Cost-effectiveness modelling in diagnostic imaging: a stepwise approach

Establishing and Leading a 3D Postprocessing Radiology Lab: A Managerial and Leadership Perspective

The rapid acquisition of larg volumes of thin-section CT images has created a considerable need and interest for 3D postprocessing during the interpretation of medical imaging. As a result of the increasing number of postprocessing applications, requiring diagnostic radiologists to perform postprocessing is no longer realistic. This article is a comprehensive review of medical resources regarding establishing a postprocessing radiology laboratory. Besides, leadership and managerial aspects have been covered through a professional business lens. In large-volume settings, a dedicated 3D postprocessing lab ensures the quality, reproducibility, and efficiency of images. Adequate staffing is necessary to fulfill the postprocessing requirements. Educational and experience requirements for 3D technologists may vary among different running laboratories. To evaluate the establishment and running of a 3D lab, it is beneficial to implement diagnostic radiology cost-effectiveness tools. Although establishing a 3D lab has many benefits, certain challenges should be considered. Outsourcing or offshoring may serve as alternatives for establishing a postprocessing laboratory. Building and operating a 3D lab is a significant change in healthcare facilities, and it is crucial for organizations to be aware of the strong resistance toward alternatives the status quo, known as the status quo trap. The change process has essential steps, and skipping the steps creates an illusion of speed but never produces satisfactory results. The organization should ensure the engagement of all interested parties in the whole process. Moreover, a clear vision and proper communication of the vision are vital, and it is crucial to value small wins and ensure expectation clarity in leading the lab during the process.

Has the quality of reporting improved since it became mandatory to use the Standards for Reporting Diagnostic Accuracy?

OBJECTIVES

To investigate whether making the Standards for Reporting Diagnostic Accuracy (STARD) mandatory by the leading journal 'Radiology' in 2016 improved the quality of reporting of diagnostic accuracy studies.

METHODS

A validated search term was used to identify diagnostic accuracy studies published in Radiology in 2015 and 2019. STARD adherence was assessed by two independent reviewers. Each item was scored as yes (1 point) if adequately reported or as no (0 points) if not. The total STARD score per article was calculated. Wilcoxon-Mann-Whitney tests were used to evaluate differences of the total STARD scores between 2015 and 2019. In addition, the total STARD score was compared between studies stratified by study design, citation rate, and data collection.

RESULTS

The median number of reported STARD items for the total of 66 diagnostic accuracy studies from 2015 to 2019 was 18.5 (interquartile range [IQR] 17.5-20.0) of 29. Adherence to the STARD checklist significantly improved the STARD score from a median of 18.0 (IQR 15.5-19.5) in 2015 to a median of 19.5 (IQR 18.5-21.5) in 2019 (p < 0.001). No significant differences were found between studies stratified by mode of data collection (prospective vs. retrospective studies, p = 0.68), study design (cohort vs. case-control studies, p = 0.81), and citation rate (two groups divided by median split [< 0.56 citations/month vs. ≥ 0.56 citations/month], p = 0.54).

CONCLUSIONS

Making use of the STARD checklist mandatory significantly increased the adherence with reporting standards for diagnostic accuracy studies and should be considered by editors and publishers for widespread implementation.

CRITICAL RELEVANCE STATEMENT

Editors may consider making reporting guidelines mandatory to improve the scientific quality.

Use of Decision-Analytic Modelling to Assess the Cost-Effectiveness of Diagnostic Imaging of the Spine, Shoulder, and Knee: A Scoping Review

BACKGROUND

Limited evidence is available on the cost-effectiveness of diagnostic imaging for back, neck, knee, and shoulder complaints. Decision analytic modelling may be an appropriate method to synthesise evidence from multiple sources, and overcomes issues with trial-based economic evaluations.

OBJECTIVE

The aim was to describe the reporting of methods and objectives utilised in existing decision analytic modelling studies that assess the cost-effectiveness of diagnostic imaging for back, neck, knee, and shoulder complaints.

METHODS

Decision analytic modelling studies investigating the use of any imaging modality for people of any age with back, neck, knee, or shoulder complaints were included. No restrictions on comparators were applied, and included studies were required to estimate both costs and benefits. A systematic search (5 January 2023) of four databases was conducted with no date limits imposed. Methodological and knowledge gaps were identified through a narrative summary.

RESULTS

Eighteen studies were included. Methodological issues were identified relating to the poor reporting of methods, and measures of effectiveness did not incorporate changes in quantity and/or quality of life (cost-utility analysis in only ten of 18 studies). Included studies, particularly those investigating back or neck complaints, focused on conditions that were of low prevalence but have a serious impact on health (i.e. cervical spine trauma, cancer-related back pain).

CONCLUSIONS

Future models should pay particular attention to the identified methodological and knowledge gaps. Investment in the health technology assessment of these commonly utilised diagnostic imaging services is needed to justify the current level of utilisation and ensure that these services represent value for money.

An economic impact of incorrect referrals for MRI and CT scans: A retrospective analysis

Background and Aims

Up to date, no research on the economic efficacy of diagnostic modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), has been done in Central Asia. The aim of this study was to analyse the inappropriate appointments of MRI and CT scanning procedures in Kazakhstan.

Methods

We used the imaging diagnostic reports and medical records from 9725 planned outpatient CT and MRI exams performed in two major hospitals in Almaty. The study period was for the period 2014-2019. The independent experts-radiologists evaluated the MRI and CT exams for validity using the ACR® compliance standards and RCR recommendations.

Results

The results showed that the combined costs of MRI and CT scans increased by $17.982 between 2014 ($22.537) and 2019 ($40.519), p = 0.002. The highest rate of MRI examinations was observed in 2019, with a rate of 6.9 per 10,000 people. It was determined that in 2019 the highest rate for men who undertook CT was equal to 12.4 per 10,000 people, while for women it was equivalent to 5.7 per 10,000 patients. The majority of non-corresponding imaging examinations (n = 1304) were referred for MRI and CT scans by general practitioners. We detected the irrational referrals for head and neck radiological examinations in n = 178 (13.7%) cases, and the abdominal cavity checks in n = 249 (19.1%) cases (p = 0.001). The main portion of erroneously unreasonable referrals for examination of the abdominal organs was made by surgeons in n = 43 (3.3%) cases.

Conclusion

The findings indicated an increase in the number of referrals for unnecessary CT and MRI tests over the research period (2014-2019). It had a substantial impact on the rise in healthcare system expenses. The results demonstrate the need for the education of GPs and improving the approaches for diagnostics.

Are immediate postoperative X-Rays valuable in evaluating complications of primary total hip arthroplasty?

PURPOSE

This study aimed to investigate the complications of primary total hip arthroplasty based on immediate postoperative X-rays. The overall quality and cost of X-rays were assessed.

METHODS

The institutional database was queried to identify all patients who underwent total hip arthroplasty in a single institution between January 1, 2018, and December 31, 2018. Immediate postoperative X-rays were reviewed to identify the complications such as periprosthetic fractures, dislocation, and fixation failure. The quality and cost of X-ray were assessed. The complications were categorized as "known" and "unknown" according to the intraoperative fluoroscopic results.

RESULTS

A total of 518 total hip arthroplasties were included in this study. Based on intraoperative fluoroscopy, periprosthetic fractures were found in 10 (2%) THAs. Compared to the X-rays taken immediately after surgery, 9 periprosthetic fractures (recorded as "known") were found and 1 was not (recorded as "unknown"). There was no significant difference between intraoperative fluoroscopy and X-rays (P > 0.05). Of the 518 X-rays, 225 (43%) were of suboptimal quality. The cost of a single portable pelvic X-ray was $647.

CONCLUSION

In total hip arthroplasty, X-rays taken immediately after surgery rarely reveal unknown complications. The X-rays are often of suboptimal quality, have minimal clinical utility, and are less cost-effective.

A Primer to Cost-Effectiveness Analysis in Breast Cancer Imaging: A Review of the Literature

Currently, there is a multitude of methods for evaluating the costs and benefits of programs, tools, etc. While cost-benefit analysis (CBA) is commonly used, cost-effectiveness analysis (CEA) is a more appropriate method of evaluation in clinical contexts, such as radiology practices, as CEAs use units such as life years gained as opposed to money (as is the case for CBAs). This review examines CEAs performed within the past 15 years to highlight their applications and key findings in the context of medical imaging.

In total, 20 articles published between 2006 and 2022 were identified using a PubMed search for keywords including “cost-effectiveness analysis,” “breast cancer,” and “medical imaging,” with studies lacking a substantial discussion of CEA or a related topic being excluded.

CEAs have traditionally been criticized for lack of a standard methodology, despite their utility in the detection and treatment of various pathologies. Although mammography and magnetic resonance imaging (MRI) are the preferred and cost-effective imaging modalities for breast cancer, other imaging modalities, such as contrast-enhanced mammography and digital breast tomosynthesis, may be more cost-effective in the appropriate clinical context. Different combinations of mammography and MRI screenings for certain breast cancers may also prove to be more cost-effective compared to current mammography/MRI screening schedules.

While CEA has shown potential utility in estimating the costs (per unit of health gained) of different imaging tools, CEA risks ignoring important outcomes not included in the analysis and cannot address if the benefits of the imaging tool exceed its costs, as a CBA would, suggesting the need for combining several economic evaluations for a more complete understanding.

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

Intravenous contrast does not improve detection of nerve lesions or active muscle denervation changes in MR neurography of the common peroneal nerve

OBJECTIVE

To evaluate the effect of intravenous (IV) contrast on sensitivity, specificity, and accuracy of magnetic resonance (MR) neurography of the knee with attention to the common peroneal nerve (CPN) in identifying nerve lesions and active muscle denervation changes.

MATERIALS AND METHODS

A retrospective search for contrast-enhanced MR neurography cases evaluating the CPN at the knee was performed. Patients with electrodiagnostic testing (EDX) within 3 months of imaging were included and those with relevant prior surgery were excluded. Two radiologists independently reviewed non-contrast sequences and then 4 weeks later evaluated non-contrast and contrast sequences. McNemar's tests were performed to detect a difference between non-contrast only and combined non-contrast and contrast sequences in identifying nerve lesions and active muscle denervation changes using EDX as the reference standard.

RESULTS

Forty-four exams in 42 patients (2 bilateral) were included. Twenty-eight cases had common peroneal neuropathy and 29, 21, and 9 cases had active denervation changes in the anterior, lateral, and posterior compartment/proximal muscles respectively on EDX. Sensitivity, specificity, and accuracy of non-contrast versus combined non-contrast and contrast sequences for common peroneal neuropathy were 50.0%, 56.2%, and 52.3% versus 50.0%, 56.2%, and 52.3% for reader 1 and 57.1%, 50.0%, and 54.5% versus 64.3%, 56.2%, and 61.4% for reader 2. Sensitivity, specificity, and accuracy of non-contrast and combined non-contrast and contrast sequences in identifying active denervation changes for anterior, lateral, and posterior compartment muscles were not significantly different. McNemar's tests were all negative.

CONCLUSION

IV contrast does not improve the ability of MR neurography to detect CPN lesions or active muscle denervation changes.

Racial and Ethnic Differences in Emergency Department Diagnostic Imaging at US Children's Hospitals, 2016-2019

IMPORTANCE

Diagnostic imaging is frequently performed as part of the emergency department (ED) evaluation of children. Whether imaging patterns differ by race and ethnicity is unknown.

OBJECTIVE

To evaluate racial and ethnic differences in the performance of common ED imaging studies and to examine patterns across diagnoses.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study evaluated visits by patients younger than 18 years to 44 US children's hospital EDs from January 1, 2016, through December 31, 2019.

EXPOSURES

Non-Hispanic Black and Hispanic compared with non-Hispanic White race/ethnicity.

MAIN OUTCOMES AND MEASURES

The primary outcome was the proportion of visits for each race/ethnicity group with at least 1 diagnostic imaging study, defined as plain radiography, computed tomography, ultrasonography, and magnetic resonance imaging. The major diagnostic categories classification system was used to examine race/ethnicity differences in imaging rates by diagnoses.

RESULTS

A total of 13 087 522 visits by 6 230 911 children and adolescents (mean [SD] age, 5.8 [5.2] years; 52.7% male) occurred during the study period. Diagnostic imaging was performed during 3 689 163 visits (28.2%). Imaging was performed in 33.5% of visits by non-Hispanic White patients compared with 24.1% of visits by non-Hispanic Black patients (odds ratio [OR], 0.60; 95% CI, 0.60-0.60) and 26.1% of visits by Hispanic patients (OR, 0.66; 95% CI, 0.66-0.67). Adjusting for confounders, visits by non-Hispanic Black (adjusted OR, 0.82; 95% CI, 0.82-0.83) and Hispanic (adjusted OR, 0.87; 95% CI, 0.87-0.87) patients were less likely to include any imaging study compared with visits by non-Hispanic White patients. Limiting the analysis to only visits by nonhospitalized patients, the adjusted OR for imaging was 0.79 (95% CI, 0.79-0.80) for visits by non-Hispanic Black patients and 0.84 (95% CI, 0.84-0.85) for visits by Hispanic patients. Results were consistent in analyses stratified by public and private insurance groups and did not materially differ by diagnostic category.

CONCLUSIONS AND RELEVANCE

In this study, non-Hispanic Black and Hispanic children were less likely to receive diagnostic imaging during ED visits compared with non-Hispanic White children. Further investigation is needed to understand and mitigate these potential disparities in health care delivery and to evaluate the effect of these differential imaging patterns on patient outcomes.

Artificial intelligence for brain diseases: A systematic review

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

An Update on Gastroenteropancreatic Neuroendocrine Neoplasms: From Mysteries to Paradigm Shifts

Although neuroendocrine neoplasms (NEN) were once thought to be rare and mostly "benign" diseases, they are now being redefined in light of recently discovered molecular information. NENs constitute a spectrum of variably differentiated neoplasms, ranging from well-differentiated tumors with a protracted course over many years to very aggressive neuroendocrine carcinomas. Although the majority of NEN are non-functional lesions, some of these tumors, do produce a hormonal hypersecretion syndrome. Their reappraisal has led scientist to unveil previously unknown oncogenic pathways and connections that resulted in a new category in the International Classification of Diseases (ICD-11) and a revised version of the World Health Organization Classification (WHO 2018). Complex diseases like NEN require a multidisciplinary approach that includes the perspectives of endocrinologists, medical and surgical oncologists, radiation oncologists, imaging specialists and pathologists. There are currently virtually thousands of ongoing trials evaluating the efficacy and safety of several molecular targeted therapies. The purpose of this review was to critically evaluate recent information regarding the pathogenesis, diagnosis and treatment of NEN.

Toward Alignment in the Reporting of Economic Evaluations of Diagnostic Tests and Biomarkers: The AGREEDT Checklist

Objectives. General frameworks for conducting and reporting health economic evaluations are available but not specific enough to cover the intricacies of the evaluation of diagnostic tests and biomarkers. Such evaluations are typically complex and model-based because tests primarily affect health outcomes indirectly and real-world data on health outcomes are often lacking. Moreover, not all aspects relevant to the evaluation of a diagnostic test may be known and explicitly considered for inclusion in the evaluation, leading to a loss of transparency and replicability. To address this challenge, this study aims to develop a comprehensive reporting checklist. Methods. This study consisted of 3 main steps: 1) the development of an initial checklist based on a scoping review, 2) review and critical appraisal of the initial checklist by 4 independent experts, and 3) development of a final checklist. Each item from the checklist is illustrated using an example from previous research. Results. The scoping review followed by critical review by the 4 experts resulted in a checklist containing 44 items, which ideally should be considered for inclusion in a model-based health economic evaluation. The extent to which these items were included or discussed in the studies identified in the scoping review varied substantially, with 14 items not being mentioned in ≥47 (75%) of the included studies. Conclusions. The reporting checklist developed in this study may contribute to improved transparency and completeness of model-based health economic evaluations of diagnostic tests and biomarkers. Use of this checklist is therefore encouraged to enhance the interpretation, comparability, and—indirectly—the validity of the results of such evaluations.

A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models

INTRODUCTION

The ongoing development of genomic medicine and the use of molecular and imaging markers in personalized medicine (PM) has arguably challenged the field of health economic modeling (HEM). This study aims to provide detailed insights into the current status of HEM in PM, in order to identify if and how modeling methods are used to address the challenges described in literature. Areas covered: A review was performed on studies that simulate health economic outcomes for personalized clinical pathways. Decision tree modeling and Markov modeling were the most observed methods. Not all identified challenges were frequently found, challenges regarding companion diagnostics, diagnostic performance, and evidence gaps were most often found. However, the extent to which challenges were addressed varied considerably between studies. Expert commentary: Challenges for HEM in PM are not yet routinely addressed which may indicate that either (1) their impact is less severe than expected, (2) they are hard to address and therefore not managed appropriately, or (3) HEM in PM is still in an early stage. As evidence on the impact of these challenges is still lacking, we believe that more concrete examples are needed to illustrate the identified challenges and to demonstrate methods to handle them.