Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks.

This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 2.

Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.

Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.

Diagnostic utility of ultrasound in pediatric nasal bone fractures: a systematic review and meta-analysis.

Ultrasonography, a radiation-free and cost-effective modality, stands out as a promising tool for evaluating nasal bone fractures. Despite limited literature on its pediatric application, there is an increasing recognition of its potential to enhance diagnostic precision. To evaluate the diagnostic efficacy of ultrasound in detecting pediatric nasal bone fractures. Employing established guidelines, a systematic review and meta-analysis were conducted through a comprehensive literature search in PubMed, Scopus, Web of Science, and Embase databases until December 5, 2023. Inclusion criteria encompassed studies reporting diagnostic accuracy measures of ultrasound in pediatric patients with nasal bone fractures. Data extraction and analysis were undertaken for the selected studies. Involving four studies with 277 patients, ultrasound demonstrated a pooled sensitivity of 66.1% (95% CI: 35.1-87.5%) and specificity of 86.8% (95% CI: 80.1-91.4%) in diagnosing pediatric nasal fractures. The area under the receiver operating characteristic curve (AUC) was 0.88 (95% CI: 0.72-0.93). After excluding an outlier study, sensitivity and specificity increased to 78.0% (95% CI: 65.6-86.9%) and 87.8 (95% CI: 78.1-93.6%), respectively, with an AUC of 0.79 (95% CI: 0.75-0.94). Pooled positive and negative likelihood ratios were 5.11 (95% CI: 2.12-9.15) and 0.40 (95% CI: 0.14-0.77) before exclusion and 6.75 (95% CI: 3.47-12.30) and 0.26 (95% CI: 0.15-0.40) after exclusion of an outlier study, respectively. This study highlighted ultrasonography's utility in diagnosing pediatric nasal bone fractures with high accuracy and specificity. However, caution is advised in relying solely on ultrasound due to suboptimal overall diagnostic performance, evident in likelihood ratios.

Management of ectopic variceal bleeding with transjugular intrahepatic portosystemic shunt: a systematic review of case reports.

Ectopic varices account for 5% of variceal bleedings and occur outside the gastro-esophageal region. This review evaluates the efficacy of transjugular intrahepatic portosystemic shunt (TIPS) for ectopic variceal management. A comprehensive search through PubMed, Scopus, Web of Science, and Embase was conducted until January 16, 2023, using relevant keywords. Case reports and case series with fewer than 10 patients on TIPS for ectopic variceal management were included. The quality assessment followed the Joanna Briggs Institute checklist for case reports. This systematic review evaluated 43 studies involving 50 patients with ectopic varices undergoing TIPS. Patients had a mean age of 54.3 years, half were female, and two were pregnant. Alcoholic liver disease (48%) and hepatitis C infection (26%) were common causes of portal hypertension. Ascites and splenomegaly were reported in 32% and 28% of the patients, respectively. Rectal, oral, and stomal variceal bleeding accounted for 62%, 16%, and 22% of the patients, respectively. Ectopic varices were mainly located in the duodenum (28%) and rectum (26%) regions. Complications affected 42% of the patients, re-bleeding in eleven and hepatic encephalopathy in seven. The follow-up lasted 12 months on average, and finally, 5 received a liver transplant. Mortality post-TIPS was 18%. Despite complications and a notable mortality rate, favorable outcomes were observed in almost half of the patients with ectopic variceal bleeding managed with TIPS. Further research is warranted to refine strategies and improve patient outcomes.

A meta-analysis on the diagnostic utility of ultrasound in pediatric distal forearm fractures.

Pediatric distal forearm fractures, comprising 30% of musculoskeletal injuries in children, are conventionally diagnosed using radiography. Ultrasound has emerged as a safer diagnostic tool, eliminating ionizing radiation, enabling bedside examinations with real-time imaging, and proving effective in non-hospital settings. The objective of this study is to evaluate the diagnostic efficacy of ultrasound for detecting distal forearm fractures in the pediatric population. A systematic review and meta-analysis were conducted through a comprehensive literature search in PubMed, Scopus, Web of Science, and Embase databases until October 1, 2023, following established guidelines. Eligible studies, reporting diagnostic accuracy measures of ultrasound in pediatric patients with distal forearm fractures, were included. Relevant data elements were extracted, and data analysis was performed. The analysis included 14 studies with 1377 patients, revealing pooled sensitivity and specificity of 94.5 (95% CI 92.7-95.9) and 93.5 (95% CI 89.6-96.0), respectively. Considering pre-test probabilities of 25%, 50%, and 75% for pediatric distal forearm fractures, positive post-test probabilities were 83%, 44%, and 98%, while negative post-test probabilities were 2%, 6%, and 15%, respectively. The bivariate model indicated significantly higher diagnostic accuracy in the subgroup with trained ultrasound performers vs. untrained performers (p = 0.03). Furthermore, diagnostic accuracy was significantly higher in the subgroup examining radius fractures vs. ulna fractures (p < 0.001), while no significant differences were observed between 4-view and 6-view ultrasound subgroups or between radiologist ultrasound interpreters and non-radiologist interpreters. This study highlighted ultrasound's reliability in detecting pediatric distal forearm fractures, emphasizing the crucial role of expertise in precisely confirming fractures through ultrasound examinations.

Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.

BACKGROUND: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored. PURPOSE: To assess the performance of ML in classifying glioma tumor grades based on various WHO criteria. STUDY TYPE: Retrospective. SUBJECTS: A neuropathologist regraded gliomas of 237 patients into WHO 2016 and 2021 from 2007 criteria. FIELD STRENGTH/SEQUENCE: Multicentric 0.5 to 3 Tesla; pre- and post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery. ASSESSMENT: Radiomic features were selected using random forest-recursive feature elimination. The synthetic minority over-sampling technique (SMOTE) was implemented for data augmentation. Stratified 10-fold cross-validation with and without SMOTE was used to evaluate 11 classifiers for 3-grade (2, 3, and 4; WHO 2016 and 2021) and 2-grade (low and high grade; WHO 2007 and 2021) classification. Additionally, we developed the models on data randomly divided into training and test sets (mixed-data analysis), or data divided based on the centers (independent-data analysis). STATISTICAL TESTS: We assessed ML classifiers using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Top performances were compared with a t-test and categorical data with the chi-square test using a significance level of P < 0.05. RESULTS: In the mixed-data analysis, Stacking Classifier without SMOTE achieved the highest accuracy (0.86) and AUC (0.92) in 3-grade WHO 2021 grouping. The results of WHO 2021 were significantly better than WHO 2016 (P-value<0.0001). In the 2-grade analysis, ML achieved 1.00 in all metrics. In the independent-data analysis, ML classifiers showed strong discrimination between grade 2 and 4, despite lower performance metrics than the mixed analysis. DATA CONCLUSION: ML algorithms performed better in glioma tumor grading based on WHO 2021 criteria. Nonetheless, the clinical use of ML classifiers needs further investigation. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Disparities in computed tomography utilization for pediatric blunt trauma: a systematic review and meta-analysis comparing pediatric and non-pediatric trauma centers.

Pediatric blunt trauma is a major cause of morbidity and mortality, and computed tomography (CT) imaging is vital for accurate evaluation and management. Pediatric trauma centers (PTCs) have selective CT practices, while non-PTCs may differ, resulting in potential variations in CT utilization. The objective of this study is to delineate disparities in CT utilization for pediatric blunt trauma patients between PTCs and non-PTCs. A systematic review and meta-analysis were conducted following established guidelines, searching PubMed, Scopus, and Web of Science up to March 3, 2023. All studies examining CT utilization in the management of pediatric (aged < 21 years) blunt trauma and specifying the type of trauma center(s) were included, and data were extracted and analyzed using STATA software version 17.0. An analysis of 30 studies revealed significant variations in CT scan utilization among pediatric blunt trauma patients across different types of trauma centers. PTCs exhibited lower pooled rates of abdominopelvic CT scans (35.4% vs. 44.9%, p < 0.01), cranial CT scans (36.9% vs. 42.9%, p < 0.01), chest CT scans (14.5% vs. 25.4%, p < 0.01), and cervical spine CT scans (23% vs. 45%, p < 0.01) compared to adult or mixed trauma centers (ATCs/MTCs). PTCs had a pooled rate of 54% for receiving at least one CT scan, while ATCs/MTCs had a higher rate of 69.3% (p < 0.05). The studies demonstrated considerable heterogeneity. These findings underscore the need to conduct further research to understand the reasons for the observed variations and to promote appropriate imaging usage, minimize radiation exposure, and encourage collaboration between pediatric and adult trauma centers.

Possible Therapeutic Targets from Derivatives of Natural Marine Products Based on PI3K/AKT Dependent Inhibitors in Viral Infection COVID-19.

Natural resources have long played a prominent part in conventional treatments as a parental source due to their multifaceted functions and lesser side effects. The diversity of marine products is a significant source of possible bioactive chemical compounds with a wide range of potential medicinal applications. Marine organisms produce natural compounds and new drugs with unique properties are produced from these compounds. A lot of bioactive compounds with medicinal properties are extracted from marine invertebrates, including Peptides, Alkaloids, Terpenoids, Steroids. Thus, it can be concluded that marine ecosystems are endowed with natural resources that have a wide range of medicinal properties, and it is important to examine the therapeutic and pharmacological capabilities of these molecules. So, finding particular inhibitors of the COVID-19 in natural compounds will be extremely important. Natural ingredients, in this light, could be a valuable resource in the progression of COVID-19 therapeutic options. Controlling the immunological response in COVID-19 patients may be possible by addressing the PI3K/Akt pathway and regulating T cell responses. T cell effector activity can be improved by preventing anti-viral exhaustion by suppressing PI3K and Akt during the early anti-viral response. The diversity of marine life is a significant supply of potentially bioactive chemical compounds with a broad range of medicinal uses. In this study, some biologically active compounds from marine organisms capable of inhibiting PI3K/AKT and the possible therapeutic targets from these compounds in viral infection COVID-19 have been addressed.

Successful management of a delayed presented button battery ingestion in a toddler: A case report and literature review.

Key Clinical Message: It is important to note that prevention of button battery ingestion is the most effective way to reduce its incidence and complications. This is unachievable without providing educational plans for parents. Moreover, triage nurses and first-line staff who take the history of patients and physicians should take the history to evaluate the risk of battery ingestion. Plain radiographs can be helpful in this matter, as the presence of "Hallow" and "Steep" signs in the anteroposterior and lateral views, respectively, can help. Abstract: Foreign body ingestion is a relatively common occurrence in pediatrics, especially among children 1-3 years of age. Although most cases are benign and managed conservatively, those with high-risk subjects such as button batterie can bring about fatal conditions in the minority of cases. In the present study, the history, diagnostic, and therapeutic procedures of a 13-month-old baby with the final diagnosis of button battery ingestion are presented. The parents ignored the symptoms, suspecting that it was a viral infection. The evaluations showed that a battery was lodged in the middle part of the thoracic esophagus, which was removed by an urgent endoscopic procedure. The patient was under observation and on a nothing-by-mouth diet for a week, receiving nutritional fluid with a nasogastric tube. The necrosis, which was obvious after the removal of the battery, was healing in the second control esophagogastroduodenoscopy performed 1 week after the procedure. The stricture was minimal, and no need for dilation was diagnosed. This case report underscores the importance of a timely diagnosis and removal of these cases. This case underscores the importance of the timely presentation of these cases to health care and the risk of delayed removal, such as necrosis, forming fistula, and perforation of the esophagus. The delay can cause necrosis, fistula, and perforation and might lead to irreversible severe complications and even death.

A Meta-analysis of Burnout in Radiology Trainees and Radiologists: Insights From the Maslach Burnout Inventory.

RATIONALE AND OBJECTIVES: Burnout, stemming from chronic work stress, is a significant issue in the medical field, especially among radiologists. It leads to compromised patient care and reduced job satisfaction. Addressing burnout in radiology is essential for the well-being of radiologists and, in turn, for improving patient care. This study aimed to assess the prevalence and dimensions of burnout among radiology trainees (RTs) and practicing radiologists (PRs). MATERIALS AND METHODS: A systematic review and meta-analysis were conducted in accordance with established guidelines. The search encompassed PubMed, Scopus, Web of Science, and Embase databases up to June 20, 2023. Eligible studies that assessed the rate of burnout syndrome and/or its subscales, including depersonalization (DP), emotional exhaustion (EE), and personal accomplishment (PA), among RTs and/or PRs using the Maslach Burnout Inventory (MBI), were included. Relevant data were extracted and analyzed using R and STATA. RESULTS: Among the 22 included studies, the pooled rates of positive MBI subscales for RTs and PRs were as follows: 54.7% (95% confidence interval [CI]: 43.8%-65.1%, I2 = 95.2%) for DP, 57.2% (95% CI: 48.7%-65.4%, I2 = 92.9%) for EE, and 38.6% (95% CI: 27%-51.7%, I2 = 95.5%) for low PA. The pooled rate indicating the presence of at least one positive MBI subscale was 82.9% (95% CI: 79.2%-86.1%, I2 = 57.4%). For two or more positive MBI subscales, the pooled rate was 55.5% (95% CI: 49.7%-61.3%, I2 = 60.2%), and for three positive MBI subscales, it was 16.7% (95% CI: 11.7%-23.3%, I2 = 82.7%). CONCLUSION: This study emphasizes a notable prevalence of burnout in the radiology specialty, with 8 of 10 individuals exhibiting positive results in at least one MBI subscale. This highlights the urgent need for interventions and support systems to protect the well-being of both trainees and practitioners and uphold the quality of patient care.

Kounis syndrome type I induced by an intramuscular injection of diclofenac: A literature review based on a case report.

Key Clinical Message: The history of any allergy to the medications should be asked by physicians before administration of the medication. The coincidence of allergic and ACS symptoms after a short time of drug administration might be an indicator of Kounis syndrome. Allergic and coronary symptoms should be considered and treated. Abstract: Ischemic heart disease is still the leading cause of death worldwide. Some medications, including NSAIDS and antibiotics, can cause allergic reactions with cardiac manifestations due to spasms of the coronary arteries. In this case, we present a patient with chest pain syndrome due to a hypersensitivity reaction caused by an intramuscular (IM) diclofenac injection. The patient was a 51-year-old male who presented to the emergency department complaining of retrosternal chest pain, breathlessness, and pruritis that started half an hour after an IM diclofenac injection he had because of low back pain. The allergic symptoms subsided with an antihistamine injection, but chest pain and dyspnea remained stable. He was admitted due to the presence of ST-segment depression in leads II, III, and AVF and underwent percutaneous coronary angiography, which was normal. The patient was discharged with the diagnosis of Kounis syndrome, and he had an uneventful follow-up 1 year later. Kounis hypersensitivity-associated acute coronary syndrome, especially type I variant coronary spasm due to endothelial dysfunction is a type of acute myocardial syndrome. The following report describes an uncommon case of anaphylaxis-associated Kounis type I syndrome manifesting ST-segment changes in a male patient following an intramuscular injection of diclofenac.

Current diversity in radiology: A comparative study.

PURPOSE: To compare the demographic characteristics of active physicians, trainees, medical school clinical sciences faculty, and department chairs in radiology with those in other medical specialties. METHODS: An analysis was conducted using publicly available deidentified aggregate data from the Association of American Medical Colleges (AAMC). Our data collection included information from the 2022 AAMC Physician Specialty Data Report, the 2022 AAMC Report on Residents, and the 2022 AAMC Faculty Roster. We examined factors such as graduation country, gender, and self-identified race/ethnicity. MedCalc software was used for the analyses. RESULTS: Compared to other specialties, active radiologists exhibited a significantly lower percentage of females, International Medical Graduates (IMGs), individuals of American Indian/Alaska Native (AIAN) descent, Black/African-American individuals, and individuals of Hispanic/Latino/Spanish origin. Conversely, the proportion of White active radiologists was higher. Among radiology trainees, there was a notably lower percentages of females, IMGs, individuals of Black/African-American descent, and individuals of Hispanic/Latino/Spanish origin, while the percentage of Asians was significantly higher. Furthermore, medical school radiology faculty showed a significant lower proportion of females, Black/African-American individuals, Hispanic/Latino/Spanish individuals, and individuals categorized under the white race/ethnicity, with Asians having a higher representation. As radiology department chairs, Asians were noted at significantly lower percentages compared to their proportions among medical school radiology faculty, while Black/African-American individuals were observed at significantly higher percentages in the same comparison. CONCLUSION: This study revealed a notable underrepresentation of females, individuals of Black/African-American descent, and those of Hispanic/Latino/Spanish origin among active radiologists, radiology trainees, and medical school radiology faculty when compared to their counterparts in other medical specialties. Given these findings, further investigation into the underlying causes of these disparities is warranted.

Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer.

BACKGROUND: Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning. METHODS: A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS). RESULTS: Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %-89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927). CONCLUSION: Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.

Predictors of postoperative pain six months after breast surgery.

Breast cancer, with a high prevalence and survival rate, leads to long-term complications. A major sequel is acute or chronic postoperative pain, and we investigated the possible relationship with clinical and psychological variables. Patients undergoing breast surgery filled out the loneliness (ULS-8) and depression (HADS) questionnaires. Patients rated their pain intensity with the Numerical Rating Scale (0-10, NRS) two days, seven days, and six months after surgery. Of 124 patients, the mean age was 45.86 years old, and the pain scores on the second and seventh postoperative days were 5.33 and 3.57, respectively. Sixth-month pain was significantly correlated with the acute scores with a mean of 3.27; and in the multivariate analysis, it was significantly associated with preoperative pain (p-value = 0.007), self-reported loneliness (p-value = 0.010), and adjuvant radiotherapy (p-value = 0.004). In conclusion, loneliness may be a risk factor for postoperative pain in breast surgery.

Diagnostic accuracy of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI to differentiate benign from malignant breast lesions: A systematic review and meta-analysis.

PURPOSE: Magnetic resonance imaging (MRI) can reduce the need for unnecessary invasive diagnostic tests by nearly half. In this meta-analysis, we investigated the diagnostic accuracy of intravoxel incoherent motion modeling (IVIM) and dynamic contrast-enhanced (DCE) MRI in differentiating benign from malignant breast lesions. METHOD: We systematically searched PubMed, EMBASE, and Scopus. We included English articles reporting diagnostic accuracy for both sequences in differentiating benign from malignant breast lesions. Articles were assessed by quality assessment of diagnostic accuracy studies-2 (QUADAS-2) questionnaire. We used a bivariate effects model for standardized mean difference (SMD) analysis and diagnostic test accuracy analysis. RESULTS: Ten studies with 537 patients and 707 (435 malignant and 272 benign) lesions were included. The D, f, Ktrans, and Kep mean values significantly differ between benign and malignant lesions. The pooled sensitivity (95 % confidence interval) and specificity were 86.2 % (77.9 %-91.7 %) and 70.3 % (56.5 %-81.1 %) for IVIM, and 93.8 % (85.3 %-97.5 %) and 68.1 % (52.7 %-80.4 %) for DCE, respectively. Combined IVIM and DCE depicted the highest area under the curve of 0.94, with a sensitivity and specificity of 91.8 % (82.8 %-96.3 %) and 87.6 % (73.8 %-94.7 %), respectively. CONCLUSIONS: Combined IVIM and DCE had the highest diagnostic accuracy, and multiparametric MRI may help reduce unnecessary benign breast biopsy.