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RMD Open Nov 2023Summarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human...
OBJECTIVES
Summarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human physicians.
METHODS
MEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots.
RESULTS
Ten studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist's sensitivity of 0.67-1.00 and specificity of 0.78-1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues.
CONCLUSION
The performance of machine learning algorithms in discriminating sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.
Topics: Humans; Sacroiliitis; Magnetic Resonance Imaging; Spondylarthritis; Sensitivity and Specificity; Machine Learning
PubMed: 37996126
DOI: 10.1136/rmdopen-2023-003783 -
BMC Health Services Research Nov 2023The United Kingdom (UK) government's healthcare policy in the early 1990s paved the way adoption of the skills mix development and implementation of diagnostic...
Assessing the barriers and enablers to the implementation of the diagnostic radiographer musculoskeletal X-ray reporting service within the NHS in England: a systematic literature review.
INTRODUCTION
The United Kingdom (UK) government's healthcare policy in the early 1990s paved the way adoption of the skills mix development and implementation of diagnostic radiographers' X-ray reporting service. Current clinical practice within the public UK healthcare system reflects the same pressures of increased demand in patient imaging and limited capacity of the reporting workforce (radiographers and radiologists) as in the 1990s. This study aimed to identify, define and assess the longitudinal macro, meso, and micro barriers and enablers to the implementation of the diagnostic radiographer musculoskeletal X-ray reporting service in the National Healthcare System (NHS) in England.
METHODS
Multiple independent databases were searched, including PubMed, Ovid MEDLINE; Embase; CINAHL, and Google Scholar, as well as journal databases (Scopus, Wiley), healthcare databases (NHS Evidence Database; Cochrane Library) and grey literature databases (OpenGrey, GreyNet International, and the British Library EthOS depository) and recorded in a PRISMA flow chart. A combination of keywords, Boolean logic, truncation, parentheses and wildcards with inclusion/exclusion criteria and a time frame of 1995-2022 was applied. The literature was assessed against Joanna Briggs Institute's critical appraisal checklists. With meta-aggregation to synthesize each paper, and coded using NVivo, with context grouped into macro, meso, and micro-level sources and categorised into subgroups of enablers and barriers.
RESULTS
The wide and diverse range of data (n = 241 papers) identified barriers and enablers of implementation, which were categorised into measures of macro, meso, and micro levels, and thematic categories of context, culture, environment, and leadership.
CONCLUSION
The literature since 1995 has reframed the debates on implementation of the radiographer reporting role and has been instrumental in shaping clinical practice. There has been clear influence upon both meso (professional body) and macro-level (governmental/health service) policies and guidance, that have shaped change at micro-level NHS Trust organisations. There is evidence of a shift in culturally intrenched legacy perspectives within and between different meso-level professional bodies around skills mix acceptance and role boundaries. This has helped shape capacity building of the reporting workforce. All of which have contributed to conceptual understandings of the skills mix workforce within modern radiology services.
Topics: Humans; State Medicine; X-Rays; England; Delivery of Health Care; United Kingdom
PubMed: 37974199
DOI: 10.1186/s12913-023-10161-y -
European Journal of Radiology Open Dec 2023Burnout among physicians has a prevalence rate exceeding 50%. The radiology department is not immune to the burnout epidemic. Understanding and addressing burnout among... (Review)
Review
RATIONALE AND OBJECTIVES
Burnout among physicians has a prevalence rate exceeding 50%. The radiology department is not immune to the burnout epidemic. Understanding and addressing burnout among radiologists has been a subject of recent interest. Thus, our study aims to systematically review studies reporting the prevalence of burnout in physicians in the radiology department while providing an overview of the factors associated with burnout among radiologists.
MATERIALS AND METHODS
The search was conducted from inception until November 13th, 2022, in PubMed, Embase, Education Resources Information Center, PsycINFO, and psycArticles. Studies reporting the prevalence of burnout or any subdimensions among radiology physicians, including residents, fellows, consultants, and attendings, were included. Data on study characteristics and estimates of burnout syndrome or any of its subdimensions were collected and summarized.
RESULTS
After screening 6379 studies, 23 studies from seven countries were eligible. The number of participants ranged from 26 to 460 (median, 162; interquartile range, 91-264). In all, 18 studies (78.3%) employed a form of the Maslach Burnout Inventory. In comparison, four studies (17.4%) used the Stanford Professional Fulfillment Index, and one study (4.3%) used a single-item measure derived from the Zero Burnout Program survey. Overall burnout prevalence estimates were reported by 14 studies (60.9%) and varied from 33% to 88%. High burnout prevalence estimates were reported by only five studies (21.7%) and ranged from 5% to 62%. Emotional exhaustion and depersonalization prevalence estimates were reported by 16 studies (69.6%) and ranged from 11%-100% and 4%-97%, respectively. Furthermore, 15 studies (65.2%) reported low personal accomplishment prevalence, ranging from 14.7% to 84%. There were at least seven definitions for overall burnout and high burnout among the included studies, and there was high heterogeneity among the cutoff scores used for the burnout subdimensions.
CONCLUSION
Burnout in radiology is increasing globally, with prevalence estimates reaching 88% and 62% for overall and high burnout, respectively. A myriad of factors has been identified as contributing to the increased prevalence. Our data demonstrated significant variability in burnout prevalence estimates among radiologists and major disparities in burnout criteria, instrument tools, and study quality.
PubMed: 37920681
DOI: 10.1016/j.ejro.2023.100530 -
Diagnostics (Basel, Switzerland) Oct 2023: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy for which ultrasound imaging has recently emerged as a valuable diagnostic tool. This... (Review)
Review
: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy for which ultrasound imaging has recently emerged as a valuable diagnostic tool. This meta-analysis aims to investigate the role of ultrasound radiomics in the diagnosis of CTS and compare it with other diagnostic approaches. : We conducted a comprehensive search of electronic databases from inception to September 2023. The included studies were assessed for quality using the Quality Assessment Tool for Diagnostic Accuracy Studies. The primary outcome was the diagnostic performance of ultrasound radiomics compared to radiologist evaluation for diagnosing CTS. : Our meta-analysis included five observational studies comprising 840 participants. In the context of radiologist evaluation, the combined statistics for sensitivity, specificity, and diagnostic odds ratio were 0.78 (95% confidence interval (CI), 0.71 to 0.83), 0.72 (95% CI, 0.59 to 0.81), and 9 (95% CI, 5 to 15), respectively. In contrast, the ultrasound radiomics training mode yielded a combined sensitivity of 0.88 (95% CI, 0.85 to 0.91), a specificity of 0.88 (95% CI, 0.84 to 0.92), and a diagnostic odds ratio of 58 (95% CI, 38 to 87). Similarly, the ultrasound radiomics testing mode demonstrated an aggregated sensitivity of 0.85 (95% CI, 0.78 to 0.89), a specificity of 0.80 (95% CI, 0.73 to 0.85), and a diagnostic odds ratio of 22 (95% CI, 12 to 41). : In contrast to assessments by radiologists, ultrasound radiomics exhibited superior diagnostic performance in detecting CTS. Furthermore, there was minimal variability in the diagnostic accuracy between the training and testing sets of ultrasound radiomics, highlighting its potential as a robust diagnostic tool in CTS.
PubMed: 37892101
DOI: 10.3390/diagnostics13203280 -
Insights Into Imaging Oct 2023The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions.
OBJECTIVES
The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions.
METHODS
MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology.
RESULTS
After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was - 16.7%.
CONCLUSION
Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation.
CLINICAL RELEVANCE STATEMENT
Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation.
KEY POINTS
• Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses. • Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. • Modelling with larger cohorts and real-world evaluation is required before clinical translation.
PubMed: 37782375
DOI: 10.1186/s13244-023-01500-y -
Sensors (Basel, Switzerland) Aug 2023Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively... (Review)
Review
Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)-one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain-is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.
Topics: Humans; Focal Cortical Dysplasia; Magnetic Resonance Imaging; Neuroimaging; Brain; Software
PubMed: 37631608
DOI: 10.3390/s23167072 -
Frontiers in Radiology 2023Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep... (Review)
Review
INTRODUCTION
Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT).
METHOD
The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review.
RESULTS
The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9.
DISCUSSION
Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.
PubMed: 37614529
DOI: 10.3389/fradi.2023.1241651 -
European Journal of Radiology Open Dec 2023Intraductal papillary mucinous neoplasm of the bile ducts (IPMN-B) is a true pre-cancerous lesion, which shares common features with pancreatic IPMN (IPMN-P). While... (Review)
Review
RATIONALE AND OBJECTIVES
Intraductal papillary mucinous neoplasm of the bile ducts (IPMN-B) is a true pre-cancerous lesion, which shares common features with pancreatic IPMN (IPMN-P). While IPMN-P is a well described entity for which guidelines were formulated and revised, IPMN-B is a poorly described entity.We carried out a systematic review to evaluate the existing literature, emphasizing the role of MRI in IPMN-B depiction.
MATERIALS AND METHODS
PubMed database was used to identify original studies and case series that reported MR Imaging features of IPMN-B. The search keywords were "IPMN OR intraductal papillary mucinous neoplasm OR IPNB OR intraductal papillary neoplasm of the bile duct AND Biliary OR biliary cancer OR hepatic cystic lesions". Risk of bias and applicability were evaluated using the QUADAS-2 tool.
RESULTS
884 Records were Identified through database searching. 12 studies satisfied the inclusion criteria, resulting in MR features of 288 patients. All the studies were retrospective. Classic features of IPMN-B are under-described. Few studies note worrisome features, concerning for an underlying malignancy. 50 % of the studies had a high risk of bias and concerns regarding applicability.
CONCLUSIONS
The MRI features of IPMN-B are not well elaborated and need to be further studied. Worrisome features and guidelines regarding reporting the imaging findings should be established and published. Radiologists should be aware of IPMN-B, since malignancy diagnosis in an early stage will yield improved prognosis.
PubMed: 37609049
DOI: 10.1016/j.ejro.2023.100515 -
Sensors (Basel, Switzerland) Jul 2023Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest... (Review)
Review
Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic features in the patient's chest X-ray (CXR). The most common radiographic features seen on CXRs include cavitation, consolidation, masses, pleural effusion, calcification, and nodules. Identifying these CXR features will help physicians in diagnosing a patient. However, identifying these radiographic features for intricate disorders is challenging, and the accuracy depends on the radiologist's experience and level of expertise. So, researchers have proposed deep learning (DL) techniques to detect and mark areas of tuberculosis infection in CXRs. DL models have been proposed in the literature because of their inherent capacity to detect diseases and segment the manifestation regions from medical images. However, fully supervised semantic segmentation requires several pixel-by-pixel labeled images. The annotation of such a large amount of data by trained physicians has some challenges. First, the annotation requires a significant amount of time. Second, the cost of hiring trained physicians is expensive. In addition, the subjectivity of medical data poses a difficulty in having standardized annotation. As a result, there is increasing interest in weak localization techniques. Therefore, in this review, we identify methods employed in the weakly supervised segmentation and localization of radiographic manifestations of pulmonary tuberculosis from chest X-rays. First, we identify the most commonly used public chest X-ray datasets for tuberculosis identification. Following that, we discuss the approaches for weakly localizing tuberculosis radiographic manifestations in chest X-rays. The weakly supervised localization of PTB can highlight the region of the chest X-ray image that contributed the most to the DL model's classification output and help pinpoint the diseased area. Finally, we discuss the limitations and challenges of weakly supervised techniques in localizing TB manifestations regions in chest X-ray images.
Topics: Humans; X-Rays; Radiography, Thoracic; Tuberculosis, Pulmonary; Radiography; Tuberculosis
PubMed: 37571564
DOI: 10.3390/s23156781 -
Diagnostics (Basel, Switzerland) Jul 2023Surgical margin status in radical prostatectomy (RP) specimens is an established predictive indicator for determining biochemical prostate cancer recurrence and disease... (Review)
Review
PURPOSE
Surgical margin status in radical prostatectomy (RP) specimens is an established predictive indicator for determining biochemical prostate cancer recurrence and disease progression. Predicting positive surgical margins (PSMs) is of utmost importance. We sought to perform a meta-analysis evaluating the diagnostic utility of a high clinical tumor stage (≥3) on magnetic resonance imaging (MRI) for predicting PSMs.
METHOD
A systematic search of the PubMed, Embase databases, and Cochrane Library was performed, covering the interval from 1 January 2000 to 31 December 2022, to identify relevant studies. The Quality Assessment of Diagnostic Accuracy Studies 2 method was used to evaluate the studies' quality. A hierarchical summary receiver operating characteristic plot was created depicting sensitivity and specificity data. Analyses of subgroups and meta-regression were used to investigate heterogeneity.
RESULTS
This meta-analysis comprised 13 studies with 3924 individuals in total. The pooled sensitivity and specificity values were 0.40 (95% CI, 0.32-0.49) and 0.75 (95% CI, 0.69-0.80), respectively, with an area under the receiver operating characteristic curve of 0.63 (95% CI, 0.59-0.67). The Higgins I2 statistics indicated moderate heterogeneity in sensitivity (I2 = 75.59%) and substantial heterogeneity in specificity (I2 = 86.77%). Area, prevalence of high Gleason scores (≥7), laparoscopic or robot-assisted techniques, field strength, functional technology, endorectal coil usage, and number of radiologists were significant factors responsible for heterogeneity ( ≤ 0.01).
CONCLUSIONS
T stage on MRI has moderate diagnostic accuracy for predicting PSMs. When determining the treatment modality, clinicians should consider the factors contributing to heterogeneity for this purpose.
PubMed: 37568860
DOI: 10.3390/diagnostics13152497