left hemihepatectomy with resection of the hepatogastric ligament and partial gastrectomy. Frozen section of a peroperative tissue sample confirmed the diagnosis of hepatic tuberculosis (TB). The granulomatous area was debrided and anti-TB treatment was started postoperatively. Recovery was unremarkable and the patient is currently asymptomatic.