Spectrum of diagnostic errors in radiology

Misdiagnosis in breast imaging: a statement paper from European Society Breast Imaging (EUSOBI)-Part 1: The role of common errors in radiology in missed breast cancer and implications of misdiagnosis

IMPORTANCE

Misdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the healthcare system as a whole.

OBSERVATIONS

Some of the potential implications of misdiagnosis in breast imaging include delayed diagnosis or false reassurance, which can result in a delay in treatment and potentially a worse prognosis. Misdiagnosis can also lead to unnecessary procedures, which can cause physical discomfort, anxiety, and emotional distress for patients, as well as increased healthcare costs. All these events can erode patient trust in the healthcare system and in individual healthcare providers. This can have negative implications for patient compliance with screening and treatment recommendations, as well as overall health outcomes. Moreover, misdiagnosis can also result in legal consequences for healthcare providers, including medical malpractice lawsuits and disciplinary action by licensing boards.

CONCLUSION AND RELEVANCE

To minimize the risk of misdiagnosis in breast imaging, it is important for healthcare providers to use appropriate imaging techniques and interpret images accurately and consistently. This requires ongoing training and education for radiologists and other healthcare providers, as well as collaboration and communication among healthcare providers to ensure that patients receive appropriate and timely care. If a misdiagnosis does occur, it is important for healthcare providers to communicate with patients and provide appropriate follow-up care to minimize the potential implications of the misdiagnosis. This may include repeat imaging, additional biopsies or other procedures, and referral to specialists for further evaluation and management.

KEY POINTS

Question What factors most contribute to and what implications stem from misdiagnosis in breast imaging? Findings Ongoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication, is paramount. Clinical relevance Misdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system.

Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis

INTRODUCTION

Head and neck cancers are the seventh most common globally, with lymph node metastasis (LNM) being a critical prognostic factor, significantly reducing survival rates. Traditional imaging methods have limitations in accurately diagnosing LNM. This meta-analysis aims to estimate the diagnostic accuracy of Artificial Intelligence (AI) models in detecting LNM in head and neck cancers.

METHODS

A systematic search was performed on four databases, looking for studies reporting the diagnostic accuracy of AI models in detecting LNM in head and neck cancers. Methodological quality was assessed using the METRICS tool and meta-analysis was performed using bivariate model in R environment.

RESULTS

23 articles met the inclusion criteria. Due to the absence of external validation in most studies, all analyses were confined to internal validation sets. The meta-analysis revealed a pooled AUC of 91% for CT-based radiomics, 84% for MRI-based radiomics, and 92% for PET/CT-based radiomics. Sensitivity and specificity were highest for PET/CT-based models. The pooled AUC was 92% for deep learning models and 91% for hand-crafted radiomics models. Models based on lymph node features had a pooled AUC of 92%, while those based on primary tumor features had an AUC of 89%. No significant differences were found between deep learning and hand-crafted radiomics models or between lymph node and primary tumor feature-based models.

CONCLUSION

Radiomics and deep learning models exhibit promising accuracy in diagnosing LNM in head and neck cancers, particularly with PET/CT. Future research should prioritize multicenter studies with external validation to confirm these results and enhance clinical applicability.

Radiological Evaluation of Vascular Structures in Cats Infected with Immature Worms of Dirofilaria immitis

This study aimed to assess thoracic radiographic abnormalities in cats infected with immature stages of Dirofilaria immitis to evaluate the utility of this diagnostic technique during early infection. A total of 123 cats from a hyperendemic area were classified into three groups: asymptomatic cats seronegative to anti-D.-immitis antibodies (Group A), seropositive asymptomatic cats (Group B), and seropositive cats with clinical signs that were at high risk of heartworm-associated respiratory disease (HARD) (Group C). Radiographic measurements and lung parenchymal abnormalities were analyzed and compared across the groups. Significant differences in several parameters, including CrPA/R4, and CdPA/R9 ratios, were observed between healthy and seropositive cats, suggesting early arterial damage even in the absence of adult worms. Other parameters that showed differences between healthy and infected cats were CVC/Ao and CVC/R4 ratios, but not the VHS. Group C exhibited a marked bronchointerstitial pattern, indicating severe parenchymal alterations associated with clinical signs. The study demonstrated that thoracic radiography can detect early vascular and parenchymal changes in feline D. immitis infections, providing valuable information for diagnosing HARD. However, it also highlights the limitations of radiographic techniques, as some seropositive cats displayed no significant abnormalities. The findings underscore the importance of combining radiography with clinical and serological assessments for a more accurate diagnosis of feline heartworm disease.

Errors in Radiology: A Standard Review

Radiological interpretations, while essential, are not infallible and are best understood as expert opinions formed through the evaluation of available evidence. Acknowledging the inherent possibility of error is crucial, as it frames the discussion on improving diagnostic accuracy and patient care. A comprehensive review of error classifications highlights the complexity of diagnostic errors, drawing on recent frameworks to categorize them into perceptual and cognitive errors, among others. This classification underpins an analysis of specific error types, their prevalence, and implications for clinical practice. Additionally, we address the psychological impact of radiological practice, including the effects of mental health and burnout on diagnostic accuracy. The potential of artificial intelligence (AI) in mitigating errors is discussed, alongside ethical and regulatory considerations in its application. This research contributes to the body of knowledge on radiological errors, offering insights into preventive strategies and the integration of AI to enhance diagnostic practices. It underscores the importance of a nuanced understanding of errors in radiology, aiming to foster improvements in patient care and radiological accuracy.

The application of deep learning in abdominal trauma diagnosis by CT imaging

BACKGROUND

Abdominal computed tomography (CT) scan is a crucial imaging modality for creating cross-sectional images of the abdominal area, particularly in cases of abdominal trauma, which is commonly encountered in traumatic injuries. However, interpreting CT images is a challenge, especially in emergency. Therefore, we developed a novel deep learning algorithm-based detection method for the initial screening of abdominal internal organ injuries.

METHODS

We utilized a dataset provided by the Kaggle competition, comprising 3,147 patients, of which 855 were diagnosed with abdominal trauma, accounting for 27.16% of the total patient population. Following image data pre-processing, we employed a 2D semantic segmentation model to segment the images and constructed a 2.5D classification model to assess the probability of injury for each organ. Subsequently, we evaluated the algorithm's performance using 5k-fold cross-validation.

RESULTS

With particularly noteworthy performance in detecting renal injury on abdominal CT scans, we achieved an acceptable accuracy of 0.932 (with a positive predictive value (PPV) of 0.888, negative predictive value (NPV) of 0.943, sensitivity of 0.887, and specificity of 0.944). Furthermore, the accuracy for liver injury detection was 0.873 (with PPV of 0.789, NPV of 0.895, sensitivity of 0.789, and specificity of 0.895), while for spleen injury, it was 0.771 (with PPV of 0.630, NPV of 0.814, sensitivity of 0.626, and specificity of 0.816).

CONCLUSIONS

The deep learning model demonstrated the capability to identify multiple organ injuries simultaneously on CT scans and holds potential for application in preliminary screening and adjunctive diagnosis of trauma cases beyond abdominal injuries.

Artificial intelligence diagnostic model for multi-site fracture X-ray images of extremities based on deep convolutional neural networks

Background

The rapid and accurate diagnosis of fractures is crucial for timely treatment of trauma patients. Deep learning, one of the most widely used forms of artificial intelligence (AI), is now commonly employed in medical imaging for fracture detection. This study aimed to construct a deep learning model using big data to recognize multiple-fracture X-ray images of extremity bones.

Methods

Radiographic imaging data of extremities were retrospectively collected from five hospitals between January 2017 and September 2020. The total number of people finally included was 25,635 and the total number of images included was 26,098. After labeling the lesions, the randomized method used 90% of the data as the training set to develop the fracture detection model, and the remaining 10% was used as the validation set to verify the model. The faster region convolutional neural networks (R-CNN) algorithm was adopted to construct diagnostic models for detection. The Dice coefficient was used to evaluate the image segmentation accuracy. The performances of detection models were evaluated with sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Results

The free-response receiver operating characteristic (FROC) curve value was 0.886 and 0.843 for the detection of single and multiple fractures, respectively. Additionally, the effective identification AUC for all parts was higher than 0.920. Notably, the AUC for wrist fractures reached 0.952. The average accuracy in detecting bone fracture regions in the extremities was 0.865. When analyzing single and multiple lesions at the patient level, the sensitivity was 0.957 for patients with multiple lesions and 0.852 for those with single lesions. In the segmentation task, the training set (the data set used by the machine learning model to train and learn) and the validation set (the data set used to evaluate the performance of the model) reached 0.996 and 0.975, respectively.

Conclusions

The faster R-CNN training algorithm exhibits excellent performance in simultaneously identifying fractures in the hands, feet, wrists, ankles, radius and ulna, and tibia and fibula on X-ray images. It demonstrates high accuracy, low false-negative rates, and controllable false-positive rates. It can serve as a valuable screening tool.

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging

Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.

Association between clinical history in the radiographic request and diagnostic accuracy of thorax radiographs in dogs: A retrospective case-control study

BACKGROUND

The effect of clinical history on the interpretation of radiographs has been widely researched in human medicine. There is, however, no data on this topic in veterinary medicine.

HYPOTHESIS/OBJECTIVES

Diagnostic accuracy would improve when history was supplied.

ANIMALS

Thirty client-owned dogs with abnormal findings on thoracic radiographs and confirmation of the disease, and 30 healthy client-owned controls were drawn retrospectively.

METHODS

Retrospective case-control study. Sixty radiographic studies of the thorax were randomized and interpreted by 6 radiologists; first, with no access to the clinical information; and a second time with access to all pertinent clinical information and signalment.

RESULTS

A significant increase in diagnostic accuracy was noted when clinical information was provided (64.4% without and 75.2% with clinical information; P = .002). There was no significant difference in agreement between radiologists when comparing no clinical information and with clinical information (Kappa 0.313 and 0.300, respectively).

CONCLUSIONS AND CLINICAL IMPORTANCE

The addition of pertinent clinical information to the radiographic request significantly improves the diagnostic accuracy of thorax radiographs of dogs and is recommended as standard practice.

Liability, risks, and recommendations for ultrasound use in the diagnosis of obstetrics diseases

This literature review will summarize the liability issues, risks, and ultrasound recommendations for diagnosing obstetrics diseases. One liability issue is related to misdiagnosis or failure to detect abnormalities during an ultrasound examination. Ultrasound images can be subjective interpretations, and errors may occur due to factors such as operator skill, equipment limitations, or fetal positioning. Another liability concern is related to the potential adverse effects of ultrasound exposure on both the mother and fetus. While extensive research has shown that diagnostic ultrasound is generally safe when used appropriately, there are still uncertainties regarding long-term effects. Some studies suggest a possible association between prolonged or excessive exposure to ultrasound waves and adverse outcomes such as low birth weight, developmental delays, or hearing impairment. Additionally, obtaining informed consent from patients is crucial in mitigating liability risks. Patients should be informed about the purpose of the ultrasound examination, its benefits, limitations, potential risks (even if minimal), and any alternative diagnostic options available. This ensures that patients know the procedure and can make informed decisions about their healthcare. Proper documentation helps establish a clear record of the care provided and can serve as evidence in any legal disputes.

Artificial Intelligence Improves Detection of Supplemental Screening Ultrasound-detected Breast Cancers in Mammography

Despite recent advances in artificial intelligence (AI) software with improved performance in mammography screening for breast cancer, insufficient data are available on its performance in detecting cancers that were initially missed on mammography. In this study, we aimed to determine whether AI software-aided mammography could provide additional value in identifying cancers detected through supplemental screening ultrasound. We searched our database from 2017 to 2018 and included 238 asymptomatic patients (median age, 50 years; interquartile range, 45-57 years) diagnosed with breast cancer using supplemental ultrasound. Two unblinded radiologists retrospectively reviewed the mammograms using commercially available AI software and identified the reasons for missed detection. Clinicopathological characteristics of AI-detected and AI-undetected cancers were compared using univariate and multivariate logistic regression analyses. A total of 253 cancers were detected in 238 patients using ultrasound. In an unblinded review, the AI software failed to detect 187 of the 253 (73.9%) mammography cases with negative findings in retrospective observations. The AI software detected 66 cancers (26.1%), of which 42 (63.6%) exhibited indiscernible findings obscured by overlapping dense breast tissues, even with the knowledge of magnetic resonance imaging and post-wire localization mammography. The remaining 24 cases (36.4%) were considered interpretive errors by the radiologists. Invasive tumor size was associated with AI detection after multivariable analysis (odds ratio, 2.2; 95% confidence intervals, 1.5-3.3; p < 0.001). In the control group of 160 women without cancer, the AI software identified 19 false positives (11.9%, 19/160). Although most ultrasound-detected cancers were not detected on mammography with the use of AI, the software proved valuable in identifying breast cancers with indiscernible abnormalities or those that clinicians may have overlooked.

Temporal uncertainty in disease diagnosis

There is a profound paradox in modern medical knowledge production: The more we know, the more we know that we (still) do not know. Nowhere is this more visible than in diagnostics and early detection of disease. As we identify ever more markers, predictors, precursors, and risk factors of disease ever earlier, we realize that we need knowledge about whether they develop into something experienced by the person and threatening to the person's health. This study investigates how advancements in science and technology alter one type of uncertainty, i.e., temporal uncertainty of disease diagnosis. As diagnosis is related to anamnesis and prognosis it identifies how uncertainties in all these fields are interconnected. In particular, the study finds that uncertainty in disease diagnosis has become more subject to prognostic uncertainty because diagnosis is more connected to technologically detected indicators and less closely connected to manifest and experienced disease. These temporal uncertainties pose basic epistemological and ethical challenges as they can result in overdiagnosis, overtreatment, unnecessary anxiety and fear, useless and even harmful diagnostic odysseys, as well as vast opportunity costs. The point is not to stop our quest for knowledge about disease but to encourage real diagnostic improvements that help more people in ever better manner as early as possible. To do so, we need to pay careful attention to specific types of temporal uncertainty in modern diagnostics.

Detecting Bone Marrow Edema of the Extremities on Spectral Computed Tomography Using a Three-Material Decomposition

BACKGROUND

Detecting bone marrow edema (BME) as a sign of acute fractures is challenging on conventional computed tomography (CT). This study evaluated the diagnostic performance of a three-material decomposition (TMD) approach for detecting traumatic BME of the extremities on spectral computed tomography (SCT).

METHODS

This retrospective diagnostic study included 81 bone compartments with and 80 without BME. A TMD application to visualize BME was developed in collaboration with Philips Healthcare. The following bone compartments were included: distal radius, proximal femur, proximal tibia, distal tibia and fibula, and long bone diaphysis. Two blinded radiologists reviewed each case independently in random order for the presence or absence of BME.

RESULTS

The interrater reliability was 0.84 (p < 0.001). The different bone compartments showed sensitivities of 86.7% to 93.8%, specificities of 84.2% to 94.1%, positive predictive values of 82.4% to 94.7%, negative predictive values of 87.5% to 93.3%, and area under the curve (AUC) values of 85.7% to 93.1%. The distal radius showed the highest sensitivity and the proximal femur showed the lowest sensitivity, while the proximal femur presented the highest specificity and the distal tibia presented the lowest specificity.

CONCLUSIONS

Our TMD approach provides high diagnostic performance for detecting BME of the extremities. Therefore, this approach could be used routinely in the emergency setting.

Pediatric hand fractures detection on radiographs: do localization cues improve diagnostic performance?

OBJECTIVE

To compare the diagnostic accuracy and interpretation time for detection of pediatric fractures on hand radiographs with and without localization cues.

MATERIALS AND METHODS

Consecutive children, who underwent radiographic examinations after injury, over 2 years (2019-2021) and with > 2 weeks of follow-up to confirm the presence or absence of a fracture, were included. Four readers, blinded to history and diagnosis, retrospectively reviewed all images twice, without and with cue, at least 1 week apart and after randomization, to determine the presence or absence of a fracture, and if present, anatomic location and diagnostic confidence were recorded. Interpretation time for each study was also recorded and averaged across readers. Inter-reader agreement was calculated using Fleiss' kappa. Diagnostic accuracy and interpretation time were compared between examinations using sensitivity, specificity, and Mann-Whitney U correlation.

RESULTS

Study group included 92 children (61 boys, 31 girls; 10.8 ± 3.4 years) with and 40 (31 boys, 9 girls; 10.9 ± 3.7 years) without fractures. Cue improved inter-reader agreement (κ = 0.47 to 0.62). While the specificity decreased (63 to 62%), sensitivity (75 to 78%), diagnostic accuracy (71 to 73%), and confidence improved (78 to 87%, p < 0.01), and interpretation time (median: 40 to 22 s, p < 0.001) reduced with examinations with localization cue. Specifically, examinations with fracture and cue had the shortest interpretation time (median: 16 s), whereas examinations without fracture and without cue had the longest interpretation time (median: 48 s).

CONCLUSION

Localization cues increased inter-reader agreement and diagnostic confidence, reduced interpretation time in the detection of fractures on pediatric hand radiographs, while maintaining diagnostic accuracy.

Factors affecting interpretation of dental radiographs

OBJECTIVES

To identify the factors influencing errors in the interpretation of dental radiographs.

METHODS

A protocol was registered on Prospero. All studies published until May 2022 were included in this review. The search of the electronic databases spanned Ovid Medline, PubMed, EMBASE, Web of Science and Scopus. The quality of the studies was assessed using the MMAT tool. Due to the heterogeneity of the included studies, a meta-analysis was not conducted.

RESULTS

The search yielded 858 articles, of which eight papers met the inclusion and exclusion criteria and were included in the systematic review. These studies assessed the factors influencing the accuracy of the interpretation of dental radiographs. Six factors were identified as being significant that affected the occurrence of interpretation errors. These include clinical experience, clinical knowledge, and technical ability, case complexity, time pressure, location and duration of dental education and training and cognitive load.

CONCLUSIONS

The occurrence of interpretation errors has not been widely investigated in dentistry. The factors identified in this review are interlinked. Further studies are needed to better understand the extent of the occurrence of interpretive errors and their impact on the practice of dentistry.

Assessment of artificial intelligence-aided reading in the detection of nasal bone fractures

BACKGROUND: Artificial intelligence (AI) technology is a promising diagnostic adjunct in fracture detection. However, few studies describe the improvement of clinicians’ diagnostic accuracy for nasal bone fractures with the aid of AI technology. OBJECTIVE: This study aims to determine the value of the AI model in improving the diagnostic accuracy for nasal bone fractures compared with manual reading. METHODS: A total of 252 consecutive patients who had undergone facial computed tomography (CT) between January 2020 and January 2021 were enrolled in this study. The presence or absence of a nasal bone fracture was determined by two experienced radiologists. An AI algorithm based on the deep-learning algorithm was engineered, trained and validated to detect fractures on CT images. Twenty readers with various experience were invited to read CT images with or without AI. The accuracy, sensitivity and specificity with the aid of the AI model were calculated by the readers. RESULTS: The deep-learning AI model had 84.78% sensitivity, 86.67% specificity, 0.857 area under the curve (AUC) and a 0.714 Youden index in identifying nasal bone fractures. For all readers, regardless of experience, AI-aided reading had higher sensitivity ([94.00 ± 3.17]% vs [83.52 ± 10.16]%, P< 0.001), specificity ([89.75 ± 6.15]% vs [77.55 ± 11.38]%, P< 0.001) and AUC (0.92 ± 0.04 vs 0.81 ± 0.10, P< 0.001) compared with reading without AI. With the aid of AI, the sensitivity, specificity and AUC were significantly improved in readers with 1–5 years or 6–10 years of experience (all P< 0.05, Table 4). For readers with 11–15 years of experience, no evidence suggested that AI could improve sensitivity and AUC (P= 0.124 and 0.152, respectively). CONCLUSION: The AI model might aid less experienced physicians and radiologists in improving their diagnostic performance for the localisation of nasal bone fractures on CT images.

After-hour trauma-radiograph interpretation in the emergency centre of a District Hospital

Introduction

Plain radiographs remain a first-line trauma investigation. Most trauma radiographs worldwide are reported by junior doctors. This study assesses the accuracy of after-hour acute trauma radiograph reporting by emergency centre (EC) doctors in an African district hospital.

Methods

An institutional review board approved retrospective descriptive study over two consecutive weekends in February 2020. The radiologist report on the admission radiographs of adult trauma patients was compared with the initial EC interpretation. The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for EC interpretation were calculated with 95% confidence intervals (95%CI). The association between reporting accuracy and anatomical region, mechanism of injury, time of investigation, and the number of abnormalities per radiograph was assessed.

Results

140 radiographs were included, of which 49 (35%) were abnormal. EC doctors recorded (95%CI) 77% (69-84%) accuracy, 38% (25-54%) sensitivity, 97% (91-99%) specificity, 86% (65-95%) PPV and 76% (71-80%) NPV. Performance was associated with the anatomical region (p=0.02), mechanism of injury (p=<0.01) time of day (p=0.04) and the number of abnormalities on the film (p=<0.01). The highest sensitivity was achieved in reports of the appendicular skeleton (42%) and in the setting of simple blunt trauma (62%). Overall accuracy was in line with the range (44%-99%) reported in the international literature.

Discussion

Accurate reporting of acute trauma radiographs is challenging. Key factors impact performance. Further training of junior doctors in this area of clinical practice is recommended. Future work should focus on assessing the impact of such training on reporting performance.

Call to Action: Creating Resources for Radiology Technologists to Capture Higher Quality Portable Chest X-rays

Background Patient rotation, foreign body overlying anatomy, and anatomy out of field of view can have detrimental impacts on the diagnostic quality of portable chest x-rays (PCXRs), especially as the number of PCXR imaging increases due to the coronavirus disease 2019 (COVID-19) pandemic. Although preventable, these "quality failures" are common and may lead to interpretative and diagnostic errors for the radiologist. Aims In this study, we present a baseline quality failure rate of PCXR imaging as observed at our institution. We also conduct a focus group highlighting the key issues that lead to the problematic images and discuss potential interventions targeting technologists that can be implemented to address imaging quality failure rate. Materials and methods A total of 500 PCXRs for adult patients admitted to a large university hospital between July 12, 2021, and July 25, 2021, were obtained for evaluation of quality. The PCXRs were evaluated by radiology residents for failures in technical image quality. The images were categorized into various metrics including the degree of rotation and obstruction of anatomical structures. After collecting the data, a focus group involving six managers of the technologist department at our university hospital was conducted to further illuminate the key barriers to quality PCXRs faced at our institution.. Results  Out of the 500 PCXRs evaluated, 231 were problematic (46.2%). 43.5% of the problematic films with a repeat PCXR within one week showed that there was a technical problem impacting the ability to detect pathology. Most problematic films also occurred during the night shift (48%). Key issues that lead to poor image quality included improper patient positioning, foreign objects covering anatomy, and variances in technologists' training. Three interventions were proposed to optimize technologist performance that can lower quality failure rates of PCXRs. These include a longitudinal educational curriculum involving didactic sessions, adding nursing support to assist technologists, and adding an extra layer of verification by internal medicine residents before sending the films to the radiologist. The rationale for these interventions is discussed in detail so that a modified version can be implemented in other hospital systems.  Conclusion This study illustrates the high baseline error rate in image quality of PCXRs at our institution and demonstrates the need to improve on image quality. Poor image quality negatively impacts the interpretive accuracy of radiologists and therefore leads to wrong diagnoses. Increasing educational resources and support for technologists can lead to higher image quality and radiologist accuracy.

Influence of Prior Imaging Information on Diagnostic Accuracy for Focal Skeletal Processes—A Retrospective Analysis of the Consistency between Biopsy-Verified Imaging Diagnoses

Introduction: Comparing imaging examinations with those previously obtained is considered mandatory in imaging guidelines. To our knowledge, no studies are available on neither the influence, nor the sequence, of prior imaging and reports on diagnostic accuracy using biopsy as the reference standard. Such data are important to minimize diagnostic errors and to improve the preparation of diagnostic imaging guidelines. The aim of our study was to provide such data. Materials and methods: A retrospective cohort of 216 consecutive skeletal biopsies from patients with at least 2 different imaging modalities (X-ray, CT and MRI) performed within 6 months of biopsy was identified. The diagnostic accuracy of the individual imaging modality was assessed. Finally, the possible influence of the sequence of imaging modalities was investigated. Results: No significant difference in the accuracy of the imaging modalities was shown, being preceded by another imaging modality or not. However, the sequence analyses indicate sequential biases, particularly if MRI was the first imaging modality. Conclusion: The sequence of the imaging modalities seems to influence the diagnostic accuracy against a pathology reference standard. Further studies are needed to establish evidence-based guidelines for the strategy of using previous imaging and reports to improve diagnostic accuracy.

The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

This paper proposes a development of automatic rib sequence labeling systems on chest computed tomography (CT) images with two suggested methods and three-dimensional (3D) region growing. In clinical practice, radiologists usually define anatomical terms of location depending on the rib’s number. Thus, with the manual process of labeling 12 pairs of ribs and counting their sequence, it is necessary to refer to the annotations every time the radiologists read chest CT. However, the process is tedious, repetitive, and time-consuming as the demand for chest CT-based medical readings has increased. To handle the task efficiently, we proposed an automatic rib sequence labeling system and implemented comparison analysis on two methods. With 50 collected chest CT images, we implemented intensity-based image processing (IIP) and a convolutional neural network (CNN) for rib segmentation on this system. Additionally, three-dimensional (3D) region growing was used to classify each rib’s label and put in a sequence label. The IIP-based method reported a 92.0% and the CNN-based method reported a 98.0% success rate, which is the rate of labeling appropriate rib sequences over whole pairs (1st to 12th) for all slices. We hope for the applicability thereof in clinical diagnostic environments by this method-efficient automatic rib sequence labeling system.

Errors in MDCT diagnosis of acute mesenteric ischemia

The causes of diagnostic errors during daily medical practice can be several, mainly attributable to perceptual, interpretive and communication factors. The eventuality of radiological error is much more amplified in the emergency setting where a high number of complex multidetector-row computed tomography (MDCT) images must be evaluated quickly and critical time decisions need to be taken. In particular, in this context, the diagnosis of vascular intestinal diseases represents a crucial and difficult challenge in case of acute abdominal pain given the importance of being able to identify patient with high suspicious for intestinal ischemia and for a specific patient to judge if his ischemia is reversible or irreversible. Awareness of potential biases which can lead to diagnostic errors together with an extensive knowledge of the imaging features of these pathologies can lead to promptly recognize them with fewer mistakes, improving patients' outcome. This article reviews the MDCT findings of acute intestinal ischemia and acute colonic ischemia and analyzes the main types of diagnostic errors, underlining the importance of being familiarized with them to avoid misdiagnosis.

Development of a mobile training app to assist radiographers’ diagnostic assessments

The current study reduced the time lag between performing a diagnostic assessment and identifying a critical finding in CT and MRI exams through improving radiographers’ abilities to identify those critical findings. Radiographers’ diagnostic assessments in CT and MRI exams were used to develop a mobile training application with the aim to improve radiographers’ awareness of critical findings. The current research used data analytics to examine radiographers’ interpretation of imaging studies from a privately owned medical group in Israel. During the project, the radiographers’ ability to identify critical findings improved. Implementation of the mobile training program yielded positive results where the knowledge gap was reduced and time to identify critical cases was decreased. Specifically, this study showed that radiographers can be trained in ways that enhance their involvement with radiologists to provide high quality services and improve treatment Ultimately, this gives patients higher quality of care and safer treatment.

Clinical Value of Surveillance 18F-fluorodeoxyglucose PET/CT for Detecting Unsuspected Recurrence or Second Primary Cancer in Non-Small Cell Lung Cancer after Curative Therapy

Simple Summary

Non-small cell lung cancer (NSCLC) patients are at considerable risk of recurrence or second primary cancer (SPC) after curative therapy. The utility of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) surveillance to detect recurrent lesions in NSCLC patients without suspicion of recurrence has not been established. The aim of our retrospective study was to evaluate the diagnostic value of surveillance FDG PET/CT for detecting clinically unsuspected recurrence or SPC in patients with NSCLC after curative therapy. In a cohort of 2684 NSCLC patients after curative therapy, surveillance FDG PET/CT showed good diagnostic efficacy for detecting clinically unexpected recurrence or SPC. Furthermore, the diagnostic performance was improved in subgroups of patients with advanced stage prior to curative therapy, PET/CT scans performed within 3 years after curative-intent therapy, and curative surgery. Surveillance PET/CT can be more useful when performed soon after therapy in curative surgery recipients and those with an advanced disease stage considering its diagnostic efficacy and yield.

Abstract

We evaluated the diagnostic value of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT surveillance for detecting clinically unsuspected recurrence or second primary cancer (SPC) in patients with non-small cell lung cancer (NSCLC) after curative therapy. A total of 4478 surveillance FDG PET/CT scans from 2864 NSCLC patients without suspicion of recurrence after curative therapy were reviewed retrospectively. In 274 of 2864 (9.6%) patients, recurrent NSCLC or SPC was found by surveillance PET/CT during clinical follow-up. Surveillance PET/CT scans showed sensitivity of 98.9% (274/277), specificity of 98.1% (4122/4201), accuracy of 98.2% (4396/4478), positive predictive value (PPV) of 77.6% (274/353), and negative predictive value of 99.9% (4122/4125). The specificity and accuracy in the curative surgery group were significantly higher than those in the curative radiotherapy group. PPV was significantly improved in subgroups of patients with advanced stage prior to curative therapy, PET/CT scans performed within 3 years after curative-intent therapy, and curative surgery. FDG PET/CT surveillance showed good diagnostic efficacy for detecting clinically unexpected recurrence or SPC in NSCLC patients after curative therapy. It can be more useful when performed soon after therapy in curative surgery recipients and those with an advanced disease stage considering its diagnostic efficacy and yield.

A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures

This paper reported a study on the 3-dimensional deep-learning-based automatic diagnosis of nasal fractures. (1) Background: The nasal bone is the most protuberant feature of the face; therefore, it is highly vulnerable to facial trauma and its fractures are known as the most common facial fractures worldwide. In addition, its adhesion causes rapid deformation, so a clear diagnosis is needed early after fracture onset. (2) Methods: The collected computed tomography images were reconstructed to isotropic voxel data including the whole region of the nasal bone, which are represented in a fixed cubic volume. The configured 3-dimensional input data were then automatically classified by the deep learning of residual neural networks (3D-ResNet34 and ResNet50) with the spatial context information using a single network, whose performance was evaluated by 5-fold cross-validation. (3) Results: The classification of nasal fractures with simple 3D-ResNet34 and ResNet50 networks achieved areas under the receiver operating characteristic curve of 94.5% and 93.4% for binary classification, respectively, both indicating unprecedented high performance in the task. (4) Conclusions: In this paper, it is presented the possibility of automatic nasal bone fracture diagnosis using a 3-dimensional Resnet-based single classification network and it will improve the diagnostic environment with future research.

Radiology Reporting Errors: Learning from Report Addenda

Background The addition of new information to a completed radiology report in the form of an "addendum" conveys a variety of information, ranging from less significant typographical errors to serious omissions and misinterpretations. Understanding the reasons for errors and their clinical implications will lead to better clinical governance and radiology practice. Aims This article assesses the common reasons which lead to addenda generation to completed reports and their clinical implications. Subjects and Methods Retrospective study was conducted by reviewing addenda to computed tomography (CT), ultrasound, and magnetic resonance imaging reports between January 2018 to June 2018, to note the frequency and classification of report addenda. Results Rate of addenda generation was 1.1% ( n = 1,076) among the 97,003 approved cross-sectional radiology reports. Errors contributed to 71.2% ( n = 767) of addenda, most commonly communication (29.3%, n = 316) and observational errors (20.8%, n = 224), and 28.7% were nonerrors aimed at providing additional clinically relevant information. Majority of the addenda (82.3%, n = 886) did not have a significant clinical impact. CT and ultrasound reports accounted for 36.9% ( n = 398) and 35.2% ( n = 379) share, respectively. A time gap of 1 to 7 days was noted for 46.8% ( n = 504) addenda and 37.6% ( n = 405) were issued in less than a day. Radiologists with more than 6-year experience created majority (1.5%, n = 456) of addenda. Those which were added to reports generated during emergency hours contributed to 23.2% ( n = 250) of the addenda. Conclusion The study has identified the prevalence of report addenda in a radiology practice involving picture archiving and communication system in a tertiary care center in India. The etiology included both errors and non-errors. Results of this audit were used to generate a checklist and put protocols that will help decrease serious radiology misses and common errors.

History-taking revisited: Simple techniques to foster patient collaboration, improve data attainment, and establish trust with the patient

The relevance and importance of the medical interview has been challenged with improved imaging technologies, web-based medicine, and use of artificial intelligence. The medical interview has three goals: Acquiring accurate medical data about the patient and the etiology of symptoms and signs, learning about the patient's personality, culture, and beliefs, and creating and building trust with the patient. Reduced human resources in the medical system and increased crowding in the interview setting, such as the emergency room and outpatient clinics, have strengthened the need for high quality and efficient interviews that fulfils the three goals of the interview. This manuscript proposes a structured six methods that contribute to the quality and efficiency of the medical interview with special focus on learning about the patients' life and creating trust with him.

Automated Pathology Detection and Patient Triage in Routinely Acquired Head Computed Tomography Scans

OBJECTIVES

Anomaly detection systems can potentially uncover the entire spectrum of pathologies through deviations from a learned norm, meaningfully supporting the radiologist's workflow. We aim to report on the utility of a weakly supervised machine learning (ML) tool to detect pathologies in head computed tomography (CT) and adequately triage patients in an unselected patient cohort.

MATERIALS AND METHODS

All patients having undergone a head CT at a tertiary care hospital in March 2020 were eligible for retrospective analysis. Only the first scan of each patient was included. Anomaly detection was performed using a weakly supervised ML technique. Anomalous findings were displayed on voxel-level and pooled to an anomaly score ranging from 0 to 1. Thresholds for this score classified patients into the 3 classes: "normal," "pathological," or "inconclusive." Expert-validated radiological reports with multiclass pathology labels were considered as ground truth. Test assessment was performed with receiver operator characteristics analysis; inconclusive results were pooled to "pathological" predictions for accuracy measurements. External validity was tested in a publicly available external data set (CQ500).

RESULTS

During the investigation period, 297 patients were referred for head CT of which 248 could be included. Definite ratings into normal/pathological were feasible in 167 patients (67.3%); 81 scans (32.7%) remained inconclusive. The area under the curve to differentiate normal from pathological scans was 0.95 (95% confidence interval, 0.92-0.98) for the study data set and 0.87 (95% confidence interval, 0.81-0.94) in external validation. The negative predictive value to exclude pathology if a scan was classified as "normal" was 100% (25/25), and the positive predictive value was 97.6% (137/141). Sensitivity and specificity were 100% and 86%, respectively. In patients with inconclusive ratings, pathologies were found in 26 (63%) of 41 cases.

CONCLUSIONS

Our study provides the first clinical evaluation of a weakly supervised anomaly detection system for brain imaging. In an unselected, consecutive patient cohort, definite classification into normal/diseased was feasible in approximately two thirds of scans, going along with an excellent diagnostic accuracy and perfect negative predictive value for excluding pathology. Moreover, anomaly heat maps provide important guidance toward pathology interpretation, also in cases with inconclusive ratings.

Radiologist errors by modality, anatomic region, and pathology for 1.6 million exams: what we have learned

PURPOSE

To evaluate the feasibility of adding pathology to recent radiologist error characterization schemes of modality and anatomic region and the potential of this data to more specifically inform peer review and peer learning.

METHODS

Quality assurance data originating from 349 radiologists in a national teleradiology practice were collected for 2019. Interpretive errors were simply categorized as major or minor. Reporting or communication errors were classified as administrative errors. Interpretive errors were then divided by modality, anatomic region and placed into one of 64 pathologic categories.

RESULTS

Out of 1,628,464 studies, the discrepancy rate was 0.5% (8181/1,634,201). The 8181 total errors consisted of 2992 major errors (0.18%) and 5189 minor errors (0.32%). Precisely, 3.1% (257/8181) of total errors were administrative. Of major interpretive errors, 75.5% occurred on CT, with CT abdomen and pelvis accounting for 40.4%. The most common pathologic discrepancy for all exams was in the category of mass, nodule, or adenopathy (1583/8181), the majority of which were minor (1315/1583). The most common pathologic discrepancy for the 2937 major interpretive errors was fracture or dislocation (27%; 793/2937), followed by bleed (10.7%; 315/2937).

CONCLUSION

The addition of error-related pathology to peer review is both feasible and practical and provides a more detailed guide to targeted individual and practice-wide peer learning quality improvement efforts. Future research is needed to determine if there are measurable improvements in detection or interpretation of specific pathologies following error feedback and educational interventions.

Comparison of nonenhanced computed tomography and ultrasonography for detection of ureteral calculi in cats: A prospective study

BACKGROUND

Radiographs and ultrasound (US) are the primary imaging modalities used to assess ureteral calculi in cats. Reports describing the use of nonenhanced computed tomography (CT) are scarce.

HYPOTHESIS/OBJECTIVES

To compare US and nonenhanced CT for detection, number and localization of ureteral calculi in cats.

ANIMALS

Fifty-one cats with at least 1 ureteral calculus, and 101 ureters.

METHODS

Prospective case series. All cats underwent an US followed by a nonenhanced CT. Cats were included in the study if at least 1 ureteral calculus was diagnosed on either modality. Number of calculi and their localization (proximal, middle, and distal) were recorded on both modalities. Pelvic dilatation and maximal ureteral diameter were recorded with US.

RESULTS

More calculi were detected by nonenhanced CT (126) compared to US (90), regardless of localization (P < .001). More ureters were affected on nonenhanced CT (70) compared to US (57; P < .001). The number of calculi detected was significantly different between US and nonenhanced CT in the proximal (P = .02) and distal ureteral region (P < .001). Bilateral calculi were more frequent with nonenhanced CT (19 cats) compared to US (9 cats; P < .001). A pelvic size superior to 5 mm and a maximal ureteral diameter value superior to 3 mm were always associated with ureteral calculi.

CONCLUSIONS AND CLINICAL IMPORTANCE

Computed tomography is an emerging imaging modality in cats with a suspected ureteral obstruction. Combination of CT and US can be beneficial for case management.

Diagnostic Errors in Neuroradiology: A Message to Emergency Radiologists and Trainees

Diagnostic errors in neuroradiology are inevitable, yet potentially avoidable. Through extensive literature search, we present an up-to-date review of the psychology of human decision making and how such complex process can lead to radiologic errors. Our focus is on neuroradiology, so we augmented our review with multiple explanatory figures to show how different errors can reflect on real-life clinical practice. We propose a new thematic categorization of perceptual and cognitive biases in this article to simplify message delivery to our target audience: emergency/general radiologists and trainees. Additionally, we highlight individual and organizational remedy strategies to decrease error rate and potential harm.

Dual Energy X-ray Absorptiometry Reports Fail to Adhere to International Guidelines

INTRODUCTION

Bone mineral density, measured by dual X-ray absorptiometry (DXA), is the gold standard for diagnosis of osteoporosis. The utility of DXA relies on the accuracy of scan acquisition, interpretation of data, and the adequacy of reports. The International Society for Clinical Densitometry (ISCD) has published guidelines regarding minimum reporting guidelines. This study assessed whether DXA reports for patients receiving care at an academic teaching hospital adhere to these reporting standards, and determine whether differences exist depending on patient factors and the imaging service.

METHODS

Patients aged ≥18 years, receiving care at specialist outpatient clinics between January 1, 2018 and December 31, 2019, with a DXA report available, were eligible for inclusion. DXA reports were manually reviewed for adherence to ISCD guidelines, with each criterion scored as one point, giving a total score of 14 for baseline DXA scans and 18 for repeat DXA scans. The score was then converted to a percentage.

RESULTS

Of 459 DXA scans included, 214 were performed internally at our hospital and 245 performed at 23 external imaging services. Mean (SD) patient age was 60 (16.3) years, and 75.8% were female. The overall median (IQR) report score was 57.1% (42.9, 82.4). ISCD criteria with the lowest scores were recommendation and timing of future DXA scans (included in 1.1% of reports) and investigation for secondary causes of osteoporosis (included in 1.2% of reports). Reports performed internally had significantly higher scores than those performed externally, after adjusting for age, sex, indication, and type of scan (incidence rate ratio 1.83, 95% confidence interval 1.77, 1.89). Baseline DXA reports had slightly higher scores than repeat DXA scans, and, among external imaging services, rural services had higher scores than metropolitan services.

CONCLUSION

This study, the largest comprehensive evaluation of DXA reports, highlights significant deficiencies and variation in report standards between imaging services. This has potential implications for osteoporosis diagnosis and management.

Investigating errors in medical imaging: medical malpractice cases in Finland

Objective

The objectives of the study were to survey patient injury claims concerning medical imaging in Finland in 1991–2017, and to investigate the nature of the incidents, the number of claims, the reasons for the claims, and the decisions made concerning the claims.

Materials and methods

The research material consisted of patient claims concerning imaging, sent to the Finnish Patient Insurance Centre (PVK). The data contained information on injury dates, the examination code, the decision code, the description of the injury, and the medical grounds for decisions.

Results

The number of claims included in the study was 1054, and the average number per year was 87. The most common cause was delayed diagnosis (404 claims, 38.3%). Most of the claims concerned mammography (314, 29.8%), radiography (170, 16.1%), and MRI (162, 15.4%). According to the decisions made by the PVK, there were no delays in 54.6% of the examinations for which claims were made. About 30% of all patient claims received compensation, the most typical reason being medical malpractice (27.7%), followed by excessive injuries and injuries caused by infections, accidents and equipment (2.7%).

Conclusion

Patient injury in imaging examinations and interventions cannot be completely prevented. However, injury data are an important source of information for health care. By analysing claims, we can prevent harm, increase the quality of care, and improve patient safety in medical imaging.

Direct Access and Skill Mix Can Reduce Telephone Interruptions and Imaging Wait Times: Improving Radiology Service Effectiveness, Safety and Sustainability

Unnecessary telephone calls to reporting radiologists impede organizations' workflow and may be associated with a higher chance of errors in reports. We conducted a prospective study in two cycles, which identified vetting plain CT heads as the most common reason for these calls and vetting CT urinary tracts (KUB) was also frequent. Clear vetting and protocolling guidelines exist for both of these scans, which do not routinely require discussion with a radiologist. Therefore, our approach was to create new flow diagrams to allow radiographers to directly accept routine requests for plain CT head and CT KUB scans in- and out-of-hours. After this intervention, incoming calls to radiology for vetting CT heads decreased by 30% and for vetting CT KUBs by 100%. The average wait time between CT head request and scan completion was reduced by 40%. The number of CT head and CT KUB scans performed remained stable. In future, maximizing the benefit of direct access in-patient imaging pathways will rely on effective and sustained communication of the protocols to the junior clinical staff rotating through the organization, as they were responsible for requesting the vast majority of tests.

Great expectations: minor differences in initial instructions have a major impact on visual search in the absence of feedback

Professions such as radiology and aviation security screening that rely on visual search-the act of looking for targets among distractors-often cannot provide operators immediate feedback, which can create situations where performance may be largely driven by the searchers' own expectations. For example, if searchers do not expect relatively hard-to-spot targets to be present in a given search, they may find easy-to-spot targets but systematically quit searching before finding more difficult ones. Without feedback, searchers can create self-fulfilling prophecies where they incorrectly reinforce initial biases (e.g., first assuming and then, perhaps wrongly, concluding hard-to-spot targets are rare). In the current study, two groups of searchers completed an identical visual search task but with just a single difference in their initial task instructions before the experiment started; those in the "high-expectation" condition were told that each trial could have one or two targets present (i.e., correctly implying no target-absent trials) and those in the "low-expectation" condition were told that each trial would have up to two targets (i.e., incorrectly implying there could be target-absent trials). Compared to the high-expectation group, the low-expectation group had a lower hit rate, lower false alarm rate and quit trials more quickly, consistent with a lower quitting threshold (i.e., performing less exhaustive searches) and a potentially higher target-present decision criterion. The expectation effect was present from the start and remained across the experiment-despite exposure to the same true distribution of targets, the groups' performances remained divergent, primarily driven by the different subjective experiences caused by each groups' self-fulfilling prophecies. The effects were limited to the single-targets trials, which provides insights into the mechanisms affected by the initial expectations set by the instructions. In sum, initial expectations can have dramatic influences-searchers who do not expect to find a target, are less likely to find a target as they are more likely to quit searching earlier.

Radiologist Opinions of a Quality Assurance Program: The Interaction Between Error, Emotion, and Preventative Action

RATIONALE AND OBJECTIVES

To investigate inter-relationships between radiologist opinions of a quality assurance (QA) program, QA Committee communications, negative emotions, self-identified risk factors, and preventive actions taken following major errors.

MATERIALS AND METHODS

A 48 question electronic survey was distributed to all 431 radiologists within the same teleradiology organization between June 15 and July 3, 2018. Two reminders were sent during the survey time period. Descriptive statistics were generated, and comparisons were made with Fisher exact test. Significance level was set at p < 0.05.

RESULTS

Response rate was 67.5% (291/431), and 72.5% of respondents completed all survey questions. A total of 64.3% of respondents were male, and the highest proportion of radiologists (28.9%, 187/291) had been in practice >20 years. Preventative actions following an error were positively correlated to a higher opinion of the QA process, self-identification of personal risk factors for error, and greater negative emotions following an error (all p < 0.05). A higher opinion of communications with the QA committee was associated with a positive opinion of the QA process (p < 0.001). An inverse relationship existed between negative emotion and opinion of QA committee communications (p < 0.05) and negative emotion and opinion of the QA process (p < 0.05). Radiologist gender and full time versus part time status had a significant effect on perception of the QA process (p < 0.05).

CONCLUSION

Radiologist opinions of their institutional QA process was related to the number of negative emotions experienced and preventative actions taken following major errors. Nurturing trust and incorporating more positive feedback in the QA process may improve interactions with QA Committees and mitigate future errors.

A proposed ''A to Z RAM (Radiograph Assessment Method)'' for triage of patients with a suspected bone tumour

INTRODUCTION

We propose a ''A to Z RAM (Radiograph Assessment Method)'' for evaluation of Radiograph of patients with a suspected bone tumour.

METHODS

In the current study, ten radiological features with letters 'A, B, C, D, E, F and Z' were used and which included the age of the patient, involved part of the bone, characteristics, content, distinctiveness, the exterior of the bone, fracture, and zone of transition. Four independent observers (orthopaedic oncologists and surgical oncologists) evaluated a set of 30 radiographs of bone tumour selected at random from our hospital database based on A to Z RAM. We classified the lesions into two groups according to the traffic signal system; Green (suspected benign lesion) and Red (suspected malignant lesion).

RESULTS

There were 18 (60%) benign bone lesions and 12 (40%) malignant lesions in the current study. 91.6% of malignant tumours and 88.8% of the benign tumours were identified correctly by the four observers. The inter-observer variability with Fleiss kappa was 0.884 (95% CI 0.7-1.03 p-value < 0.05), suggestive of agreement not by chance. These radiographs were again reassessed by the four observers after 3 months. The interobserver variability by Fleiss kappa was 1.0 (95% CI 0.8-1.1) suggesting complete agreement amongst the observers. Both orthopaedic oncologists had intra-observer kappa as 1.0 each and both surgical oncologists had 0.795 and 0.930 respectively.

CONCLUSION

The proposed A to Z RAM is an easy to use and reproducible method for reviewing radiographs in the out-patient department along with clinical findings for better management of patients with suspected bone lesions. The A to Z RAM can be a medical triage tool and subdivide bone lesions into two subgroups i.e. suspected benign lesion with a suggestion of further investigations with MRI and biopsy and suspected malignant lesion with a suggestion of MRI or early referral to a tertiary cancer center with expertise in orthopaedic oncology.

IMPLICATIONS FOR PRACTICE

The A to Z RAM (Radiologic Assessment Method) is a reproducible method for reviewing radiographs in the out-patient department and can be an aid for better management of patients. A to Z RAM is useful as a medical triage system, subdividing patients according to the probable diagnosis into a suspected benign lesion and suspected malignant lesion.

A novel framework for rapid diagnosis of COVID-19 on computed tomography scans

Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.

The role of clinical history in the interpretation of chest radiographs

OBJECTIVE

This review will appraise the literature pertaining to the influences that clinical history has on the action of assessing the chest radiograph.

KEY FINDINGS

There remains conflicting evidence on the impact of clinical history on chest radiography. Some research suggests that clinical history has the potential to influence the reporter in a negative way by limiting their search strategy to a more focussed search. Image interpretation is more accurate when reporters are allowed to conduct a free search of the chest image, untainted by preconceived concepts.

CONCLUSION

Clinical history needs to be accessed appropriately to aid and not stifle accurate image interpretation. Reporters need to be aware of the potential bias clinical history can introduce to their reporting and develop strategies to alleviate this as much as possible.

IMPLICATIONS FOR PRACTICE

A greater understanding of the potential bias of clinical history on the process of image interpretation is required by all reporters. Reporters need to develop an approach and strategy when accessing clinical history. Novice reporters need to be educated regarding the impact of clinical history on their reporting.

Traumatic lumbar spine fractures: Transverse process fractures dominate

PURPOSE

With motor vehicle collisions (MVC) predominating as a source of trauma now, we sought to 1) reassess the types of traumatic lumbar spine fractures, 2) highlight the coincidence of transverse process fractures (TPF) with visceral injuries and 3) emphasize the difference in management between compression fracture (CF) and TPF.

METHODS

We retrospectively reviewed the reports of lumbar spine and abdominopelvic CT scans from 2017 and 2018 to classify the types of spine fractures, their mechanism of injury, treatment and coexistence of abdominopelvic injuries.

RESULTS

2.2% of patients had posttraumatic lumbar spine fractures (113/5229), including 58 patients (51.3%) with isolated TPF and 42 (37.2%) with isolated CF; 13 patients had mixed types. TPF accounted for 70% of all fractures (195/277) as opposed to 24% for CF (67/277). MVC was responsible for 60.3% (35/58) of TPF but falls accounted for 73.8% (31/42) of CF. The odds ratio of having isolated TPF from MVC was 4.1[1.8-9.0] versus CF after a fall from standing was 4.5[2.0-10.5]. Of patients with both visceral injuries and lumbar spine fractures, 75% (27/36) had isolated TPF (odds ratio of visceral injury with TPF was 4.4[1.8-10.7]). No TPF were treated with an intervention, however 77% (40/52) of CF were addressed surgically or with braces.

CONCLUSION

TPF are the most common lumbar spine fractures and are often associated with MVC. There is a high association between TPF and abdominopelvic visceral injury requiring radiologists' attentiveness even though the TPF is not directly addressed.

The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability

In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, three dimensions are taken into account: agreement (that is, how much a group of raters mutually agree on a single case); confidence (that is, how much a rater is certain of each rating expressed); and competence (that is, how accurate a rater is). Therefore, this metric produces a reliability score weighted for the raters’ confidence and competence, but it only requires the former information to be actually collected, as the latter can be obtained by the ratings themselves, if no further information is available. We found that our proposal was both more conservative and robust to known paradoxes than other existing agreement measures, by virtue of a more articulated notion of the agreement due to chance, which was based on an empirical estimation of the reliability of the single raters involved. We discuss the above metric within a realistic annotation task that involved 13 expert radiologists in labeling the MRNet dataset. We also provide a nomogram by which to assess the actual accuracy of a classification model, given the reliability of its ground truth. In this respect, we also make the point that theoretical estimates of model performance are consistently overestimated if ground truth reliability is not properly taken into account.