Towards clinical implementation of an AI-algorithm for detection of cervical spine fractures on computed tomography

BACKGROUND

Artificial intelligence (AI) applications can facilitate detection of cervical spine fractures on CT and reduce time to diagnosis by prioritizing suspected cases.

PURPOSE

To assess the effect on time to diagnose cervical spine fractures on CT and diagnostic accuracy of a commercially available AI application.

MATERIALS AND METHODS

In this study (June 2020 - March 2022) with historic controls and prospective evaluation, we evaluated regulatory-cleared AI-software to prioritize cervical spine fractures on CT. All patients underwent non-contrast CT of the cervical spine. The time between CT acquisition and the moment the scan was first opened (DNT) was compared between the retrospective and prospective cohorts. The reference standard for determining diagnostic accuracy was the radiology report created in routine clinical workflow and adjusted by a senior radiologist. Discrepant cases were reviewed and clinical relevance of missed fractures was determined.

RESULTS

2973 (mean age, 55.4 ± 19.7 [standard deviation]; 1857 men) patients were analyzed by AI, including 2036 retrospective and 938 prospective cases. Overall prevalence of cervical spine fractures was 7.6 %. The DNT was 18 % (5 min) shorter in the prospective cohort. In scans positive for cervical spine fracture according to the reference standard, DNT was 46 % (16 min) shorter in the prospective cohort. Overall sensitivity of the AI application was 89.8 % (95 % CI: 84.2-94.0 %), specificity was 95.3 % (95 % CI: 94.2-96.2 %), and diagnostic accuracy was 94.8 % (95 % CI: 93.8-95.8 %). Negative predictive value was 99.1 % (95 % CI: 98.5-99.4 %) and positive predictive value was 63.0 % (95 % CI: 58.0-67.8 %). 22 fractures were missed by AI of which 5 required stabilizing therapy.

CONCLUSION

A time gain of 16 min to diagnosis for fractured cases was observed after introducing AI. Although AI-assisted workflow prioritization of cervical spine fractures on CT shows high diagnostic accuracy, clinically relevant cases were missed.

Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation

OBJECTIVES

To evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs.

METHODS

A retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs and radiologists' authorised reports using a deep learning and natural language processing algorithm, respectively. The AI-detected discrepant findings between images and reports were assessed for clinical relevance by an external radiologist, as part of the commercial service provided by the AI vendor. The selected missed findings were subsequently returned to the institution's radiologist for final review.

RESULTS

In total, 25,104 chest radiographs of 21,039 patients (mean age 61.1 years ± 16.2 [SD]; 10,436 men) were included. The AI software detected discrepancies between imaging and reports in 21.1% (5289 of 25,104). After review by the external radiologist, 0.9% (47 of 5289) of cases were deemed to contain clinically relevant missed findings. The institution's radiologists confirmed 35 of 47 missed findings (74.5%) as clinically relevant (0.1% of all cases). Missed findings consisted of lung nodules (71.4%, 25 of 35), pneumothoraces (17.1%, 6 of 35) and consolidations (11.4%, 4 of 35).

CONCLUSION

The AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low.

CLINICAL RELEVANCE STATEMENT

The AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography.

KEY POINTS

• A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions. • Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations. • Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist's reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevance.

Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice

OBJECTIVE

To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology.

METHODS

This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value.

RESULTS

RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring.

CONCLUSION

The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology.

CRITICAL RELEVANCE STATEMENT

The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation.

KEYPOINTS

• Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap.

Selenoprotein deficiency disorder predisposes to aortic aneurysm formation

Aortic aneurysms, which may dissect or rupture acutely and be lethal, can be a part of multisystem disorders that have a heritable basis. We report four patients with deficiency of selenocysteine-containing proteins due to selenocysteine Insertion Sequence Binding Protein 2 (SECISBP2) mutations who show early-onset, progressive, aneurysmal dilatation of the ascending aorta due to cystic medial necrosis. Zebrafish and male mice with global or vascular smooth muscle cell (VSMC)-targeted disruption of Secisbp2 respectively show similar aortopathy. Aortas from patients and animal models exhibit raised cellular reactive oxygen species, oxidative DNA damage and VSMC apoptosis. Antioxidant exposure or chelation of iron prevents oxidative damage in patient's cells and aortopathy in the zebrafish model. Our observations suggest a key role for oxidative stress and cell death, including via ferroptosis, in mediating aortic degeneration.

Artificial Intelligence Tool for Detection and Worklist Prioritization Reduces Time to Diagnosis of Incidental Pulmonary Embolism at CT

Purpose

To evaluate the diagnostic efficacy of artificial intelligence (AI) software in detecting incidental pulmonary embolism (IPE) at CT and shorten the time to diagnosis with use of radiologist reading worklist prioritization.

Materials and Methods

In this study with historical controls and prospective evaluation, regulatory-cleared AI software was evaluated to prioritize IPE on routine chest CT scans with intravenous contrast agent in adult oncology patients. Diagnostic accuracy metrics were calculated, and temporal end points, including detection and notification times (DNTs), were assessed during three time periods (April 2019 to September 2020): routine workflow without AI, human triage without AI, and worklist prioritization with AI.

Results

In total, 11 736 CT scans in 6447 oncology patients (mean age, 63 years ± 12 [SD]; 3367 men) were included. Prevalence of IPE was 1.3% (51 of 3837 scans), 1.4% (54 of 3920 scans), and 1.0% (38 of 3979 scans) for the respective time periods. The AI software detected 131 true-positive, 12 false-negative, 31 false-positive, and 11 559 true-negative results, achieving 91.6% sensitivity, 99.7% specificity, 99.9% negative predictive value, and 80.9% positive predictive value. During prospective evaluation, AI-based worklist prioritization reduced the median DNT for IPE-positive examinations to 87 minutes (vs routine workflow of 7714 minutes and human triage of 4973 minutes). Radiologists' missed rate of IPE was significantly reduced from 44.8% (47 of 105 scans) without AI to 2.6% (one of 38 scans) when assisted by the AI tool (P < .001).

Conclusion

AI-assisted workflow prioritization of IPE on routine CT scans in oncology patients showed high diagnostic accuracy and significantly shortened the time to diagnosis in a setting with a backlog of examinations.Keywords: CT, Computer Applications, Detection, Diagnosis, Embolism, Thorax, ThrombosisSupplemental material is available for this article.© RSNA, 2023See also the commentary by Elicker in this issue.

Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI)

OBJECTIVES

Only few published artificial intelligence (AI) studies for COVID-19 imaging have been externally validated. Assessing the generalizability of developed models is essential, especially when considering clinical implementation. We report the development of the International Consortium for COVID-19 Imaging AI (ICOVAI) model and perform independent external validation.

METHODS

The ICOVAI model was developed using multicenter data (n = 1286 CT scans) to quantify disease extent and assess COVID-19 likelihood using the COVID-19 Reporting and Data System (CO-RADS). A ResUNet model was modified to automatically delineate lung contours and infectious lung opacities on CT scans, after which a random forest predicted the CO-RADS score. After internal testing, the model was externally validated on a multicenter dataset (n = 400) by independent researchers. CO-RADS classification performance was calculated using linearly weighted Cohen's kappa and segmentation performance using Dice Similarity Coefficient (DSC).

RESULTS

Regarding internal versus external testing, segmentation performance of lung contours was equally excellent (DSC = 0.97 vs. DSC = 0.97, p = 0.97). Lung opacities segmentation performance was adequate internally (DSC = 0.76), but significantly worse on external validation (DSC = 0.59, p < 0.0001). For CO-RADS classification, agreement with radiologists on the internal set was substantial (kappa = 0.78), but significantly lower on the external set (kappa = 0.62, p < 0.0001).

CONCLUSION

In this multicenter study, a model developed for CO-RADS score prediction and quantification of COVID-19 disease extent was found to have a significant reduction in performance on independent external validation versus internal testing. The limited reproducibility of the model restricted its potential for clinical use. The study demonstrates the importance of independent external validation of AI models.

KEY POINTS

• The ICOVAI model for prediction of CO-RADS and quantification of disease extent on chest CT of COVID-19 patients was developed using a large sample of multicenter data. • There was substantial performance on internal testing; however, performance was significantly reduced on external validation, performed by independent researchers. The limited generalizability of the model restricts its potential for clinical use. • Results of AI models for COVID-19 imaging on internal tests may not generalize well to external data, demonstrating the importance of independent external validation.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

BACKGROUND

Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).

OBJECTIVES

To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.

METHODS

The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.

RESULTS

A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.

CONCLUSION

We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

Optimization of Preoperative Lymph Node Staging in Patients with Muscle-Invasive Bladder Cancer Using Radiomics on Computed Tomography

Approximately 25% of the patients with muscle-invasive bladder cancer (MIBC) who are clinically node negative have occult lymph node metastases at radical cystectomy (RC) and pelvic lymph node dissection. The aim of this study was to evaluate preoperative CT-based radiomics to differentiate between pN+ and pN0 disease in patients with clinical stage cT2-T4aN0-N1M0 MIBC. Patients with cT2-T4aN0-N1M0 MIBC, of whom preoperative CT scans and pathology reports were available, were included from the prospective, multicenter CirGuidance trial. After manual segmentation of the lymph nodes, 564 radiomics features were extracted. A combination of different machine-learning methods was used to develop various decision models to differentiate between patients with pN+ and pN0 disease. A total of 209 patients (159 pN0; 50 pN+) were included, with a total of 3153 segmented lymph nodes. None of the individual radiomics features showed significant differences between pN+ and pN0 disease, and none of the radiomics models performed substantially better than random guessing. Hence, CT-based radiomics does not contribute to differentiation between pN+ and pN0 disease in patients with cT2-T4aN0-N1M0 MIBC.

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

Objective

Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians’ understanding and to promote quality of medical AI research.

Methods

We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine.

Conclusion

This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside.

Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach

Treatment planning of gastrointestinal stromal tumors (GISTs) includes distinguishing GISTs from other intra-abdominal tumors and GISTs’ molecular analysis. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status, and mitotic index (MI). Patients diagnosed at the Erasmus MC between 2004 and 2017, with GIST or non-GIST intra-abdominal tumors and a contrast-enhanced venous-phase CT, were retrospectively included. Tumors were segmented, from which 564 image features were extracted. Prediction models were constructed using a combination of machine learning approaches. The evaluation was performed in a 100 × random-split cross-validation. Model performance was compared to that of three radiologists. One hundred twenty-five GISTs and 122 non-GISTs were included. The GIST vs. non-GIST radiomics model had a mean area under the curve (AUC) of 0.77. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for c-KIT, 0.56 for c-KIT exon 11, and 0.52 for the MI. The numbers of PDGFRA, BRAF, and other c-KIT mutations were too low for analysis. Our radiomics model was able to distinguish GISTs from non-GISTs with a performance similar to three radiologists, but less observer dependent. Therefore, it may aid in the early diagnosis of GIST, facilitating rapid referral to specialized treatment centers. As the model was not able to predict any genetic or molecular features, it cannot aid in treatment planning yet.

Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging

Purpose

To compare two artificial intelligence software packages performing normative brain volumetry and explore whether they could differently impact dementia diagnostics in a clinical context.

Methods

Sixty patients (20 Alzheimer’s disease, 20 frontotemporal dementia, 20 mild cognitive impairment) and 20 controls were included retrospectively. One MRI per subject was processed by software packages from two proprietary manufacturers, producing two quantitative reports per subject. Two neuroradiologists assigned forced-choice diagnoses using only the normative volumetry data in these reports. They classified the volumetric profile as “normal,” or “abnormal”, and if “abnormal,” they specified the most likely dementia subtype. Differences between the packages’ clinical impact were assessed by comparing (1) agreement between diagnoses based on software output; (2) diagnostic accuracy, sensitivity, and specificity; and (3) diagnostic confidence. Quantitative outputs were also compared to provide context to any diagnostic differences.

Results

Diagnostic agreement between packages was moderate, for distinguishing normal and abnormal volumetry (K = .41–.43) and for specific diagnoses (K = .36–.38). However, each package yielded high inter-observer agreement when distinguishing normal and abnormal profiles (K = .73–.82). Accuracy, sensitivity, and specificity were not different between packages. Diagnostic confidence was different between packages for one rater. Whole brain intracranial volume output differed between software packages (10.73%, p < .001), and normative regional data interpreted for diagnosis correlated weakly to moderately (r_s = .12–.80).

Conclusion

Different artificial intelligence software packages for quantitative normative assessment of brain MRI can produce distinct effects at the level of clinical interpretation. Clinics should not assume that different packages are interchangeable, thus recommending internal evaluation of packages before adoption.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-022-02898-w.

Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study

Histopathological growth patterns (HGPs) are independent prognosticators for colorectal liver metastases (CRLM). Currently, HGPs are determined postoperatively. In this study, we evaluated radiomics for preoperative prediction of HGPs on computed tomography (CT), and its robustness to segmentation and acquisition variations. Patients with pure HGPs [i.e. 100% desmoplastic (dHGP) or 100% replacement (rHGP)] and a CT-scan who were surgically treated at the Erasmus MC between 2003-2015 were included retrospectively. Each lesion was segmented by three clinicians and a convolutional neural network (CNN). A prediction model was created using 564 radiomics features and a combination of machine learning approaches by training on the clinician's and testing on the unseen CNN segmentations. The intra-class correlation coefficient (ICC) was used to select features robust to segmentation variations; ComBat was used to harmonize for acquisition variations. Evaluation was performed through a 100 × random-split cross-validation. The study included 93 CRLM in 76 patients (48% dHGP; 52% rHGP). Despite substantial differences between the segmentations of the three clinicians and the CNN, the radiomics model had a mean area under the curve of 0.69. ICC-based feature selection or ComBat yielded no improvement. Concluding, the combination of a CNN for segmentation and radiomics for classification has potential for automatically distinguishing dHGPs from rHGP, and is robust to segmentation and acquisition variations. Pending further optimization, including extension to mixed HGPs, our model may serve as a preoperative addition to postoperative HGP assessment, enabling further exploitation of HGPs as a biomarker.

Radiology in the Era of Value-Based Healthcare: A Multi Society Expert Statement From the ACR, CAR, ESR, IS3R, RANZCR, and RSNA

BACKGROUND

The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation.

METHODS, FINDINGS AND INTERPRETATION

This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning

Patients with BRAF mutated (BRAF-mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the BRAF status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the BRAF status in a non-invasive manner. Patients with melanoma lung metastases, known BRAF status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for BRAF mutation status. A combination of various machine learning methods was used to develop BRAF decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51 BRAF-mt; 52 BRAF wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the BRAF mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria.

Radiology in the Era of Value-Based Healthcare: A Multi-Society Expert Statement From the ACR, CAR, ESR, IS3R, RANZCR, and RSNA

BACKGROUND

The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation.

METHODS, FINDINGS AND INTERPRETATION

This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Radiology in the era of value-based healthcare: a multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA

Background

The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology’s central role; this may have future negative consequences for resource allocation.

Methods, findings and interpretation

This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the healthcare value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Radiology in the era of value-based healthcare: A multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR and RSNA

BACKGROUND

The value-based healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation.

METHODS, FINDINGS AND INTERPRETATION

This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia and New Zealand, describes the place of radiology in VBH models and the healthcare value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Radiology in the Era of Value-based Healthcare: A Multi-Society Expert Statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA

Background The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. Methods, findings and interpretation This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined. Published under a CC BY 4.0 license.

The Need for Sustainable Teleconsultation Systems in the Aftermath of the First COVID-19 Wave

The physical and social distancing measures that have been adopted worldwide because of COVID-19 will probably remain in place for a long time, especially for senior adults, people with chronic conditions, and other at-risk populations. Teleconsultations can be useful in ensuring that patients continue to receive clinical care while reducing physical crowding and avoiding unnecessary exposure of health care staff. Implementation processes that typically take months of planning, budgeting, pilot testing, and education were compressed into days. However, in the urgency to deal with the present crisis, we may be forgetting that the introduction of digital health is not exclusively a technological issue, but part of a complex organizational change problem. This viewpoint offers insight regarding issues that rapidly adopted teleconsultation systems may face in a post-COVID-19 world.

The Value of Quantitative Musculoskeletal Imaging

Musculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.

We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed.

Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics

PURPOSE

Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types.

METHODS

Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1-weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset.

RESULTS

The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74.

CONCLUSIONS

Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status.

Assessment of actionable findings in radiology reports

PURPOSE

The American College of Radiology (ACR) Actionable Reporting Work Group defined three categories of imaging findings that require additional, nonroutine communication with the referring physician because of their urgency or unexpectedness. The objective of this study was to determine the prevalence of actionable findings in radiology reports, and to assess how well radiologists agree on the categorisation of actionable findings.

METHOD

From 124,909 consecutive radiology reports stored in the electronic health record system of a large university hospital, 1000 reports were randomly selected. Two radiologists independently annotated all actionable findings according to the three categories of urgency defined by the ACR Work Group. Annotation differences were resolved in a consensus meeting and a final category was established for each report. Interannotator agreement was measured by accuracy and the kappa coefficient.

RESULTS

The prevalence of the three categories of actionable findings together was 32.5 %. Of all reports, 10.9 % were from patients seen in the emergency department. Prevalence of actionable findings for these patients (45.9 %) was considerably higher than for patients in routine clinical care (30.9 %). Interannotator agreement scores on the categorisation of actionable findings were 0.812 for accuracy and 0.616 for kappa coefficient.

CONCLUSIONS

The prevalence of actionable findings in radiology reports is high. The interannotator agreement scores are moderate, indicating that categorisation of actionable findings is a difficult task. To avoid unneeded increase in the workload of radiologists, in particular in routine practice, clinical context may need to be considered in deciding whether a finding is actionable.

Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI

Background

Well differentiated liposarcoma (WDLPS) can be difficult to distinguish from lipoma. Currently, this distinction is made by testing for MDM2 amplification, which requires a biopsy. The aim of this study was to develop a noninvasive method to predict MDM2 amplification status using radiomics features derived from MRI.

Methods

Patients with an MDM2‐negative lipoma or MDM2‐positive WDLPS and a pretreatment T1‐weighted MRI scan who were referred to Erasmus MC between 2009 and 2018 were included. When available, other MRI sequences were included in the radiomics analysis. Features describing intensity, shape and texture were extracted from the tumour region. Classification was performed using various machine learning approaches. Evaluation was performed through a 100 times random‐split cross‐validation. The performance of the models was compared with the performance of three expert radiologists.

Results

The data set included 116 tumours (58 patients with lipoma, 58 with WDLPS) and originated from 41 different MRI scanners, resulting in wide heterogeneity in imaging hardware and acquisition protocols. The radiomics model based on T1 imaging features alone resulted in a mean area under the curve (AUC) of 0·83, sensitivity of 0·68 and specificity of 0·84. Adding the T2‐weighted imaging features in an explorative analysis improved the model to a mean AUC of 0·89, sensitivity of 0·74 and specificity of 0·88. The three radiologists scored an AUC of 0·74 and 0·72 and 0·61 respectively; a sensitivity of 0·74, 0·91 and 0·64; and a specificity of 0·55, 0·36 and 0·59.

Conclusion

Radiomics is a promising, non‐invasive method for differentiating between WDLPS and lipoma, outperforming the scores of the radiologists. Further optimization and validation is needed before introduction into clinical practice.

Patient-specific workup of adrenal incidentalomas

PURPOSE

: To develop a clinical prediction model to predict a clinically relevant adrenal disorder for patients with adrenal incidentaloma.

MATERIALS AND METHODS

: This retrospective study is approved by the institutional review board, with waiver of informed consent. Natural language processing is used for filtering of adrenal incidentaloma cases in all thoracic and abdominal CT reports from 2010 till 2012. A total of 635 patients are identified. Stepwise logistic regression is used to construct the prediction model. The model predicts if a patient is at risk for malignancy or hormonal hyperfunction of the adrenal gland at the moment of initial presentation, thus generates a predicted probability for every individual patient. The prediction model is evaluated on its usefulness in clinical practice using decision curve analysis (DCA) based on different threshold probabilities. For patients whose predicted probability is lower than the predetermined threshold probability, further workup could be omitted.

RESULTS

: A prediction model is successfully developed, with an area under the curve (AUC) of 0.78. Results of the DCA indicate that up to 11% of patients with an adrenal incidentaloma can be avoided from unnecessary workup, with a sensitivity of 100% and specificity of 11%.

CONCLUSION

: A prediction model can accurately predict if an adrenal incidentaloma patient is at risk for malignancy or hormonal hyperfunction of the adrenal gland based on initial imaging features and patient demographics. However, with most adrenal incidentalomas labeled as nonfunctional adrenocortical adenomas requiring no further treatment, it is likely that more patients could be omitting from unnecessary diagnostics.

Using Cost-Effectiveness Analysis to Measure Value in Musculoskeletal Imaging

In the era of value-based health care, adding value is a key element in providing care. The choice of appropriate imaging modality and protocol should be based on consideration of patients' values, health care outcomes, and cost-effectiveness, taking into account the perspective of the decision maker, the health care system, and society at large. This article provides an overview of the available tools to measure value, outcomes, and cost-effectiveness in musculoskeletal radiology, illustrated with relevant examples.

Systematic review of guidelines on imaging of asymptomatic coronary artery disease

OBJECTIVES

The purpose of this study was to critically appraise guidelines on imaging of asymptomatic coronary artery disease (CAD).

BACKGROUND

Various imaging tests exist to detect CAD in asymptomatic persons. Because randomized controlled trials are lacking, guidelines that address the use of CAD imaging tests may disagree.

METHODS

Guidelines in English published between January 1, 2003, and February 26, 2010, were retrieved using MEDLINE, Cumulative Index to Nursing and Allied Health Literature, the National Guideline Clearinghouse, the National Library for Health, the Canadian Medication Association Infobase, and the Guidelines International Network International Guideline Library. Guidelines developed by national and international medical societies from Western countries, containing recommendations on imaging of asymptomatic CAD were included. Rigor of development was scored by 2 independent reviewers using the Appraisal of Guidelines Research and Evaluation (AGREE) instrument. One reviewer performed full extraction of recommendations, which was checked by a second reviewer.

RESULTS

Of 2,415 titles identified, 14 guidelines met our inclusion criteria. Eleven of 14 guidelines reported relationship with industry. The AGREE scores varied across guidelines from 21% to 93%. Two guidelines considered cost effectiveness. Eight guidelines recommended against or found insufficient evidence for testing of asymptomatic CAD. The other 6 guidelines recommended imaging patients at intermediate or high CAD risk based on the Framingham risk score, and 5 considered computed tomography calcium scoring useful for this purpose.

CONCLUSIONS

Guidelines on risk assessment by imaging of asymptomatic CAD contain conflicting recommendations. More research, including randomized controlled trials, evaluating the impact of imaging on clinical outcomes and costs is needed.

Systematic review of guidelines on cardiovascular risk assessment: Which recommendations should clinicians follow for a cardiovascular health check?

OBJECTIVE

To appraise guidelines on cardiovascular risk assessment to guide selection of screening interventions for a health check.

DATA SOURCES

Guidelines in the English language published between January 1, 2003, and May 2, 2009, were retrieved using MEDLINE and CINAHL. This was supplemented by searching the National Guideline Clearinghouse, National Library for Health, Canadian Medical Association Infobase, and G-I-N International Guideline Library.

STUDY SELECTION

We included guidelines developed on behalf of professional organizations from Western countries, containing recommendations on cardiovascular risk assessment for the apparently healthy population. Titles and abstracts were assessed by 2 independent reviewers. Of 1984 titles identified, 27 guidelines met our criteria.

DATA EXTRACTION

Rigor of guideline development was assessed by 2 independent reviewers. One reviewer extracted information on conflicts of interest and recommendations.

RESULTS

Sixteen of 27 guidelines reported conflicts of interest and 17 showed considerable rigor. These included recommendations on assessment of total cardiovascular risk (7 guidelines), dyslipidemia (2), hypertension (2), and dysglycemia (7). Recommendations on total cardiovascular risk and dyslipidemia included prediction models integrating multiple risk factors, whereas remaining recommendations were focused on single risk factors. No consensus was found on recommended target populations, treatment thresholds, and screening tests.

CONCLUSIONS

Differences among the guidelines imply important variation in allocation of preventive interventions. To make informed decisions, physicians should use only the recommendations from rigorously developed guidelines.

The 30-day mortality of ruptured abdominal aortic aneurysms: influence of gender, age, diameter and comorbidities

AIM

The aim of this study was to determine the influence of gender, age, the aneurysm diameter and comorbidity on the 30-day mortality after open repair of ruptured abdominal aortic aneurysms (AAA).

METHODS

Between January 1, 1993, and December 31, 2006 all consecutive patients who underwent open repair for a ruptured AAA at the tertiary care of Catharina Teaching Hospital were included in this study (N=186). Patients who underwent endovascular repair of their ruptured abdominal aortic aneurysms were excluded from this study. Patient and procedure characteristics were collected and analyzed in relation to 30-day mortality. The association between age, gender, diameter of AAA and comorbidity with 30-day mortality was analyzed with c2 are and logistic regression; a P value <0.05 was considered significant.

RESULTS

In this study there were 186 patients with ruptured AAA repair with an 30-day mortality of 36.6% (68/186). Among female patient 30-day mortality was 45.8% (11/24) compared with 35.2% (57/162) among male patients (P=0.31). Patients of 80 years and older had a 61.3% (19/31) 30-day mortality where younger patients had 33% (51/155) 30-day mortality (P=0.02). Thirty-day mortality was 47.2% (17/36) for patients with an AAA less than 65 mm compared with 34% (36/104) for patients with an AAA of 65 mm or larger (P=0.16). Multivariate analysis demonstrated age was a significant predictor of ruptured AAA repair mortality (P=0.017).

CONCLUSION

In this study, age was the only significant risk factor of 30-day mortality after open repair in patients with ruptured AAA.

Ruptured Abdominal Aortic Aneurysms: Endovascular Repair versus Open Surgery—Systematic Review

PURPOSE

To perform a systematic review of studies in which endovascular repair was compared with open surgery in the treatment of patients with a ruptured abdominal aortic aneurysm (AAA).

MATERIALS AND METHODS

A search of the English-language literature from January 1994 until March 2006 was performed. Inclusion criteria for studies were that they were about a comparison between patients who underwent endovascular repair and patients who underwent open surgery, that each treatment group included at least five patients, that information about patients' hemodynamic condition at presentation was reported, and that 30-day mortality was reported for each treatment group. Two reviewers independently extracted the data, and discrepancies were resolved by an arbiter. Random-effects models and meta-regression analysis were used to calculate crude and adjusted odds ratios (ORs) for endovascular repair versus open surgery.

RESULTS

Ten studies, in which the results of 478 procedures (n=148 for endovascular repair, n=330 for open surgery) were reported, met the inclusion criteria. All studies were observational; no randomized controlled trials were found. The pooled 30-day mortality was 22% (95% confidence interval [CI]: 16%, 29%) for endovascular repair and 38% (95% CI: 32%, 45%) for open surgery. The pooled rate for total systemic complications was 28% (95% CI: 17%, 48%) for endovascular repair and 56% (95% CI: 37%, 85%) for open surgery. The crude OR for 30-day mortality for endovascular repair compared with open surgery was 0.45 (95% CI: 0.28, 0.72). After adjustment for patients' hemodynamic condition, the OR was 0.67 (95% CI: 0.31, 1.44).

CONCLUSION

In this systematic review, after adjustment for patients' hemodynamic condition at presentation, a benefit in 30-day mortality for endovascular repair compared with open surgery for patients with a ruptured AAA was observed, but it was not statistically significant.

Endovascular repair versus open surgery in patients with ruptured abdominal aortic aneurysms: clinical outcomes with 1-year follow-up

OBJECTIVE

To compare the clinical outcomes of treatment after endovascular repair and open surgery in patients with ruptured infrarenal abdominal aortic aneurysms (AAAs), including 1-year follow-up.

METHODS

All consecutive conscious patients with ruptured infrarenal AAAs who presented to our tertiary care teaching hospital between January 1, 2001, and December 31, 2005, were included in this study (n = 55). Twenty-six patients underwent endovascular repair, and 29 patients underwent open surgery. Patients who were hemodynamically too unstable to undergo a computed tomography angiography scan were excluded. Outcomes evaluated were intraoperative mortality, 30-day mortality, systemic complications, complications necessitating surgical intervention, and mortality and complications during 1-year follow-up. The statistical tests we used were the Student t test, chi2 test, Fisher exact test, and Mann-Whitney U test (two sided; alpha = .05).

RESULTS

Thirty-day mortality was 8 (31%) of 26 patients who underwent endovascular repair and 9 (31%) of 29 patients who underwent open surgery (P = .98). Systemic complications and complications necessitating surgical intervention during the initial hospital stay were similar in both treatment groups (8/26 [31%] and 5/26 [19%] for endovascular repair, respectively, and 9/29 [31%] and 8/29 [28%] for open surgery, respectively; P > .40). During 1-year follow-up, two patients initially treated with endovascular repair died as a result of non-aneurysm-related causes; no death occurred in the open surgery group. Complications during 1-year follow-up were 1 (5%) of 20 for endovascular repair and 4 (16%) of 25 for open surgery (P = .36).

CONCLUSIONS

On the basis of our study with a highly selected population, the mortality and complication rates after endovascular repair may be similar compared with those after open surgery in patients treated for ruptured infrarenal AAAs.

Acute Abdominal Aortic Aneurysms: Cost Analysis of Endovascular Repair and Open Surgery in Hemodynamically Stable Patients with 1-year Follow-up

PURPOSE

To retrospectively assess the in-hospital and 1-year follow-up costs of endovascular aneurysm repair and conventional open surgery in patients with acute infrarenal abdominal aortic aneurysm (AAA) by using a resource-use approach.

MATERIALS AND METHODS

Institutional Review Board approval was obtained, and informed consent was waived. In-hospital costs for all consecutive patients (61 men, six women; mean age, 72.0 years) who underwent endovascular repair (n = 32) or open surgery (n = 35) for acute infrarenal AAA from January 1, 2001, to December 31, 2004, were assessed by using a resource-use approach. Patients who did not undergo computed tomography before the procedure were excluded from analysis. One-year follow-up costs were complete for 30 patients who underwent endovascular repair and for 34 patients who underwent open surgery. Costs were assessed from a health care perspective. Mean costs were calculated for each treatment group and were compared by using the Mann-Whitney U test (alpha = .05). The influence of clinical variables on the total in-hospital cost was investigated by using univariate and multivariate analyses. Costs were expressed in euros for the year 2003.

RESULTS

Sex, age, and comorbidity did not differ between treatment groups (P > .05). The mean total in-hospital costs were lower for patients who underwent endovascular repair than for those who underwent open surgery (euro20 767 vs euro35 470, respectively; P = .004). The total costs, including those for 1-year follow-up, were euro23 588 for patients who underwent endovascular repair and euro36 448 for those who underwent open surgery (P = .05). The results of multivariate analysis indicated that complications had a significant influence on total in-hospital cost; patients who had complications incurred total in-hospital costs that were 2.27 times higher than those for patients who had no complications.

CONCLUSION

Total in-hospital costs and total overall costs, which included 1-year follow-up costs, were lower in patients with acute AAA who underwent endovascular repair than in those who underwent open surgery.

Patient-risk due to negligence by Locum General Practitioners outside of office hours

In Holland, first tier medical care outside of office hours is provided by regular GP's acting as Locum GP's. On several occasions concerns have been expressed about Locum GP's not visiting the patient when necessary. The number of complaints against Locum GP's submitted to the Medical Boards has been increasing over time and relate in particular to refusals of Locum GP's to visit the patient. In many of these cases the patient died. The paper develops a quantitative estimate of the risk for the patient due to negligence of Locum GP's. The measure of the risk is taken as the number of patients who died unnecessarily due to negligence of Locum GP's as determined by the Medical Boards. The paper estimates that in Holland in the year 2000 about 1500 patients would probably not have died had the Locum GP's working out-of-hours not been negligent by contravening professional standards. In more than half of these cases the Locum GP's did not visit the patient, or did not visit the patient in time, when necessary. This level of patient risk must be regarded as socially unacceptable. One important explanation for these accidents could be that doctors are largely unfamiliar with the professional standards developed by the Medical Boards. Because of the assumptions made and the uncertainty regarding the data used, the actual numbers of these accidents may be higher or lower than estimated here. Although more reliable data will allow the results to be determined more reliably, this is unlikely to change the acceptability of the risk.

L-glutamate, L-arginine and L-citrulline levels in cerebrospinal fluid of Parkinson's disease, multiple system atrophy, and Alzheimer's disease patients

Alterations in neuronal nitric oxide (NO) production may play a role in the pathophysiology of Parkinson's disease (PD) Alzheimer's disease (AD), and multiple system atrophy (MSA). The biosynthesis of NO is dependent on the availability of L-arginine, the substrate for NO-synthase (NOS), and on L-glutamate, which stimulates NO synthesis via the NMDA receptor. In this process L-citrulline is formed. We measured the levels of these amino acids in cerebrospinal fluid (CSF) of 108 PD patients, 12 AD patients, 15 MSA patients and 21 healthy subjects. A slight but statistically significant elevation of CSF L-citrulline was found in MSA patients, while CSF L-glutamate was found to be significantly decreased in AD patients. We found no significant changes in L-arginine levels. Although the relation between the CSF levels of these amino acids and neuronal NO production is still unclear, our findings suggest that AD is associated with a decrease in NO synthesis.

Dietary glutamine supplementation reduces plasma nitrate levels in rats

It was recently shown that L-glutamine inhibits vascular nitric oxide (NO) production in vitro. The present study investigated the effect of glutamine enriched enteral diets on in vivo NO production in the rat. Nitrate, the stable end-product of NO production, was measured in plasma and 24 h urine collections in glutamine supplemented rats (6.25%, 12.5% and 25% w/w) and compared to the effect of isocaloric, nitrogenous control diets. Glutamine supplementation increased plasma levels of glutamine (up to 91%), arginine (up to 17%) and citrulline (up to 54%). After 1 week of glutamine supplementation plasma nitrate levels were significantly reduced by 50% compared to control (P < 0. 0001); irrespective of the amount of supplementation. No further decrease was observed after 2 weeks of feeding. No differences in daily urinary losses were found between the groups. These results point to an in vivo inhibitory effect of glutamine supplemented enteral feeding on NO production.

The use of biliary CEA measurements in the diagnosis of recurrent colorectal cancer

To assess the usefulness of biliary CEA determinations in the diagnosis of recurrent tumour, gallbladder bile was sampled in patients who underwent laparotomy for proven or suspected recurrent colorectal cancer and in control patients. Biliary CEA concentrations in controls were < 5 ng/ml, whereas significantly elevated CEA concentrations were found in the bile of all patients with tumour recurrence. Serum concentrations in these patients were elevated in 77% only. In a series of 12 patients with (a) suspicious lesion(s) on liver imaging but normal serum CEA concentration during follow-up, biliary CEA determination differentiated clearly between metastases and benign lesions. Biliary CEA determination seems to aid detection of tumour recurrence at an early stage and may preclude unnecessary surgery in patients with undefined liver lesions.

Arginine deficiency in bile duct-ligated rats after surgery: the role of plasma arginase and gut endotoxin restriction

BACKGROUND & AIMS

Arginine deficiency may underlie the cellular immune depression after surgery in obstructive jaundice, which is associated with gut-derived endotoxemia. The aim of this study was to study arginine metabolism in the bile duct-ligated rat (BDL) after laparotomy.

METHODS

Treatment with cholestyramine, a known endotoxin binder, was used to evaluate the role of gut-derived endotoxemia.

RESULTS

In BDL rats, arginine levels were lower compared with those in sham-operated controls (P < 0.005), despite a three-fold increase in renal arginine release (P < 0.01). Liver and gut arginine handling also could not explain the reduced arginine levels. Higher plasma arginase activity (P < 0.0001) was measured in BDL rats, explaining both the lower arginine levels (r = 0.73, P < 0.01) and the increase in arginase product levels: ornithine (P < 0.005 and r = 0.72; P < 0.01) and urea (P < 0.01). Cholestyramine treatment prevented the decrease in postoperative arginine deficiency by reducing plasma arginase activity by 43% (P < 0.005). In addition, it significantly lowered plasma levels of the other liver enzymes (aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, and alkaline phosphatase; P < 0.05) in BDL rats.

CONCLUSIONS

The study concluded that arginine deficiency in BDL rats after surgery is caused by high plasma liver arginase activity. Cholestyramine prevented the arginine deficiency by reducing plasma arginase activity through the inhibition of additional endotoxin-mediated hepatocellular damage after surgery in BDL rats.

The role of nitric oxide in bacterial meningitis in children

To investigate the role of nitric oxide (NO) in bacterial meningitis, concentrations in serum, cerebrospinal fluid (CSF), or both of the precursor (L-arginine) and degradation products of NO (nitrate, nitrite) and tumor necrosis factor (TNF)-alpha were measured in 35 patients and 30 controls. CSF nitrate levels were significantly elevated, mainly due to increased blood-brain barrier permeability, and are therefore not a good parameter for gauging endogenous NO production in the CSF compartment. CSF NO/nitrite levels were significantly elevated in patients. NO/nitrite levels decreased over time (26%/6 h; P < .001). CSF levels of NO/nitrite correlated with those of TNF-alpha (r = .55; P = .001) and glucose (r = -.43; P = .02). CSF levels of L-arginine were lower in patients than in controls (P < .001). Dexamethasone did not exert a significant effect on NO metabolism. In conclusion, enhanced NO production may contribute to anaerobic glycolysis and neurologic damage in bacterial meningitis.

Detection of occult liver metastases by measurement of biliary carcinoembryonic antigen concentrations

OBJECTIVE

To assess whether biliary CEA concentrations can be used as early markers of occult liver metastases in patients with colorectal cancer.

DESIGN

Consecutive open study.

SETTING

University hospital, The Netherlands.

SUBJECTS

76 patients with a primary colorectal carcinoma (group 1) and 19 patients who had recently undergone a curative resection of a locally advanced carcinoma (group 2).

INTERVENTIONS

Bile sampling by transhepatic puncture of the gallbladder.

MAIN OUTCOME MEASURE

Recurrence of tumour.

RESULTS

Twenty-one of the 76 patients (28%) with primary colorectal carcinoma had liver metastases; all had a raised biliary CEA concentration. Of the remaining 55 patients 39 (71%) also had raised CEA concentrations. At a median follow-up of 30 months, only seven patients had developed liver metastases, indicating a high number of false-positive results. In Group 2 7/19 patients (37%) had a CEA concentration above the cut-off point. Six of them developed recurrent tumour; 2 patients had liver metastases and 4 others had extrahepatic recurrences. None of the 12 patients with biliary CEA concentrations within the reference range has developed recurrent tumour.

CONCLUSION

Biliary CEA concentrations do not predict the presence of occult liver metastases if bile samples are taken during resection of the primary tumour. If samples are taken some time afterwards, a raised CEA concentration seems to predict recurrence at an early stage, either in or outside the liver.

Serum nitrate levels in ulcerative colitis and Crohn's disease

BACKGROUND

Nitric oxide is an important mediator in inflammatory and autoimmune-mediated tissue destruction and may be of pathophysiologic importance in inflammatory bowel disease. We studied whether serum levels of nitrate, the stable end-product of nitric oxide, are increased in active Crohn's disease or ulcerative colitis, in comparison with quiescent disease and healthy controls. The setting was the gastroenterology unit of the Free University Hospital, Amsterdam.

METHODS

In 146 patients--75 with ulcerative colitis and 71 with Crohn's disease--and 33 controls serum nitrate was measured by the Griess reaction after enzymatic conversion of nitrate to nitrite with nitrate reductase.

RESULTS

Median serum nitrate concentrations did not differ statistically significantly between ulcerative colitis (median, 34.2 mumol/l; range, 15.6-229.4 mumol/l), Crohn's disease (median 32.3 mumol; range 13.2-143.2 mumol/l), and healthy controls (median, 28.7 mumol/l; range, 13.0-108.4 mumol/l). However, when active ulcerative colitis patients (median, 44 mumol/l; range, 29.1-229.4 mumol/l were compared with inactive ulcerative colitis patients (median, 31.2 mumol/l; range, 15.6-59.7 mumol/l), a significant difference in nitrate concentration was found (p < 0.0001). A significant positive correlation was found between serum nitrate levels in ulcerative colitis and erythrocyte sedimentation rate (ESR) (r = 0.30, p - 0.01), leucocyte count (r = 0.27, p = 0.02), and thrombocyte count (r = 0.24, p = 0.04). Comparing active Crohn's disease patients (median, 37.5 mumol/l; range, 13.2-143.2 mumol/l) with inactive Crohn's disease patients (median, 31.3 mumol/l; range, 14.5-92.3 mumol/l) also showed a significant difference in serum nitrate concentration (p < 0.009). Serum nitrate levels correlated with the ESR (r = 0.26, p = 0.028) and serum albumin (r = 0.38, p = 0.004) as well.

CONCLUSION

Nitric oxide production is increased in both active ulcerative colitis and Crohn's disease and may be implicated in the pathogenesis of inflammatory bowel disease.

Local colonic blood pressure degree through aortic reconstruction procedures: a porcine model

Sigmoideal ischemia after aortic grafting is a severe complication with high morbidity and mortality. To investigate the basics of this circulatory problem an animal model was created with sigmoideal ischemia that could be quantified. For this purpose a new pig model was developed with stable general circulatory and ventilatory parameters for several hours, while at the same time controlled sigmoideal ischemia was induced. In five pigs a left retroperitoneal approach to the aorta was performed to isolate the caudal mesenteric artery (CMA). Sigmoideal ischemia was achieved by ligating the collateral circulation and constricting the distal aorta. A flow probe was applied to the CMA. An intravascular saturation probe was introduced in the caudal mesenteric vein (CMV) and a pulse oximeter was applied to the serosal surface of the sigmoid. Every hour, blood gas analyses from the carotic artery, CMA, and CMV were completed. Registrations of all circulatory and ventilatory parameters were performed with the help of a computer. The mean flow in the CMA was 29 mL/min (13-45) and decreased to 5 mL/min (3-7) after aortic constriction. Parameters reflecting the stability of the model, such as the cardiac index (mean 89 mL/min kg-1), the mixed venous oxygen saturation (mean 67%), and the total body oxygen consumption (mean 3.3 mL/min kg-1), did not change with statistical significance during 4 h of partial aortic constriction. The conclusion is that a new model has been developed of quantitative sigmoideal ischemia in the pig that was stable for several hours.