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(1) Background: The aim of this retrospective study is to assess safety and efficacy of lung radiofrequency (RFA) and microwave ablation (MWA) using an augmented reality computed tomography (CT) navigation system (SIRIO) and to compare it with the standard CT-guided technique. (2) Methods: Lung RFA and MWA were performed with an augmented reality CT 3D navigation system (SIRIO) in 52 patients. A comparison was then performed with a group of 49 patients undergoing the standard CT-guided technique. All the procedures were divided into four groups based on the lesion diameter (>2 cm or ≤2 cm), and procedural time, the number of CT scans, radiation dose administered, and complications rate were evaluated. Technical success was defined as the presence of a "ground glass" area completely covering the target lesion at the immediate post-procedural CT. (3) Results: Full technical success was achieved in all treated malignant lesions for all the considered groups. SIRIO-guided lung thermo-ablations (LTA) displayed a significant decrease in the number of CT scans, procedure time, and patients' radiation exposure ( p < 0.001). This also resulted in a dosage reduction in hypnotics and opioids administrated for sedation during LTA. No significant differences were observed between the SIRIO and non-SIRIO group in terms of complications incidence. (4) Conclusions: SIRIO is an efficient tool to perform CT-guided LTA, displaying a significant reduction ( p < 0.001) in the number of required CT scans, procedure time, and patients' radiation exposure.

(1) Background: The purpose of this study is to evaluate the impact of an augmented reality navigation system (SIRIO) for percutaneous biopsies and ablative treatments on bone lesions, compared to a standard CT-guided technique. (2) Methods: Bioptic and ablative procedures on bone lesions were retrospectively analyzed. All procedures were divided into SIRIO and Non-SIRIO groups and in <2 cm and >2 cm groups. Number of CT-scans, procedural time and patient's radiation dose were reported for each group. Diagnostic accuracy was obtained for bioptic procedures. (3) Results: One-hundred-ninety-three procedures were evaluated: 142 biopsies and 51 ablations. Seventy-four biopsy procedures were performed using SIRIO and 68 under standard CT-guidance; 27 ablative procedures were performed using SIRIO and 24 under standard CT-guidance. A statistically significant reduction in the number of CT-scans, procedural time and radiation dose was observed for percutaneous procedures performed using SIRIO, in both <2 cm and >2 cm groups. The greatest difference in all variables examined was found for procedures performed on lesions <2 cm. Higher diagnostic accuracy was found for all SIRIO-assisted biopsies. No major or minor complications occurred in any procedures. (4) Conclusions: The use of SIRIO significantly reduces the number of CT-scans, procedural time and patient's radiation dose in CT-guided percutaneous bone procedures, particularly for lesions <2 cm. An improvement in diagnostic accuracy was also achieved in SIRIO-assisted biopsies.

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis., Methods: We enrolled three groups: a pneumonia-free group ( n = 30), a COVID-19 group ( n = 34), and a group of patients with ICI therapy-related pneumonitis ( n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann-Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve)., Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97)., Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.