Nonsurgical removal of a massive calcification of the shoulder.

Calcific tendinopathy of the shoulder is a prevalent and painful condition marked by calcific deposits in the rotator cuff tendons or subacromial bursa, with an incidence of 2.7% to 20%, predominantly affecting individuals aged 30 to 50. Women are 1.5 times more likely to be affected than men. Deposits are frequently bilateral in 10%-20% of cases and most commonly found in the supraspinatus tendon. The pathogenesis remains unclear, with theories suggesting repetitive strain or ischemic degeneration leading to calcium deposition. The disease progresses through precalcific, calcific, and postcalcific phases, with symptoms ranging from mild pain to severe, disabling pain resistant to medication. Diagnosis primarily involves radiographs or CT scans, with ultrasound aiding in deposit staging. Conservative treatments include medication, physiotherapy, and subacromial corticosteroid injections. Novel nonsurgical treatments like ultrasound-guided needling (UGN) and extracorporeal shock wave therapy (ESWT) have shown promise. When conservative measures fail, surgical options achieve significant improvement. This case report details a 53-year-old woman with a 12 cm calcification treated successfully with UGN, demonstrating the efficacy of this minimally invasive technique for large deposits.

Artificial Intelligence in Lung Cancer Screening: The Future Is Now.

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.