Mesothelioma: Scientific clues for prevention, diagnosis, and therapy.

Mesothelioma affects mostly older individuals who have been occupationally exposed to asbestos. The global mesothelioma incidence and mortality rates are unknown, because data are not available from developing countries that continue to use large amounts of asbestos. The incidence rate of mesothelioma has decreased in Australia, the United States, and Western Europe, where the use of asbestos was banned or strictly regulated in the 1970s and 1980s, demonstrating the value of these preventive measures. However, in these same countries, the overall number of deaths from mesothelioma has not decreased as the size of the population and the percentage of old people have increased. Moreover, hotspots of mesothelioma may occur when carcinogenic fibers that are present in the environment are disturbed as rural areas are being developed. Novel immunohistochemical and molecular markers have improved the accuracy of diagnosis; however, about 14% (high-resource countries) to 50% (developing countries) of mesothelioma diagnoses are incorrect, resulting in inadequate treatment and complicating epidemiological studies. The discovery that germline BRCA1-asssociated protein 1 (BAP1) mutations cause mesothelioma and other cancers (BAP1 cancer syndrome) elucidated some of the key pathogenic mechanisms, and treatments targeting these molecular mechanisms and/or modulating the immune response are being tested. The role of surgery in pleural mesothelioma is controversial as it is difficult to predict who will benefit from aggressive management, even when local therapies are added to existing or novel systemic treatments. Treatment outcomes are improving, however, for peritoneal mesothelioma. Multidisciplinary international collaboration will be necessary to improve prevention, early detection, and treatment.

Olfactory sniffing signals consciousness in unresponsive patients with brain injuries.

After severe brain injury, it can be difficult to determine the state of consciousness of a patient, to determine whether the patient is unresponsive or perhaps minimally conscious1, and to predict whether they will recover. These diagnoses and prognoses are crucial, as they determine therapeutic strategies such as pain management, and can underlie end-of-life decisions2,3. Nevertheless, there is an error rate of up to 40% in determining the state of consciousness in patients with brain injuries4,5. Olfaction relies on brain structures that are involved in the basic mechanisms of arousal6, and we therefore hypothesized that it may serve as a biomarker for consciousness7. Here we use a non-verbal non-task-dependent measure known as the sniff response8-11 to determine consciousness in patients with brain injuries. By measuring odorant-dependent sniffing, we gain a sensitive measure of olfactory function10-15. We measured the sniff response repeatedly over time in patients with severe brain injuries and found that sniff responses significantly discriminated between unresponsive and minimally conscious states at the group level. Notably, at the single-patient level, if an unresponsive patient had a sniff response, this assured future regaining of consciousness. In addition, olfactory sniff responses were associated with long-term survival rates. These results highlight the importance of olfaction in human brain function, and provide an accessible tool that signals consciousness and recovery in patients with brain injuries.

Automating hybrid collective intelligence in open-ended medical diagnostics.

Collective intelligence has emerged as a powerful mechanism to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and medical diagnostics. However, collective intelligence has been mostly applied to relatively simple decision tasks (e.g., binary classifications). Applications in more open-ended tasks with a much larger problem space, such as emergency management or general medical diagnostics, are largely lacking, due to the challenge of integrating unstandardized inputs from different crowd members. Here, we present a fully automated approach for harnessing collective intelligence in the domain of general medical diagnostics. Our approach leverages semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology to overcome a major hurdle to collective intelligence in open-ended medical diagnostics, namely to identify the intended diagnosis from unstructured text. We tested our method on 1,333 medical cases diagnosed on a medical crowdsourcing platform: The Human Diagnosis Project. Each case was independently rated by ten diagnosticians. Comparing the diagnostic accuracy of single diagnosticians with the collective diagnosis of differently sized groups, we find that our method substantially increases diagnostic accuracy: While single diagnosticians achieved 46% accuracy, pooling the decisions of ten diagnosticians increased this to 76%. Improvements occurred across medical specialties, chief complaints, and diagnosticians' tenure levels. Our results show the life-saving potential of tapping into the collective intelligence of the global medical community to reduce diagnostic errors and increase patient safety.

Diagnostic Discordance, Uncertainty, and Treatment Ambiguity in Community-Acquired Pneumonia : A National Cohort Study of 115 U.S. Veterans Affairs Hospitals.

BACKGROUND: Evidence-based practice in community-acquired pneumonia often assumes an accurate initial diagnosis. OBJECTIVE: To examine the evolution of pneumonia diagnoses among patients hospitalized from the emergency department (ED). DESIGN: Retrospective nationwide cohort. SETTING: 118 U.S. Veterans Affairs medical centers. PATIENTS: Aged 18 years or older and hospitalized from the ED between 1 January 2015 and 31 January 2022. MEASUREMENTS: Discordances between initial pneumonia diagnosis, discharge diagnosis, and radiographic diagnosis identified by natural language processing of clinician text, diagnostic coding, and antimicrobial treatment. Expressions of uncertainty in clinical notes, patient illness severity, treatments, and outcomes were compared. RESULTS: Among 2 383 899 hospitalizations, 13.3% received an initial or discharge diagnosis and treatment of pneumonia: 9.1% received an initial diagnosis and 10.0% received a discharge diagnosis. Discordances between initial and discharge occurred in 57%. Among patients discharged with a pneumonia diagnosis and positive initial chest image, 33% lacked an initial diagnosis. Among patients diagnosed initially, 36% lacked a discharge diagnosis and 21% lacked positive initial chest imaging. Uncertainty was frequently expressed in clinical notes (58% in ED; 48% at discharge); 27% received diuretics, 36% received corticosteroids, and 10% received antibiotics, corticosteroids, and diuretics within 24 hours. Patients with discordant diagnoses had greater uncertainty and received more additional treatments, but only patients lacking an initial pneumonia diagnosis had higher 30-day mortality than concordant patients (14.4% [95% CI, 14.1% to 14.7%] vs. 10.6% [CI, 10.4% to 10.7%]). Patients with diagnostic discordance were more likely to present to high-complexity facilities with high ED patient load and inpatient census. LIMITATION: Retrospective analysis; did not examine causal relationships. CONCLUSION: More than half of all patients hospitalized and treated for pneumonia had discordant diagnoses from initial presentation to discharge. Treatments for other diagnoses and expressions of uncertainty were common. These findings highlight the need to recognize diagnostic uncertainty and treatment ambiguity in research and practice of pneumonia-related care. PRIMARY FUNDING SOURCE: The Gordon and Betty Moore Foundation.

Development of Patient Safety Measures to Identify Inappropriate Diagnosis of Common Infections.

BACKGROUND: Inappropriate diagnosis of infections results in antibiotic overuse and may delay diagnosis of underlying conditions. Here we describe the development and characteristics of 2 safety measures of inappropriate diagnosis of urinary tract infection (UTI) and community-acquired pneumonia (CAP), the most common inpatient infections on general medicine services. METHODS: Measures were developed from guidelines and literature and adapted based on data from patients hospitalized with UTI and CAP in 49 Michigan hospitals and feedback from end-users, a technical expert panel (TEP), and a patient focus group. Each measure was assessed for reliability, validity, feasibility, and usability. RESULTS: Two measures, now endorsed by the National Quality Forum (NQF), were developed. Measure reliability (derived from 24 483 patients) was excellent (0.90 for UTI; 0.91 for CAP). Both measures had strong validity demonstrated through (a) face validity by hospital users, the TEPs, and patient focus group, (b) implicit case review (ĸ 0.72 for UTI; ĸ 0.72 for CAP), and (c) rare case misclassification (4% for UTI; 0% for CAP) due to data errors (<2% for UTI; 6.3% for CAP). Measure implementation through hospital peer comparison in Michigan hospitals (2017 to 2020) demonstrated significant decreases in inappropriate diagnosis of UTI and CAP (37% and 32%, respectively, P < .001), supporting usability. CONCLUSIONS: We developed highly reliable, valid, and usable measures of inappropriate diagnosis of UTI and CAP for hospitalized patients. Hospitals seeking to improve diagnostic safety, antibiotic use, and patient care should consider using these measures to reduce inappropriate diagnosis of CAP and UTI.

Histological diagnostic discrepancy and its clinical impact in bone and soft tissue tumors referred to a sarcoma center.

Histological diagnosis of sarcomas (malignant bone and soft tissue tumors) is challenging due to their rarity, morphological diversity, and constantly evolving diagnostic criteria. In this study, we aimed to assess the concordance in histological diagnosis of bone and soft tissue tumors between referring hospitals and a tertiary sarcoma center and analyzed the clinical impact of the diagnostic alteration. We analyzed 628 consecutively accessioned specimens from 624 patients who visited a specialized sarcoma center for treatment. The diagnoses at referring hospitals and those at the sarcoma center were compared and classified into four categories: agreed, disagreed, specified, and de-specified. Of the 628 specimens, the diagnoses agreed in 403 (64.2%) specimens, whereas some changes were made in 225 (35.8%) specimens: disagreed in 153 (24.3%), specified in 52 (8.3%), and de-specified in 20 (3.2%) cases. The benign/intermediate/malignant judgment changed for 92 cases (14.6%). The diagnostic change resulted in patient management modification in 91 cases (14.5%), including surgical and medical treatment changes. The main inferred reason for the diagnostic discrepancies was a different interpretation of morphological findings of the tumor, which accounted for 48.9% of the cases. This was followed by the unavailability of specialized immunohistochemical antibodies and the unavailability of genetic analysis. In summary, our study clarified the actual clinical impact of diagnostic discrepancy in bone and soft tissue tumors. This may underscore the value of pathology consultation, facilitating access to specialized diagnostic tools, and continued education. These measures are expected to improve diagnostic precision and ultimately benefit patients.

Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review.

PURPOSE: Artificial intelligence (AI) for reading breast screening mammograms could potentially replace (some) human-reading and improve screening effectiveness. This systematic review aims to identify and quantify the types of AI errors to better understand the consequences of implementing this technology. METHODS: Electronic databases were searched for external validation studies of the accuracy of AI algorithms in real-world screening mammograms. Descriptive synthesis was performed on error types and frequency. False negative proportions (FNP) and false positive proportions (FPP) were pooled within AI positivity thresholds using random-effects meta-analysis. RESULTS: Seven retrospective studies (447,676 examinations; published 2019-2022) met inclusion criteria. Five studies reported AI error as false negatives or false positives. Pooled FPP decreased incrementally with increasing positivity threshold (71.83% [95% CI 69.67, 73.90] at Transpara 3 to 10.77% [95% CI 8.34, 13.79] at Transpara 9). Pooled FNP increased incrementally from 0.02% [95% CI 0.01, 0.03] (Transpara 3) to 0.12% [95% CI 0.06, 0.26] (Transpara 9), consistent with a trade-off with FPP. Heterogeneity within thresholds reflected algorithm version and completeness of the reference standard. Other forms of AI error were reported rarely (location error and technical error in one study each). CONCLUSION: AI errors are largely interpreted in the framework of test accuracy. FP and FN errors show expected variability not only by positivity threshold, but also by algorithm version and study quality. Reporting of other forms of AI errors is sparse, despite their potential implications for adoption of the technology. Considering broader types of AI error would add nuance to reporting that can inform inferences about AI's utility.

Using AI to Identify Unremarkable Chest Radiographs for Automatic Reporting.

Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose To estimate the proportion of unremarkable chest radiographs, where AI can correctly exclude pathology (ie, specificity) without increasing diagnostic errors. Materials and Methods In this retrospective study, consecutive chest radiographs in unique adult patients (≥18 years of age) were obtained January 1-12, 2020, at four Danish hospitals. Exclusion criteria included insufficient radiology reports or AI output error. Two thoracic radiologists, who were blinded to AI output, labeled chest radiographs as "remarkable" or "unremarkable" based on predefined unremarkable findings (reference standard). Radiology reports were classified similarly. A commercial AI tool was adapted to output a chest radiograph "remarkableness" probability, which was used to calculate specificity at different AI sensitivities. Chest radiographs with missed findings by AI and/or the radiology report were graded by one thoracic radiologist as critical, clinically significant, or clinically insignificant. Paired proportions were compared using the McNemar test. Results A total of 1961 patients were included (median age, 72 years [IQR, 58-81 years]; 993 female), with one chest radiograph per patient. The reference standard labeled 1231 of 1961 chest radiographs (62.8%) as remarkable and 730 of 1961 (37.2%) as unremarkable. At 99.9%, 99.0%, and 98.0% sensitivity, the AI had a specificity of 24.5% (179 of 730 radiographs [95% CI: 21, 28]), 47.1% (344 of 730 radiographs [95% CI: 43, 51]), and 52.7% (385 of 730 radiographs [95% CI: 49, 56]), respectively. With the AI fixed to have a similar sensitivity as radiology reports (87.2%), the missed findings of AI and reports had 2.2% (27 of 1231 radiographs) and 1.1% (14 of 1231 radiographs) classified as critical (P = .01), 4.1% (51 of 1231 radiographs) and 3.6% (44 of 1231 radiographs) classified as clinically significant (P = .46), and 6.5% (80 of 1231) and 8.1% (100 of 1231) classified as clinically insignificant (P = .11), respectively. At sensitivities greater than or equal to 95.4%, the AI tool exhibited less than or equal to 1.1% critical misses. Conclusion A commercial AI tool used off-label could correctly exclude pathology in 24.5%-52.7% of all unremarkable chest radiographs at greater than or equal to 98% sensitivity. The AI had equal or lower rates of critical misses than radiology reports at sensitivities greater than or equal to 95.4%. These results should be confirmed in a prospective study. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yoon and Hwang in this issue.

Lophomonas as a respiratory pathogen-jumping the gun.

Human infections with the protozoan Lophomonas have been increasingly reported in the medical literature over the past three decades. Initial reports were based on microscopic identification of the purported pathogen in respiratory specimens. Later, a polymerase chain reaction (PCR) was developed to detect Lophomonas blattarum, following which there has been a significant increase in reports. In this minireview, we thoroughly examine the published reports of Lophomonas infection to evaluate its potential role as a human pathogen. We examined the published images and videos of purported Lophomonas, compared its morphology and motility characteristics with host bronchial ciliated epithelial cells and true L. blattarum derived from cockroaches, analyzed the published PCR that is being used for its diagnosis, and reviewed the clinical data of patients reported in the English and Chinese literature. From our analysis, we conclude that the images and videos from human specimens do not represent true Lophomonas and are predominantly misidentified ciliated epithelial cells. Additionally, we note that there is insufficient clinical evidence to attribute the cases to Lophomonas infection, as the clinical manifestations are non-specific, possibly caused by other infections and comorbidities, and there is no associated tissue pathology attributable to Lophomonas. Finally, our analysis reveals that the published PCR is not specific to Lophomonas and can amplify DNA from commensal trichomonads. Based on this thorough review, we emphasize the need for rigorous scientific scrutiny before a microorganism is acknowledged as a novel human pathogen and discuss the potential harms of misdiagnoses for patient care and scientific literature.

Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study.

BACKGROUND: In clinical laboratories, the precision and sensitivity of autoverification technologies are crucial for ensuring reliable diagnostics. Conventional methods have limited sensitivity and applicability, making error detection challenging and reducing laboratory efficiency. This study introduces a machine learning (ML)-based autoverification technology to enhance tumor marker test error detection. METHODS: The effectiveness of various ML models was evaluated by analyzing a large data set of 397 751 for model training and internal validation and 215 339 for external validation. Sample misidentification was simulated by random shuffling error-free test results with a 1% error rate to achieve a real-world approximation. The ML models were developed with Bayesian optimization for tuning. Model validation was performed internally at the primary institution and externally at other institutions, comparing the ML models' performance with conventional delta check methods. RESULTS: Deep neural networks and extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.834 to 0.903, outperforming that of conventional methods (0.705 to 0.816). External validation by 3 independent laboratories showed that the balanced accuracy of the ML model ranged from 0.760 to 0.836, outperforming the balanced accuracy of 0.670 to 0.773 of the conventional models. CONCLUSIONS: This study addresses limitations regarding the sensitivity of current delta check methods for detection of sample misidentification errors and provides versatile models that mitigate the operational challenges faced by smaller laboratories. Our findings offer a pathway toward more efficient and reliable clinical laboratory testing.

Redefining ITT cortisol thresholds on Abbott platforms to prevent misdiagnosis of adrenal insufficiency.

BACKGROUND: Adrenal insufficiency (AI) is a life-threatening condition which requires long term glucocorticoid replacement. The insulin tolerance test (ITT) is the current gold standard test for diagnosis of secondary AI, but the widely accepted cut-off value of a peak cortisol of less than 500 nmol/L assumes that anyone who does not reach this value has AI and thus requires full replacement. The cut-off used to diagnose AI is also founded on outdated assays. Use of this cut-off in an era of more specific immunoassays therefore risks misdiagnosis, subsequent unnecessary glucocorticoid exposure and associated adverse effects with increased mortality risk. DESIGN, PATIENTS AND MEASUREMENTS: This retrospective analysis assessed 300 ITT cortisol responses using the Abbott Architect and Alinity analyser platforms in patients with suspected AI over a period of 12 years (August 2010 to January 2022), at a tertiary centre. RESULTS: Patients were classified as having AI or not, based on a comprehensive clinical review of electronic patient records from the point of test to the present day by a panel of pituitary and adrenal specialists. Using the current institutional cut-off value of 500 nmol/L, receiver operating characteristic analysis identified a 100.0% sensitivity and 43.6% specificity (area under the curve 0.979). Using a lower cortisol threshold value of 416 nmol/L on the Abbott analyser platform maintained a sensitivity of 100.0% and improved the specificity to 86.7%. CONCLUSION: This data supports lowering the Abbott analyser ITT peak cortisol threshold to 416 nmol/L. Use of this improved cut-off avoids unnecessary glucocorticoid replacement therapy in 104 (34.7%) of individuals in this study. All patients remained well with at least 1 year longitudinal follow up of glucocorticoid replacement.

Is work overload associated with diagnostic errors on 18F-FDG-PET/CT?

PURPOSE: To determine the association between workload and diagnostic errors on 18F-FDG-PET/CT. MATERIALS AND METHODS: This study included 103 18F-FDG-PET/CT scans with a diagnostic error that was corrected with an addendum between March 2018 and July 2023. All scans were performed at a tertiary care center. The workload of each nuclear medicine physician or radiologist who authorized the 18F-FDG-PET/CT report was determined on the day the diagnostic error was made and normalized for his or her own average daily production (workloadnormalized). A workloadnormalized of more than 100% indicates that the nuclear medicine physician or radiologist had a relative work overload, while a value of less than 100% indicates a relative work underload on the day the diagnostic error was made. The time of the day the diagnostic error was made was also recorded. Workloadnormalized was compared to 100% using a signed rank sum test, with the hypothesis that it would significantly exceed 100%. A Mann-Kendall test was performed to test the hypothesis that diagnostic errors would increase over the course of the day. RESULTS: Workloadnormalized (median of 121%, interquartile range: 71 to 146%) on the days the diagnostic errors were made was significantly higher than 100% (P = 0.014). There was no significant upward trend in the frequency of diagnostic errors over the course of the day (Mann-Kendall tau = 0.05, P = 0.7294). CONCLUSION: Work overload seems to be associated with diagnostic errors on 18F-FDG-PET/CT. Diagnostic errors were encountered throughout the entire working day, without any upward trend towards the end of the day.

Exploring Clinical Lessons Learned by Experienced Hospitalists from Diagnostic Errors and Successes.

BACKGROUND: Diagnostic errors cause significant patient harm. The clinician's ultimate goal is to achieve diagnostic excellence in order to serve patients safely. This can be accomplished by learning from both errors and successes in patient care. However, the extent to which clinicians grow and navigate diagnostic errors and successes in patient care is poorly understood. Clinically experienced hospitalists, who have cared for numerous acutely ill patients, should have great insights from their successes and mistakes to inform others striving for excellence in patient care. OBJECTIVE: To identify and characterize clinical lessons learned by experienced hospitalists from diagnostic errors and successes. DESIGN: A semi-structured interview guide was used to collect qualitative data from hospitalists at five independently administered hospitals in the Mid-Atlantic area from February to June 2022. PARTICIPANTS: 12 academic and 12 community-based hospitalists with ≥ 5 years of clinical experience. APPROACH: A constructivist qualitative approach was used and "reflexive thematic analysis" of interview transcripts was conducted to identify themes and patterns of meaning across the dataset. RESULTS: Five themes were generated from the data based on clinical lessons learned by hospitalists from diagnostic errors and successes. The ideas included appreciating excellence in clinical reasoning as a core skill, connecting with patients and other members of the health care team to be able to tap into their insights, reflecting on the diagnostic process, committing to growth, and prioritizing self-care. CONCLUSIONS: The study identifies key lessons learned from the errors and successes encountered in patient care by clinically experienced hospitalists. These findings may prove helpful for individuals and groups that are authentically committed to moving along the continuum from diagnostic competence towards excellence.

Ethical considerations for artificial intelligence in dermatology: a scoping review.

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.

The use of artificial intelligence (AI) in dermatology is rapidly increasing, with applications in dermatopathology, medical dermatology, cutaneous surgery, microscopy/spectroscopy and the identification of prognostic biomarkers (characteristics that provide information on likely patient health outcomes). However, with the rise of AI in dermatology, ethical concerns have emerged. We reviewed the existing literature to identify applications of AI in the field of dermatology and understand the ethical implications. Our search initially identified 202 papers, and after we went through them (screening), 68 were included in our review. We found that ethical concerns are related to the use of AI in the areas of clinical image analysis, teledermatology, natural language processing models, privacy, skin of colour representation, and patient and provider attitudes toward AI. We identified nine ethical principles to facilitate the safe use of AI in dermatology. These ethical principles include fairness, inclusivity, transparency, accountability, security, privacy, reliability, informed consent and conflict of interest. Although there are many benefits of integrating AI into clinical practice, our findings highlight how safeguards must be put in place to reduce rising ethical concerns.

Paediatric-onset lymphomatoid papulosis: results of a multicentre retrospective cohort study on behalf of the EORTC Cutaneous Lymphoma Tumours Group (CLTG).

BACKGROUND: Lymphomatoid papulosis (LyP) is a rare cutaneous T-cell lymphoproliferative disorder. Comprehensive data on LyP in the paediatric population are scarce. OBJECTIVES: To characterize the epidemiological, clinical, histopathological and prognostic features of paediatric LyP. METHODS: This was a retrospective multicentre international cohort study that included 87 children and adolescents with LyP diagnosed between 1998 and 2022. Patients aged ≤ 18 years at disease onset were included. LyP diagnosis was made in each centre, based on clinicopathological correlation. RESULTS: Eighty-seven patients from 12 centres were included. Mean age at disease onset was 7.0 years (range 3 months-18 years) with a male to female ratio of 2 : 1. Mean time between the onset of the first cutaneous lesions and diagnosis was 1.3 years (range 0-14). Initial misdiagnosis concerned 26% of patients. LyP was most often misdiagnosed as pityriasis lichenoides et varioliformis acuta, insect bites or mollusca contagiosa. Erythematous papules or papulonodules were the most frequent clinical presentation. Pruritus was specifically mentioned in 21% of patients. The main histological subtype was type A in 55% of cases. When analysed, monoclonal T-cell receptor rearrangement was found in 77% of skin biopsies. The overall survival rate was 100%, with follow-up at 5 years available for 33 patients and at 15 years for 8 patients. Associated haematological malignancy (HM) occurred in 10% of cases (n = 7/73), including four patients with mycosis fungoides, one with primary cutaneous anaplastic large cell lymphoma (ALCL), one with systemic ALCL and one with acute myeloid leukaemia. If we compared incidence rates of cancer with the world population aged 0-19 years from 2001 to 2010, we estimated a significantly higher risk of associated malignancy in general, occurring before the age of 19 years (incidence rate ratio 87.49, 95% confidence interval 86.01-88.99). CONCLUSIONS: We report epidemiological data from a large international cohort of children and adolescents with LyP. Overall, the disease prognosis is good, with excellent survival rates for all patients. Owing to an increased risk of associated HM, long-term follow-up should be recommended for patients with LyP.

Lymphomatoid papulosis is a very rare skin condition caused by an abnormal increase in white blood cells (called 'lymphocytes') in the skin. The condition rarely affects children, so most of the scientific data published about this disease focuses on adults. This study involved 12 academic dermatology centres in Europe, the Middle East and North America, and gathered data from about 87 children who presented with symptoms of lymphomatoid papulosis before the age of 19 years. The aim of this study was to better describe this disease in the paediatric population and discuss its treatment options and evolution. We found that the presentation of the disease in children is roughly the same as in adults. Safe and effective treatment options exist. The disease is not life threatening, but it requires investigation by a dermatologist, both to make a careful diagnosis and to monitor it as sometimes associated cancers that originate from blood cells can occur, mostly on the skin.

Diagnostic and treatment patterns in urinary and genital tract infections: insights from a referral clinic in Beirut, Lebanon.

BACKGROUND: Urinary Tract Infections (UTIs) and Genital Tract Infections (GTIs) are common yet serious health concerns. Precise diagnosis is crucial due to the potential severe consequences of misdiagnosis. This study aims to distinguish between UTIs and GTIs, highlighting the importance of accurate differentiation. MATERIALS AND METHODS: The study encompassed 294 patients, categorized into 4 groups: Group GNI (no infection, N = 57), Group GUI (urinary infection, N = 52), Group GGI (genital infection, N = 139), and Group GGUI (both infections, N = 46). Methods included patient interviews, clinical examinations, and laboratory tests such as urine and vaginal swab cultures. RESULTS: The investigation revealed no significant differences in age, BMI, residency, or nationality across groups. However, socioeconomic status varied, with Group GNI having the lowest proportion of low socioeconomic status. In obstetrical characteristics, non-pregnancy rates were higher in Groups GUI and GGUI, with GGUI showing a notably higher abortion rate. Symptom analysis indicated lower symptom prevalence in Group GNI, with pain, itching, pruritus, and vaginal discharge being less frequent, suggesting a link between infection presence and symptom severity. Treatment patterns showed higher usage of ciprofloxacin, antifungals, and vaginal tablets in Groups GUI and GGUI. Laboratory findings highlighted significant Leucocyte Esterase presence and variations in WBC and RBC counts, particularly in Group GGUI. CONCLUSION: The study emphasizes the need for advanced diagnostic techniques, especially those focusing on individual microbial patterns, to enhance UGTI diagnosis. Variations in symptom presentation and treatment across groups underline the necessity for personalized diagnostic and treatment strategies.

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

OBJECTIVES: To evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs. METHODS: A retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs and radiologists' authorised reports using a deep learning and natural language processing algorithm, respectively. The AI-detected discrepant findings between images and reports were assessed for clinical relevance by an external radiologist, as part of the commercial service provided by the AI vendor. The selected missed findings were subsequently returned to the institution's radiologist for final review. RESULTS: In total, 25,104 chest radiographs of 21,039 patients (mean age 61.1 years ± 16.2 [SD]; 10,436 men) were included. The AI software detected discrepancies between imaging and reports in 21.1% (5289 of 25,104). After review by the external radiologist, 0.9% (47 of 5289) of cases were deemed to contain clinically relevant missed findings. The institution's radiologists confirmed 35 of 47 missed findings (74.5%) as clinically relevant (0.1% of all cases). Missed findings consisted of lung nodules (71.4%, 25 of 35), pneumothoraces (17.1%, 6 of 35) and consolidations (11.4%, 4 of 35). CONCLUSION: The AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low. CLINICAL RELEVANCE STATEMENT: The AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography. KEY POINTS: • A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions. • Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations. • Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist's reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevance.

Musculoskeletal misdiagnoses in pediatric patients with spinal tumors.

OBJECTIVE: Childhood spinal tumors often present with musculoskeletal symptoms, potentially causing a misdiagnosis and delays in diagnosis and treatment. This study aims to identify, characterize, and compare children with spinal tumors who had prior musculoskeletal misdiagnoses to those without, analyzing clinical presentation, diagnostic interval, and outcome. STUDY DESIGN: This retrospective cohort study evaluated all children aged 0-14 years diagnosed with a spinal tumor in Denmark from 1996 to 2018. The cohort was identified through the Danish Childhood Cancer Registry, and the registry data were supplemented with data from medical records. The survival was compared using the Kaplan-Meier method. RESULTS: Among 58 patients, 57% (33/58) received musculoskeletal misdiagnoses before the spinal tumor diagnosis. Misdiagnoses were mostly nonspecific (64%, 21/33), involving pain and accidental lesions, while 36% (12/33) were rheumatologic diagnoses. The patients with prior misdiagnosis had less aggressive tumors, fewer neurological/general symptoms, and 5.5 months median diagnostic interval versus 3 months for those without a misdiagnosis. Those with prior misdiagnoses tended to have a higher 5-year survival of 83% (95% confidence interval [CI]: 63%-92%) compared to 66% (95% CI: 44%-82%) for those without (p = .15). CONCLUSION: Less aggressive spinal tumors may manifest as gradual skeletal abnormalities and musculoskeletal symptoms without neurological/general symptoms, leading to misdiagnoses and delays.

What Is Diagnostic Excellence? AJR Podcast Series on Diagnostic Excellence and Error, Episode 1.

In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, introduces the concept of diagnostic excellence and its relevance to radiologists. Patient-centered definitions of diagnostic error and conceptualizations of the diagnostic process are discussed.

In this episode of the AJR Podcast Series on Diagnostic Excellence and Error, Francis Deng, MD, introduces the concept of diagnostic excellence and its relevance to radiologists. Patient-centered definitions of diagnostic error and conceptualizations of the diagnostic process are discussed.

Effect of different working periods on missed diagnosis of colorectal polyps in colonoscopy.

BACKGROUND: To investigate the effect of different working periods on missed diagnoses in patients with colorectal polyps in colonoscopy. METHODS: We conducted a retrospective analysis of patients who were diagnosed with colorectal polyps during colonoscopy in an outpatient department between July and December 2022. These patients were subsequently hospitalized for resection during this period. Patients with missed diagnoses were those who had newly discovered polyps in a second colonoscopy. The working periods were categorized as work, near the end of work, and delayed work, respectively, in the morning and afternoon. RESULTS: A total of 482 patients were included, and the miss rate of diagnosis was 48.1% (232/482), mainly in the transverse colon (25%), and the ascending colon (23%). Patient age was a risk factor for the miss rate of diagnosis (OR = 1.025, 95%CI: 1.009-1.042, P = 0.003) and was also associated with the number of polyps detected for the first colonoscopy (χ2 = 18.196, P = 0.001). The different working periods had no statistical effect on the missed rate of diagnosis (χ2 = 1.998, P = 0.849). However, there was an increasing trend in miss rates towards the end of work and delayed work periods, both in the morning and afternoon. The highest miss rate (60.0%) was observed during delayed work in the afternoon. Additionally, poor bowel preparation was significantly more common during delayed work in the afternoon. CONCLUSIONS: The increasing trend in miss rates towards the end of work and delayed work periods deserves clinical attention. Endoscopists cannot always stay in good condition under heavy workloads.