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Background: Interventional radiology techniques have become pivotal in recent years in managing metastatic bone disease, which frequently results in skeletal complications such as fractures and severe pain. Thermoablative methods like radiofrequency ablation (RFA) and microwave ablation (MWA), when combined with vertebroplasty (VP), are proving increasingly beneficial for these patients. Methods: The search was independently conducted by two radiologists on MEDLINE databases, using specified strings up to April 2024. Methodological quality was assessed using PRISMA guidelines. Studies meeting inclusion criteria investigated thermoablation techniques (RFA and/or MWA) combined with VP, focusing on pain management and disease control outcomes in adults. Results: Among 147 results, 42 articles met the criteria, with varied prospective and retrospective designs and sample sizes averaging 49 patients, predominantly involving RFA (30 studies), MWA (11 studies), and one comparative study. Our review highlights significant pain reduction, effective local tumor control, and favorable safety of combined RFA or MWA with VP, supporting its potential in managing vertebral pathologies and warranting further clinical integration. Conclusions: The combined treatment of RFA/MWA with VP demonstrates significant pain reduction and local tumor control, with a rapid onset of analgesic effect. These findings support its crucial role in clinical practice for managing vertebral metastases. [ABSTRACT FROM AUTHOR]

Simple Summary: Our study aims to demonstrate and quantify the usefulness of three different AI models for lymph node involvement prediction in prostate cancer patients comparing the performance with current routinely used clinical nomograms. Knowing the lymph node involvement with accuracy and confidence can influence the type of surgical and therapeutic choice. PURPOSE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients. MATERIALS AND METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared. RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences. CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction. [ABSTRACT FROM AUTHOR]

Simple Summary: Bone metastases represent a serious and common challenge in advanced cancer patients, making early detection crucial. The growing implementation of artificial intelligence (AI) in various medical fields is revolutionizing healthcare by enhancing diagnostic accuracy. This systematic review includes 59 articles and sought to assess the potential of machine learning models in the fields of nuclear medicine, clinical research, radiology, and molecular biology. The review provides a comprehensive analysis of AI's effectiveness in detecting bone metastases, which had not yet been thoroughly conducted. The findings highlight the benefits of integrating AI technologies in the inspected fields, encouraging further advancements and adoption of AI to ultimately improve patient care and treatment planning. This study provides invaluable perceptions into the promising application of computational intelligence in the detection of bone metastases, demonstrating its superiority over standard tests. However, further research is necessary to substantiate these findings with the aim of developing better-trained data-driven models soon. Background: Metastasis commonly occur in the bone tissue. Artificial intelligence (AI) has become increasingly prevalent in the medical sector as support in decision-making, diagnosis, and treatment processes. The objective of this systematic review was to assess the reliability of AI systems in clinical, radiological, and pathological aspects of bone metastases. Methods: We included studies that evaluated the use of AI applications in patients affected by bone metastases. Two reviewers performed a digital search on 31 December 2023 on PubMed, Scopus, and Cochrane library and extracted authors, AI method, interest area, main modalities used, and main objectives from the included studies. Results: We included 59 studies that analyzed the contribution of computational intelligence in diagnosing or forecasting outcomes in patients with bone metastasis. Six studies were specific for spine metastasis. The study involved nuclear medicine (44.1%), clinical research (28.8%), radiology (20.4%), or molecular biology (6.8%). When a primary tumor was reported, prostate cancer was the most common, followed by lung, breast, and kidney. Conclusions: Appropriately trained AI models may be very useful in merging information to achieve an overall improved diagnostic accuracy and treatment for metastasis in the bone. Nevertheless, there are still concerns with the use of AI systems in medical settings. Ethical considerations and legal issues must be addressed to facilitate the safe and regulated adoption of AI technologies. The limitations of the study comprise a stronger emphasis on early detection rather than tumor management and prognosis as well as a high heterogeneity for type of tumor, AI technology and radiological techniques, pathology, or laboratory samples involved. [ABSTRACT FROM AUTHOR]

Despite cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) being the current gold standard for non-invasive myocardial characterization and fibrosis quantification, its accessibility is limited, particularly in acute settings and in certain patient populations with contraindications to magnetic resonance imaging. Late iodine enhancement (LIE) in computed tomography (CT) imaging has emerged as a potential alternative, capitalizing on the similarities in the contrast kinetics between gadolinium and iodinated contrast agents. Studies have investigated LIE-CT's effectiveness in myocardial infarction (MI) detection, revealing promising outcomes alongside some disparities compared to LGE-CMR. LIE-CT also proves beneficial in diagnosing non-ischemic heart diseases such as myocarditis, hypertrophic cardiomyopathy, and sarcoidosis. While LIE-CT demonstrates good accuracy in detecting certain myocardial pathologies, including acute MI and chronic fibrotic changes, it has limitations, such as the inability to detect diffuse myocardial enhancement. Nonetheless, thanks to the availability of optimized protocols with minimal radiation doses and contrast medium administration, integrating LIE-CT into cardiac CT protocols could enhance its clinical utility, particularly in acute settings, providing valuable prognostic and management insights across a spectrum of cardiac ischemic and non-ischemic conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: This systematic review explores the relationship between obesity and renal-cell carcinoma (RCC), focusing on how different types of fat measurements influence the behavior and prognosis of the disease. Despite some studies suggesting a protective role of certain fat deposits, particularly visceral adipose tissue (VAT), conflicting findings exist across different adipose metrics and RCC subtypes. Methodological variations and limitations, such as retrospective designs and sample size constraints, pose challenges to standardization and generalizability. Further research efforts are needed to understand these associations better and establish standardized approaches for adiposity assessment in RCC patients, which could inform clinical practice and therapeutic decision-making. (1) Background: Renal-cell carcinoma (RCC) incidence has been steadily rising, with obesity identified as a potential risk factor. However, the relationship between obesity and RCC prognosis remains unclear. This systematic review aims to investigate the impact of different adipose tissue measurements on RCC behavior and prognosis. (2) Methods: A search of MEDLINE databases identified 20 eligible studies focusing on various fat measurements, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), perirenal adipose tissue (PRAT), and the Mayo adhesive probability (MAP) score. (3) Results: The review revealed conflicting findings regarding the association between adipose tissue measurements and RCC outcomes. While some studies suggested a protective role of certain fat deposits, particularly VAT, against disease progression and mortality, others reported contradictory results across different adipose metrics and RCC subtypes. (4) Conclusions: Methodological variations and limitations, such as retrospective designs and sample size constraints, pose challenges to standardization and generalizability. Further research is needed to understand these associations better and establish standardized approaches for adiposity assessment in RCC patients, which could inform clinical practice and therapeutic decision-making. [ABSTRACT FROM AUTHOR]