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Worldwide, myocardial infarction is a leading cause of mortality and disability. The phrase ``myocardial infarction'' refers to ischemia, which is the outcome of an imbalance in perfusion between supply and demand and results in the death of cardiac myocytes Myocardial ischemia is often diagnosed based on the patient's medical history and electrocardiogram (ECG) findings. Potential ischemic symptoms include a variety of chest, upper extremity, jaw, or epigastric pain or discomfort that typically lasts at least 20 minutes, is diffuse, not positional, not localized, not dependent on movement of the area, and may be accompanied by syncope, dyspnea, or nausea. These symptoms can occur at rest or after physical activity. These symptoms may be mistaken for other conditions since they are not specific to myocardial ischemia.Radiologists play a crucial role in this scenario since imaging is increasingly being used to identify and categorize these individuals. We report 4 cases of myocardial infarction presenting without chest pain and discovered incidentally during imaging tests.

In this case report, we describe a rare case of inter-atrial septal lipomatosis occasionally found in a PET-CT scan performed in an 81-year-old patient with a history of periprosthetic endocarditis post aortic prosthesis implantation. The patient reappeared in 2022 in the emergency department complaining of symptomatology of worsening asthenia, fever, and elevated inflammatory indices. He was hospitalized in Cardiology department on suspect of recurrence of endocarditis and underwent PET-CT examination that showed an increased metabolic finding at the interatrial septum. On possible suspicion of recurrence of infective endocarditis or cardiac tumor pathology, further diagnostic investigation by cardio-RM examination was requested. However, after radiologic consultation, the previous performed FDG-PET/TC examinations were re-evaluated and a misdiagnosed uptake at this level was revealed, which was assumed to be due to the endocarditis condition because of widespread uptake throughout the perivalvular area.

Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76–81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied—functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery.