Search Results (573)

Background and Objectives: Cluster headaches (CHs) are one of the most painful primary headaches and negatively affect the lives of patients due to misdiagnosis. Family medicine (FM) and emergency medicine (EM) physicians are one of the most important steps in making the correct diagnosis and directing patients to headache specialists. In this study, the knowledge and management approaches of these two groups regarding CH were evaluated. Materials and Methods: Two online questionnaires were developed to gather the demographic data of physicians and to assess their knowledge about the characteristics, diagnosis, and treatment of CHs. Results: A total of 120 FM doctors and 98 EM doctors participated in this study. Answers about diagnostic criteria were similar in both groups. It was found that 70% of the participating physicians had concerns about misdiagnosing cluster headaches, and only 15% considered themselves sufficiently knowledgeable on the topic. Additionally, nearly half of the physicians were unaware that autonomic symptoms are mandatory for diagnosis and believed that NSAIDs are effective in treatment. Conclusions: In our study, for the first time, EM and FM physicians’ knowledge about the diagnosis and treatment of and professional competence in CHs was evaluated. It was found that the participants had knowledge about CHs but still considered themselves incompetent. For the correct and early diagnosis and for the proper management of CHs, EM and FM physicians, who can be called gatekeepers of CHs, need more medical education-based strategies. Full article

Due to the incomplete nature of cognitive testing data and human subjective biases, accurately diagnosing mental disease using functional magnetic resonance imaging (fMRI) data poses a challenging task. In the clinical diagnosis of mental disorders, there often arises a problem of limited labeled data due to factors such as large data volumes and cumbersome labeling processes, leading to the emergence of unlabeled data with new classes, which can result in misdiagnosis. In the context of graph-based mental disorder classification, open-world semi-supervised learning for node classification aims to classify unlabeled nodes into known classes or potentially new classes, presenting a practical yet underexplored issue within the graph community. To improve open-world semi-supervised representation learning and classification in fMRI under low-label settings, we propose a novel open-world semi-supervised learning approach tailored for functional magnetic resonance imaging analysis, termed Open-World Semi-Supervised Learning for fMRI Analysis (OpenfMA). Specifically, we employ spectral augmentation self-supervised learning and dynamic concept contrastive learning to achieve open-world graph learning guided by pseudo-labels, and construct hard positive sample pairs to enhance the network’s focus on potential positive pairs. Experiments conducted on public datasets validate the superior performance of this method in the open-world psychiatric disease diagnosis domain. Full article

Background: Hemophagocytic lymphohistiocytosis (HLH) is an aggressive, life-threatening condition commonly observed in young children. Distinguishing primary HLH from secondary HLH, such as malignancy-associated HLH, can be challenging, potentially leading to misdiagnosis and inappropriate treatment. Case presentation: A 16-month-old female presented with fever, decreased appetite, and rhinorrhea. A review of the peripheral blood smear revealed anemia and leukopenia, with absolute neutropenia characterized by a high lymphocyte count (approximately 80% were T cells by flow cytometry). Flow cytometry was negative for immunophenotypically abnormal cells. Initially, the cytopenia was attributed to a viral infection. However, the cytopenia did not improve, and a bone marrow evaluation revealed evidence of HLH but no immunophenotypically abnormal population. An extensive work-up for HLH, including next-generation sequencing (NGS) and cytogenetic testing identified the KMT2A::MLLT3 fusion transcript, indicating malignancy-associated HLH in the setting of evolving leukemia. Because there was no increase in blasts or immunophenotypically abnormal cells, the diagnosis of leukemia could not be made at that time. The patient was closely monitored and, seven weeks later, was diagnosed with acute myeloid leukemia/acute monocytic leukemia. In addition to the KMT2A::MLLT3 fusion, pathogenic variants in the PTPN11 and FLT3 genes were detected by NGS. Conclusions: The presentation of evolving acute monocytic leukemia can be nonspecific, mimicking conditions such as HLH, without an initial increase in immature cells or monocytes. Maintaining a broad differential diagnosis and including comprehensive molecular genetic testing may facilitate early diagnosis and appropriate treatment. Full article

Introduction: Liposclerosing myxofibrous tumors (LSMFTs) have been described as an infrequent and peculiar fibrous dysplasia variant with a predilection for the intertrochanteric femoral region and are not globally considered a distinct tumor. Given their features, they may be confused with a variety of entities. Our aim is to analyze the clinical, radiological, histopathological and molecular features of LSMFTs. Material and Methods: We report 15 new LSMFT cases managed in our tertiary referral hospital and compare our findings with those of the 241 previous LSMFT cases published in the English medical literature. Results: In plain radiography and computerized tomography, LSMFTs are well-defined intraosseous lytic masses with peripheral sclerotic rims and variable amounts of internal calcifications. Histopathologically, LSFMTs consist of variable amounts of spindle cells, bone matrix, adipose tissue, and cystic spaces embedded in a predominantly fibromyxoid stroma. Molecular tests reveal GNAS and TP53 mutations. Conclusions: Knowledge of LSMFT and its typical radiological appearance with heterogeneous histopathological findings—especially in small biopsies—are key to preventing the misdiagnosis and overtreatment of affected patients. Full article

Background/Objectives: Acute Limb Ischemia (ALI) is a vascular emergency which is accompanied by a significant risk of limb loss or even death. Rapid restoration of arterial perfusion using surgical and/or endovascular techniques is crucial for limb salvage. Undeniably, an accurate and prompt diagnosis is the first step to improve patient prognosis. The typical clinical presentation is not always present and the variety of symptoms may result in non-vascular specialists missing the diagnosis. Methods: In this single-center retrospective descriptive study, we reviewed all patients hospitalized between January 2018 and January 2024 for ALI. Patients who were initially misdiagnosed, causing a delayed diagnosis > 24 h, and who therefore did not receive timely treatment, were identified. Moreover, patients with a timely diagnosis of ALI who were treated in our institution during the same time period were collected. Results: Among 280 ALI patients, 14 were initially misdiagnosed. The median time from initial symptoms to definite diagnosis was 38.8 days (range 1.5–365). Several specialties such as orthopedic surgeons, neurologists, and general practitioners were involved in patients’ initial assessment. Three patients underwent primary amputation due to irreversible ALI, while nine underwent revascularization and one conservative treatment. Thirty-day limb salvage rate was 9/14 and thirty-day mortality was observed in one patient. Secondary interventions were needed in 65% of these cases. Patients with a delayed ALI diagnosis, when compared to those with a timely diagnosis, presented a significantly lower limb salvage rate (65% vs. 89%, p-value = 0.02) and a significantly higher rate of reinterventions (65% vs. 18%, p-value < 0.001). Conclusions: Many patients with ALI are primarily referred to non-vascular specialties. Misdiagnosed and mistreated ALI negatively affects outcomes. Full article

Skin diseases in melanin-rich skin often present diagnostic challenges due to the unique characteristics of darker skin tones, which can lead to misdiagnosis or delayed treatment. This disparity impacts millions within diverse communities, highlighting the need for accurate, AI-based diagnostic tools. In this paper, we investigated the performance of three machine learning methods -Support Vector Machines (SVMs), Random Forest (RF), and Decision Trees (DTs)-combined with state-of-the-art (SOTA) deep learning models, EfficientNet, MobileNetV2, and DenseNet121, for predicting skin conditions using dermoscopic images from the HAM10000 dataset. The features were extracted using the deep learning models, with the labels encoded numerically. To address the data imbalance, SMOTE and resampling techniques were applied. Additionally, Principal Component Analysis (PCA) was used for feature reduction, and fine-tuning was performed to optimize the models. The results demonstrated that RF with DenseNet121 achieved a superior accuracy of 98.32%, followed by SVM with MobileNetV2 at 98.08%, and Decision Tree with MobileNetV2 at 85.39%. The proposed methods overcome the SVM with the SOTA EfficientNet model, validating the robustness of the proposed approaches. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to benchmark performance, showcasing the potential of these methods in advancing skin disease diagnostics for diverse populations. Full article

Background: Pancreatic neuroendocrine neoplasms (PNENs) are rare but clinically significant tumors with variable radiological presentations that complicate diagnosis. While typical PNENs are well characterized, atypical features, such as cystic or hypoenhancing patterns, are less understood and can lead to diagnostic delays or misdiagnosis. This study aimed to evaluate atypical radiological presentations of PNENs, focusing on their impact on diagnostic pathways and differentiation from other pancreatic pathologies. Methods: A retrospective review was conducted of all PNEN cases diagnosed at a single tertiary center between 2010 and 2020. Cases with histopathological confirmation and available cross-sectional imaging were included. Radiological features were categorized as typical (solid and hyperenhancing) or atypical (cystic and hypoenhancing). Demographic, radiological, and pathological data were analyzed. Comparisons between typical and atypical PNENs were performed using descriptive and inferential statistics. Results: Among 77 PNEN cases, 39 met the inclusion criteria. Atypical radiological presentations were identified in 46% of cases, including cystic (18%) and hypoenhancing (28%) lesions. Hypoenhancing PNENs were significantly more likely to present with advanced disease (54% vs. 14% in typical PNENs, p = 0.016). In contrast, none of the cystic PNENs exhibited advanced disease. Atypical PNENs posed greater diagnostic challenges, with alternative diagnoses initially considered in 64% of hypoenhancing and 43% of cystic cases compared to 10% of typical PNENs (p = 0.0042). Conclusions: Atypical PNENs, particularly hypoenhancing lesions, present significant diagnostic challenges and are more likely to be associated with advanced disease. These findings highlight the need for improved recognition of atypical imaging patterns and more precise diagnostic strategies. However, the retrospective design and small cohort size limit the generalizability of our findings. Further multicenter studies are warranted to refine the imaging criteria and optimize the differentiation from other pancreatic neoplasms. Full article

Background/Objectives: The increasing utilization of artificial intelligence (AI) in the medical field holds the potential to address the global shortage of doctors. However, various challenges, such as usability, privacy, inequality, and misdiagnosis, complicate its application. This literature review focuses on AI’s role in cardiology, specifically its impact on the diagnostic accuracy of AI algorithms analyzing 12-lead electrocardiograms (ECGs) to detect left ventricular hypertrophy (LVH). Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of PubMed, CENTRAL, Google Scholar, Web of Science, and Cochrane Library. Eligible studies included randomized controlled trials (RCTs), observational studies, and case–control studies across various settings. This review is registered in the PROSPERO database (registration number 531468). Results: Seven significant studies were selected and included in our review. Meta-analysis was performed using RevMan. Co-CNN (with incorporated demographic data and clinical variables) demonstrated the highest weighted average sensitivity at 0.84. 2D-CNN models (with demographic features) showed a balanced performance with good sensitivity (0.62) and high specificity (0.82); Co-CNN models excelled in sensitivity (0.84) but had lower specificity (0.71). Traditional ECG criteria (SLV and CV) maintained high specificities but low sensitivities. Scatter plots revealed trends between demographic factors and performance metrics. Conclusions: AI algorithms can rapidly analyze ECG data with high sensitivity. The diagnostic accuracy of AI models is variable but generally comparable to classical criteria. Clinical data and the training population of AI algorithms play a critical role in their efficacy. Future research should focus on collecting diverse ECG data across different populations to improve the generalizability of AI algorithms. Full article

Nowadays, the diagnosis of Parkinson’s disease (PD) remains essentially clinical, based on the subjective observations of clinicians. In addition, misdiagnosis with other neuro disorders, such as Alzheimer’s (AD), can occur. Herein, an untargeted lipidomic analysis of 75 plasma samples was performed to identify lipid species capable of discriminating between these two neuro groups. Therefore, PLS-DA and OPLS-DA analysis revealed significant differences in patient profiles in the sphingolipid and glycerophospholipid categories. As a result, a putative lipid biomarker panel was developed, which included HexCer (40:1; O2) and PC (O-32:0), with an area under the receiver operating characteristic curve (AUC) > 80, respectively. This panel was effective in discriminating between diseased and healthy subjects, but most importantly, it could discriminate between two neurodegenerative disorders that can present similar symptoms, namely PD and AD. Together, these findings suggest that the dysregulated metabolism of lipids plays a critical role in AD and PD pathology and may represent a valuable clinical tool for their diagnosis. Thus, further targeted studies are encouraged to better understand the underlying mechanisms of PD and confirm the diagnostic potency of the identified lipid metabolites. Full article

Autoimmune pancreatitis (AIP) is a rare chronic pancreatitis subtype that often mimics pancreatic cancer due to the overlapping clinical and radiological features, posing significant diagnostic challenges. Similarly, distinguishing AIP from pancreatic neuroendocrine neoplasms (PanNENs), which present with nonspecific symptoms, adds complexity to clinical evaluations. We present the case of a 46-year-old male with recurrent acute idiopathic pancreatitis. Abdominal computed tomography (CT) revealed a 25 mm hypodense mass in the pancreatic tail with mild arterial contrast enhancement. Magnetic resonance imaging (MRI) showed the mass to be hypointense on T2-weighted sequences, with no diffusion restriction and an enhancement pattern akin to normal pancreatic tissue. The endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) was inconclusive. Gallium-68 DOTATATE positron emission tomography–CT (Ga-68 DOTATATE PET-CT) showed an increased tracer uptake, leading to a distal pancreatectomy with a splenectomy. Histopathology demonstrated chronic sclerotic pancreatitis with inflammatory infiltrates. Elevated serum IgG4 levels confirmed the diagnosis of type 1 AIP Differentiating AIP from pancreatic malignancies, including PanNENs, is both critical and complex. This case highlights a misdiagnosis of PanNENs in a patient with focal AIP, where neuroendocrine hyperplasia and islet cell clusters within fibrotic areas mimicked PanNENs, even on Ga-68 PET-CT. The findings emphasize the potential for false positives with Ga-68 DOTATATE PET-CT and the importance of integrating clinical, radiological, and histological data for an accurate diagnosis. Full article

Fumarate hydratase (FH) deficiency is a rare, yet impactful metabolic disorder caused by mutations in the FH gene, affecting the Krebs cycle, leading to the accumulation of fumarate and pseudohypoxic states. This metabolic shift promotes cell signaling alterations that can drive tumorigenesis, as heterozygous germline mutations in the FH gene, resulting in hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome. FH-deficient uterine leiomyomas show peculiar histological features that may lead to misdiagnosis STUMP (smooth muscle tumor of uncertain malignant potential) and uLMS (uterine leiomyosarcoma). Definitive diagnosis involves clinical evaluation, imaging, and histopathological examination, with immunohistochemistry for FH protein being a key diagnostic tool. Management of FH-deficient leiomyomas may involve conventional treatments like surgery and hormonal therapy but also requires careful monitoring and genetic counseling for associated malignancies. High-intensity focused ultrasound (HIFU) has emerged as a promising treatment option for fibroids, although long-term efficacy remains a concern also because of its inability to obtain tissue for a pathological diagnosis. Fumarate hydratase deficiency (FHD) represents a significant challenge in gynecologic oncology due to its association with an increased risk of hereditary leiomyomatosis and renal cell carcinoma. Nevertheless, to the best of our knowledge, there is a lack of studies demonstrating the potential role of FH deficiency in increased risk of leiomyosarcomatosus transformation. Early detection, genetic screening, and personalized treatment approaches are critical for improving patient outcomes. The aim of this review is to develop a narrative overview of the implications of FHD in gynecological diseases and its correlation with cancer risk. For the first time, this review offers an overview of the necessity for studies to address the possible correlation between FH deficiency and the risk of developing leiomyosarcoma, focusing on new perspectives that can be explored in the field of better FH deficiency knowledge and cancer risk. Full article

Background/Objectives: Unlike rhabdomyolysis and neuroleptic malignant syndrome (NMS), massive asymptomatic creatine kinase elevation (MACKE) represents a condition commonly detected during routine screening in patients receiving antipsychotic drugs. In particular, current evidence indicates a greater incidence of this condition in patients without signs of NMS, rhabdomyolysis, or other causes of CK increase during exposure to second-generation antipsychotics (SGAs) than first-generation antipsychotics (FGAs) with a variable onset and duration. Although its pathophysiology is still not fully elucidated, MACKE has usually been recognized as a self-limiting condition, but drug discontinuation might also be required to successfully revert it. Overall, knowledge in this field is mainly extrapolated from adult data, while similar evidence in youths is still limited. As clinicians might often deal with MACKE, its understanding needs to be expanded to avoid misdiagnosis, potentially leading to wasteful healthcare spending and unfavorable patient outcomes. Methods: By reporting the first case of MACKE in an adolescent receiving an SGA, namely paliperidone, we also aimed to provide a comprehensive overview of this medical condition. Conclusions: Making a MACKE diagnosis is essential since its relevant clinical and economic implications are mainly related to unnecessary closer laboratory monitoring or therapeutic changes (e.g., drug discontinuation or switch to another medication). Full article

Background: Patients with functional neurological disorder presenting as stroke mimics or functional stroke mimics (FSMs) pose significant diagnostic challenges. In the acute phase, especially when patients are present within the therapeutic window for acute reperfusion treatments, a misdiagnosis of FSM can lead to unnecessary and costly interventions. Despite its clinical importance, the literature on the risk factors for FSM is limited. This study aims to compare the clinical and epidemiological characteristics of patients with FSM to those with confirmed acute ischemic stroke (AIS). Methods: This case–control study involved temporal matching between consecutive series of patients with FSM and controls with AIS from a single tertiary university hospital in southern Portugal. Results: A total of 188 patients were included: 64 cases (FSM) and 188 controls (AIS). The rate of stroke code activation and use of ambulance between was comparable between the two groups. The group of patients with FSM was younger (53.2 years vs. 69.5 years, p < 0.001) and had a higher proportion of females (52.4% vs. 47.6%, p = 0.001). There was no difference in terms of clinical severity at presentation. The proportion of specific signs, such as transcortical aphasia (3.1% vs. 20.9%, p = 0.014), gait abnormalities (15.6% vs. 33.9%, p = 0.004), and cranial nerve abnormalities (31.2% vs. 43.5%, p = 0.042), was lower in the FSM group compared to the AIS group. The proportion of patients on antithrombotic therapy (90.9% vs. 9.1%, p = 0.007) and antihypertensive drugs (78.5%, vs. 21.5%, p < 0.001) prior to the event was significantly higher in the AIS group. Likewise, the prevalence of cerebrovascular risk factors such as diabetes mellitus (14.3% vs. 85.7%, p = 0.005), arterial hypertension (23.8% vs. 76.2%, p = 0.001), and smoking (43.7% vs. 56.3%, p = 0.005) was lower in the FSM group compared to the AIS group. No statistically significant differences were observed in cholesterol levels or the prevalence of dyslipidemia between the two groups. Psychiatric comorbidities, including generalized anxiety disorder (71.4% vs. 28.6%, p = 0.05) and major depressive disorder (61.9% vs. 28.1%, p = 0.01), were more prevalent in the FSM group. Conclusions: Patients with FSM display different clinical and epidemiological profiles, with a higher likelihood of being younger, female, having prior psychiatric conditions, and lacking traditional cerebrovascular risk factors. Full article

Background and Objectives: Actinomycosis is a rare chronic contagion caused by Actinomyces spp. known for its ability to mimic malignant processes across various anatomical locations. Its clinical presentation can often resemble malignancies, Mycobacterium tuberculosis infections, nocardiosis, fungal infections, or other granulomatous diseases. This case series presents three patients diagnosed with Actinomyces spp., highlighting the diagnostic challenges and diverse clinical manifestations of the disease. Materials and Methods: We reviewed the clinical course, diagnostic procedures, and treatment outcomes of three patients with confirmed Actinomyces spp. The first case involved a 51-year-old male with a history of rhabdomyosarcoma in remission who presented with dysphagia. Magnetic resonance imaging identified an irregularly enhancing mass in the tonsil, and subsequent tonsillectomy confirmed Actinomyces spp. The second patient, an 80-year-old female, presented with dysphagia and a sublingual mass initially suspected to be diffuse large B-cell non-Hodgkin lymphoma; however, a histopathological analysis confirmed Actinomyces spp. The third case involved a 72-year-old male with abdominal pain and an ulcerated gastric lesion, where subtotal gastrectomy and histopathological examination confirmed the diagnosis of Actinomyces spp. Results: These three cases highlight the ability of Actinomyces spp. to closely mimic malignant lesions, which significantly complicates the diagnostic process. Although personalized interventions were required for each patient, diagnoses were ultimately confirmed through histopathology. Despite these challenges, timely recognition and appropriate treatment were achieved, underscoring the need to consider Actinomyces spp. in the differential diagnosis of similar presentations. Conclusions:Actinomyces spp. remains a diagnostic challenge due to its ability to mimic a variety of malignant and contagion conditions. This case series emphasizes the need for a thorough histopathological examination and a high index of suspicion when encountering lesions with atypical presentations. Given the potential for misdiagnosis, awareness and consideration of Actinomyces spp. are crucial in the differential diagnosis of chronic contagion and mass lesions. Further studies are warranted to refine diagnostic and therapeutic approaches. Full article

Background: Since Legionella pneumophila (Lp) is widely present in natural and artificial water environments, it has a high potential risk of outbreak. Diarrhea caused by Lp pulmonary infection is an important symptom of Legionnaires’ disease (LD); however, the underlying mechanism of the diarrhea has not yet been revealed. This not only has a negative impact on clinical diagnosis and treatment, but may also cause misdiagnosis. Methods: In the present study, a mouse model of enteritis caused by pulmonary infection of Lp was established. By using this mouse model, we explored the underlying mechanisms of the enteritis caused by Lp pulmonary infection. Results: The results indicated that the systemic inflammatory response played a very important role in the enteritis phenotype caused by a strong-virulence strain of Lp. Furthermore, we found that the expression of Bcl-2 was downregulated by IL-6 through the p53 signaling pathway, thereby activating the caspase 3 of intestinal epithelial cells (IECs), causing the apoptosis of IECs, and ultimately leading to the enteritis phenotype. Conclusions: The IL-6–caspase 3 axis plays an important role in enteritis caused by Lp pulmonary infection. Full article