Gynecologic cancers are among the most common cancers in women and are a significant cause of mortality in women worldwide. FDG-PET images play a critical role in several areas of oncology and are an essential tool for the diagnosis and staging of gynecologic cancer, providing information about tumor metabolism. Aim: The aim of this study was to investigate the relationship between tumor differentiation, type of malignancy, and glucose metabolism in patients with confirmed gynecologic cancer. Materials and Methods We performed a retrospective review of 60 consecutive patients (ages 31-76) with histopathologically confirmed gynecologic cancer who underwent PET-CT in our department between January 2021 and March 2022. Quantitative data on glucose metabolism were collected using the maximum standardized uptake value normalized by lean body mass (SULmax). We reviewed a total of 60 patients with a mean age of 60±9 years, and a median of 62. Results: 14 patients were diagnosed with cervical cancer, 20 with endometrial cancer, and 26 with ovarian cancer; 13 patients had well- differentiated cancer, 11 had moderately differentiated cancer, and 36 had poorly differentiated cancer. A positive correlation was observed between the measured SULmax and the degree of tumor differentiation, indicating higher glucose uptake in poorly differentiated cancer. When examining the relationship between the type of cancer and SULmax, we found that endometrial cancer exhibited the highest mean SULmax (8.36±6.75), followed by ovarian cancer and cervical cancer. Additionally, we noted that glucose uptake was higher in patients aged over 62 years. Regardless of the tumor differentiation, the findings in endometrial cancer showed the highest mean SUVmax values in all grades of tumor differentiation. Conclusion: Our study showed that glucose consumption correlates not only with the degree of differentiation but also with age and type of tumor, with the highest values in patients older than 62 years with endometrial cancer. Given the correlation between glucose consumption and tumor aggressiveness, this finding may be important for prognostic evaluation of patients with gynecologic malignancies.
Coronary CT angiography is a non-invasive method of analyzing the coronary lumen through which the atheromatous bur-den can be evaluated, with the analysis of the type of plaque (soft, calcified, mixed) and the impact on the arterial lumen (stenosis, occlusion). The anatomical evaluation by coronary CT is indicated in symptomatic patients with low and medium risk factors for coronary atherosclerotic disease, in those with inconclusive laboratory and EKG results, patients with un-certain stress test results, in the evaluation of coronary grafts and intrastent stenoses. Depending on the result of the angiography, the severity of the stenosis and the management of the patient are established. In the case of mild and medium stenoses, risk factors management and drug treatment are recommended. For severe stenoses, patients are referred for interventional coronary angiography or functional evaluation. Coronary CT angiography increases the certainty of coronary atherosclerotic disease diagnosis. It has a superior discriminative capacity of plaques with low attenuation, the main predictor of myocardial infarction, with almost five-fold higher risk. Coronary CT angiography increases the adaptation of medication, and the inclusion of statin therapy decreases the risk of fatal and nonfatal myocardial infarction at 5 years and increases the quality of life.
Background: Rotator cuff tendinopathy is most often described as a continuum between the normal cuff and rotator cuff tears with calcific tendinitis having its place along this continuum. Although many studies have focused on the role of magnetic resonance imagining (MRI) in diagnosing the extent of rotator cuff tears and their associated findings with good interobserver reliability, the same cannot be stated about MRI tendinopathy findings. Because of this discrepancy in diagnostic reliability, tendinopathy tends to be overtreated with injections when associated with symptoms, thus potentially increasing the risk of calcific tendinitis and progression toward rotator cuff tears. This study aims to assess whether diagnosing shoulder MRI tendinopathy patterns through dichotomization can accelerate clinical progress toward consensus. Methods: This study is a large retrospective cohort of 184 patients that underwent a 1.5T shoulder MRI for shoulder pain. Inclusion criteria were acromioclavicular arthrosis diagnosed in patients of any age. Exclusion criteria were partial or complete rotator cuff tears. Tendinopathy was considered the dependent variable and registered as a dichotomous variable while acromioclavicular joint arthrosis together with gender was categorical and age was the continuous variable. An attempt was made to generate a clinically significant binary logistic regression to assess the odds ratio of diagnosing tendinopathy based on age, gender, and acromioclavicular joint arthrosis status. Results: An overwhelming proportion of patients was positive for tendinopathy findings (95.11%). 64.12% of patients were within the active age group with patients within the 50-59 group being diagnosed the most with rotator cuff tendinopathy. Conclusions: Due to the high variability of MRI findings that can be considered positive for rotator cuff tendinopathy, an overwhelming skew toward a positive diagnosis was observed, thus dichotomizing tendinopathy diagnosis is not appropriate for clinically relevant conclusion-making.
Recent advances in technology and artificial intelligence have transformed radiology. Artificial intelligence, which uses machine learning, deep learning algorithms, and cellular neural networks, is now applicable to image analysis, enabling the analysis of large volumes of data and the early identification of lesion features, such as tumors. Integrating artificial intelligence into clinical workflows increases the efficiency of medical report generation and enables efficient prioritization of complex or urgent cases. Predictive analytics based on artificial intelligence uses extensive patient data, including demographics, medical history, tests, and imaging, to build more accurate predictive models. Among the areas where artificial intelligence is successfully implemented in radiology is the automated detection of lung nodules. In the field of neurology, artificial intelligence helps track progressive lesions through volumetric analysis of the brain and surveillance of demyelinating diseases. In oncologic radiology, artificial intelligence is used for automated image segmentation, lesion review, and standardized report generation. Therefore, in this ever-evolving landscape, radiologists must embrace emerging technological advances in order to occupy a unique position at the intersection of different diagnostic fields.
Artificial Intelligence (AI) has become an important tool for computer-aided diagnosis of medical imaging. This review aims to provide an overview for clinicians, explaining the relevant aspects of artificial intelligence and machine learning (ML) and presenting up-to-date applications of AI and ML techniques to medical imaging methods such as angiography, magnetic resonance, and echocardiography. For each imaging method, we present the acquisition process, the types of diagnostic test interpretation, and the challenges related to them, as well as how AI/ML techniques, have improved the process of decision making. A summary of selected works applying AI/ML techniques to medical imaging is organized into a table, which highlights the scope of the study, the dataset used, the details of each approach as well as the measured results, including objectives and criteria. The overall benefits of AI in medical imaging are extracted based on the diverse applications and high evaluation scores. In the end, cardiologists should have an advanced understanding of using AI to integrate clinical data and making the final decision in diagnosis.
The article presents the main aspects of sectional anatomy, lymph nodes and adjacent structures as well as MRI examination standard protocol for prostate cancer diagnosis. Using MRI multiparametric examination we succeed in classifying efficiently the malignant prostatic tumors using PI- RADS system. Also, using MRI multiparametric examination we can evaluate the effectiveness of prostate cancer treatment.
Chronic pancreatitis is characterised by progressive and irreversible damage of the pancreatic parenchyma and ductal system, which leads to chronic pain, loss of endocrine and exocrine functions. Clinically, pancreatic exocrine insufficiency becomes apparent only after 90% of the parenchima has been lost. Despite the simple definition, diagnosing chronic pancreatitis remains a challenge, especially for early stage disease. Because pancreatic function tests can be normal until late stages and have significant limitations, there is an incresing interest in the role of imaging techniques for the diagnosis of chronic pancreatitis. In this article we review the utility and accuracy of different imaging methods in the diagnosis of chronic pancreatitis, focusing on the role of advanced imaging (magnetic resonance imaging, endoscopic retrograde cholangiopancreatography and endoscopic ultrasound).
