Osteoarthritis (OA) is a chronic disease and the most common orthopedic disorder. A vast majority of the social OA burden is related to hips and knees. The prevalence of knee OA varied across studies and such differences are reflected by the heterogeneity of data reported by studies conducted worldwide. A complete understanding of the pathogenetic mechanisms underlying this pathology is essential. The OA inflammatory process starts in the synovial membrane with the activation of the immune system, involving both humoral and cellular mediators. A crucial role in this process is played by the so-called “damage-associated molecular patterns” (DAMPs). Mesenchymal stem cells (MSCs) may be a promising option among all possible therapeutic options. However, many issues are still debated, such as the best cell source, their nature, and the right amount. Further studies are needed to clarify the remaining doubts. This review provides an overview of the most recent and relevant data on the molecular mechanism of cartilage damage in knee OA, including current therapeutic approaches in regenerative medicine.

Sarcopenia is a progressive, generalized skeletal muscle disorder characterized by reduction of muscle mass and strength. It is associated with increased adverse outcomes including falls, fractures, physical disability, and mortality, particularly, in elderly patients. Nowadays, sarcopenia has become a specific imaging biomarker able to predict clinical outcomes of patients. Muscle fibre reduction has shown to be an unfavourable pre-operative predictive factor in patients with cancer, and is associated with worse clinical outcomes in terms of postoperative complications, morbidity, mortality, and lower tolerance of chemoradiation therapy. Several imaging modalities, including dual-energy X-ray absorptiometry, CT, MRI, and US can be used to estimate muscle mass and quality to reach the diagnosis of sarcopenia. This article reviews the clinical implications of sarcopenia, how this condition can be assessed through different imaging modalities, and future perspectives of imaging of sarcopenia.

The widespread use of imaging examinations has increased the detection of incidental adrenal lesions, which are mostly benign and non-functioning adenomas. The differentiation of a benign from a malignant adrenal mass can be crucial especially in oncology patients since it would greatly affect treatment and prognosis. In this setting, imaging plays a key role in the detection and characterization of adrenal lesions, with several imaging tools which can be employed by radiologists. A thorough knowledge of the imaging features of adrenal masses is essential to better characterize these lesions, avoiding a misinterpretation of imaging findings, which frequently overlap between benign and malignant conditions, thus helping clinicians and surgeons in the management of patients. The purpose of this paper is to provide an overview of the main imaging features of adrenal masses and tumor-like conditions recalling the strengths and weaknesses of imaging modalities commonly used in adrenal imaging.

To date, diffusion weighted imaging (DWI) is included in routine magnetic resonance imaging (MRI) protocols for several cancers. The real additive role of DWI lies in the “functional” information obtained by probing the free diffusivity of water molecules into intra and inter-cellular spaces that in tumors mainly depend on cellularity. Although DWI has not gained much space in some oncologic scenarios, this non-invasive tool is routinely used in clinical practice and still remains a hot research topic: it has been tested in almost all cancers to differentiate malignant from benign lesions, to distinguish different malignant histotypes or tumor grades, to predict and/or assess treatment responses, and to identify residual or recurrent tumors in follow-up examinations. In this review, we provide an up-to-date overview on the application of DWI in oncology.

Infection with the novel coronavirus SARS-CoV-2 may remain asymptomatic, leading to under-recognition of the related disease, COVID-19, and to incidental findings in nuclear imaging procedures performed for standard clinical indications. Here, we report about our local experience in a region with high COVID-19 prevalence and dynamically increasing infection rates. Methods: Within the 8 day period of March 16-24, 2020, hybrid imaging studies of asymptomatic patients who underwent ¹⁸F-FDG-PET/CT or ¹³¹I-SPECT/CT for standard oncologic indications at our institution in Brescia, Italy, were analyzed for findings suggestive of COVID-19. Presence, radiological features and metabolic activity of interstitial pneumonia were identified, correlated with subsequent short-term clinical course and described in a case series. Results: Six of 65 patients (9%) that underwent PET/CT for various malignancies showed unexpected signs of interstitial pneumonia on CT and elevated regional FDG-avidity. Additionally, 1 of 12 patients who received radioiodine for differentiated thyroid carcinoma also showed interstitial pneumonia on SPECT/CT. 5/7 patients had subsequent proof of COVID-19 by RT-PCR. The remaining 2 patients were not tested immediately but underwent quarantine and careful monitoring. Conclusion: Incidental findings suggestive of COVID-19 may not be infrequent in hybrid imaging of asymptomatic patients, in regions with expansive spread of SARS-CoV-2. Nuclear medicine services should prepare accordingly.

Background: The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). Materials and methods: An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. Results: Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3–0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. Conclusion: AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. Protocol: PROSPERO identifier: CRD42023470708