Background: Chronic venous disease (CVD) represents a significant health challenge, particularly in obese individuals. This study focuses on the interplay between inflammation, obesity, and CVD, by analyzing the role of inflammatory markers in the disease progression. Methods: Clinical and paraclinical data of 619 patients hospitalized and treated in the Phlebology Department (1stSurgical Department, “Pius Brînzeu” Emergency County Hospital Timișoara, Romania) between 2018 and 2024 were analyzed. Results: The statistical analysis revealed that age, C-reactive protein (CRP), fibrinogen, and absolute neutrophil count (ANC) were key predictors of CVD progression. Specifically, elevated CRP and fibrinogen levels correlated strongly with increased CVD severity, particularly in patients with higher body-mass index (BMI). BMI, while not an independent predictor, contributed indirectly to the disease severity through its association with these inflammatory markers. The logistic regression model incorporating age, BMI, CRP, fibrinogen, and ANC demonstrated a high predictive accuracy, with an area under the curve (AUC) of 0.902, highlighting the models reliability in stratifying patients at risk for severe CVD. Conclusions: This predictive model not only aids in identifying high-risk patients but also reinforces inflammation as a critical therapeutic target in CVD management.

Synovial sarcoma (SS) is a rare and highly malignant tumor and a type of soft tissue sarcoma (STS), for which survival has not improved significantly in recent years. Synovial sarcomas occur mostly in adolescents and young adults (15–35 years old), usually affecting the deep soft tissues near the large joints of the extremities, with males being at a slightly higher risk. Despite its name, synovial sarcoma is neither related to the synovial tissues that are a part of the joints, i.e., the synovium, nor does it express synovial markers; however, the periarticular synovial sarcomas can spread as a secondary tumor to the joint capsule. SS was initially described as a biphasic neoplasm comprising of both epithelial and uniform spindle cell components. Synovial sarcoma is characterized by the presence of the pathognomonic t (X; 18) (p11.2; q11.2) translocation, involving a fusion of the SS18 (formerly SYT) gene on chromosome 18 to one of the synovial sarcoma X (SSX) genes on chromosome X (usually SSX1 or SSX2), which is seen in more than 90% of SSs and results in the formation of SS18-SSX fusion oncogenes.

Background/Objectives: Artificial intelligence (AI) is rapidly transforming the landscape of modern medicine, offering advanced tools for diagnosing complex conditions. In the realm of venous pathologies such as chronic venous disease (CVD), venous reflux, and deep venous thrombosis (DVT), AI has shown tremendous potential to improve diagnostic accuracy, streamline workflows, and enhance clinical decision-making. This study aims to evaluate the efficacy and feasibility of AI algorithms in diagnosing venous diseases and explore their potential impact on clinical practice. Methods: This paper provides a comprehensive review of key studies documenting the use of AI in venous pathology diagnostics, with different electronic databases being searched, including MEDLINE/Pub Med, Web of Science, Scopus, Embase, ResearchGate, and Google Scholar. Results: Out of 52 reports assessed for eligibility, 43 were excluded according to the preset criteria; therefore, findings from nine major studies involving more than 1000 patients were analyzed. The evaluation shows that AI utilization in the diagnosis of venous pathologies has demonstrated significant improvements. Notably, AI algorithms have achieved an accuracy exceeding 90%, significantly reducing inter-observer variability and ensuring consistent interpretation of ultrasonographic images across different clinicians and settings. Additionally, AI has accelerated diagnostic workflows, decreasing the time required for image analysis by more than 50%. Furthermore, AI has proven capable of detecting subtle abnormalities, such as minor venous reflux or early-stage thrombi, which may be overlooked during manual evaluations. Conclusions: Artificial intelligence represents a transformative innovation in the diagnosis and management of venous diseases. By enhancing diagnostic accuracy, streamlining workflows, and enabling personalized care, AI has the potential to address current challenges in venous diagnostics and improve patient outcomes. The future of AI in venous diagnostics is promising, and several areas of development were noted, including AI algorithms embedding directly into ultrasound devices to provide instantaneous diagnostic insights during patient evaluations; combining AI-processed Doppler data with other imaging modalities, such as computed tomography or MRI, for comprehensive assessments; AI usage in order to predict disease progression and tailor treatment strategies based on individual patient profiles; and constructing large-scale, multicenter datasets to improve the robustness and generalizability of AI algorithms.

Background/Objectives: Artificial intelligence (AI) is gaining an increasing amount of influence in various fields, including medicine. In radiology, where diagnoses are based on collaboration between diagnostic devices and the professional experience of radiologists, AI intervention seems much easier than in other fields, but this is often not the case. Many times, the patients orient themselves according to the doctor, which is not applicable in the case of AI. Another limitation rests in the controversies regarding medico-legal liability. In the field of radio-imaging in Romania, the implementation of AI systems in diagnosis is at its beginning. An important aspect of this is raising awareness among the population about these assistive AI systems and, also, awareness of the technological evolution of AI among medical staff. This narrative review manuscript analyzes the existing literature data regarding the medico-legal aspects of AI application in radiology, highlighting the controversial aspects and the lack of statutory legislative regulations in Romania. Methods: A detailed search was conducted across three electronic databases including MEDLINE/PubMed, Scopus, and Web of Science, with 53 papers serving as the literature corpus of our review. Results: General requirements for artificial intelligence systems used in radiology have been established. In the radiological diagnostic process, there are five levels of AI system implication. Until now, completely autonomous AI systems have not been used. Regarding liability in the case of malpractice, at the currently accepted legislative level, most of the time, the radiologist is liable for their own fault or non-compliant use of diagnostic AI systems. Controversies arise in the case of radio-imaging diagnosis in which AI systems act autonomously. Conclusions: In order for AI diagnostic radio-imaging systems to be implemented, they must meet certain quality standards and be approved. The radiologist must know these systems, accept them, know their limits, and validate them in accordance with their degree of involvement in radiological diagnosis. Considering the evolution of technology in the Romanian medical system, including radiology, in the future, an alignment with the legal standards established/proposed at the European level is desired.

Background and Objectives: The prevalence of thyroid nodules and type 2 diabetes mellitus (T2D) has increased. This study firstly aims to assess the prevalence of T2DM among thyroid nodules patients who underwent total thyroidectomy in the Western Romanian population. By focusing on the biochemical and hormonal parameters, it also aims to provide insights into possible connections between T2D and different types (benignant or malignant) of thyroid nodules. Materials and Methods: A total of 926 patients who underwent total thyroidectomy were analysed, comprising 99 (10.7%) T2D patients and 827 (89.3%) non-diabetic patients (non-T2D). Results: This study’s results revealed an increased prevalence of T2D among thyroid nodules patients. Among these patients, higher values of FT4 and inflammatory markers and a higher prevalence of malignant nodules (55%, p = 0.001) were noted. Conclusions: Our study revealed an increased prevalence of T2D among thyroid nodules patients. The biochemical profile of thyroid nodules in T2D patients presents aspects, like elevated FT4 and inflammatory markers, which should be considered in their clinical management. Furthermore, a T2D patient seems to be more likely to develop thyroid malignancies. Thyroid screening strategies in diabetic patients should be considered.

Background: IgG4-RD is an autoimmune fibro-inflammatory condition with chronic evolution and systemic involvement. The incidence is not clearly established. IgG4-RD can easily mimic other pathologic conditions, such as tumors and infections; therefore, affected patients are frequently misdiagnosed and sometimes undertreated. The disease can affect multiple organs and needs a multidisciplinary therapeutic approach. Sometimes, the lack of diagnosis is a result of the high cost of dedicated serological analyses, as well as patient non-compliance. Methods: This review summarizes the current state of knowledge regarding the diagnosis and treatment of IgG4-RD, together with the authors’ practical experience. Results: The diagnosis of IgG4-RD has no specific clinical, paraclinical, or imaging aspects. This condition can mimic any other disease in the affected organ. Frequently, a diagnosis will result from the exclusion of other diseases and even the analysis of a patient’s reaction to a dedicated treatment. Spontaneous remission is rarely observed, and recurrence is common . Most patients have a favorable response to glucocorticoid therapy, but there is a need for maintenance treatment due to the risk of recurrence. Conclusions: IgG4-RD is an underdiagnosed and undertreated condition due to the lack of specific clinical, paraclinical, and imaging changes, the cost-related limitations of dedicated laboratory analyses, and patient non-compliance. If, in the case of patients with fibro-inflammatory changes in one or more structures, the diagnosis of IgG4-RD is excluded, even by elimination, the complications of its occurrence are prevented. This approach is cost-effective, as it prevents complications that require acute and long-term supportive treatment.