The PIGTAIL paradigm for a fast and safe transfemoral transcatheter aortic valve replacement.

Transfemoral transcatheter aortic valve replacement is the preferred primary access route whenever possible. Despite advancements in expertise and delivery system profiles, complications associated with the primary femoral access still significantly affect procedural morbidity and outcomes. The current standard for accurate main access planning involves proper preprocedural evaluation guided by computed tomography. Several baseline clinical and anatomical features serve as predictors for the risk of vascular injury occurring during or after transcatheter aortic valve replacement. In this paper, we aimed at reviewing the most up-to-date knowledge of the topic for a safe transfemoral access approach according to a paradigm we have called "PIGTAIL."

Endovascular Low-Pass Filter: Performing Mitral Edge-to-Edge Repair With PASCAL Through an Inferior Vena Cava Filter.

We present the case of an 82-year-old man with a history of inferior vena cava filter implantation and concomitant severe mitral regurgitation requiring transcatheter edge-to-edge repair. Despite being deemed ineligible for transfemoral access as technically challenging, he successfully underwent mitral transcatheter edge-to-edge repair after crossing and dilatation of the inferior vena cava filter. (Level of Difficulty: Intermediate.).

COVID-19 vaccination, implications for PET/CT image interpretation and future perspectives.

Introduction: The present paper aims to systematically review the literature on COVID-19 vaccine-related findings in patients undergoing PET/CT. Methods: The search algorithms included the following combination of terms: "PET" OR "positron emission tomography" AND "COVID"; "PET" OR "positron emission tomography" AND "COVID" AND "vaccination"; "PET" OR "positron emission tomography" AND "COVID", AND "autoimmune". Results: We selected 17 articles which were assessed for quality and included in the systematic analysis. The most frequent vaccine-related signs on PET/CT were the deltoid [18F]FDG uptake and axillary hypermetabolic lymph nodes, which were described in 8-71% and 7-90% of the patients, respectively. Similarly, frequency of these findings using other tracers than [18F]FDG was greatly variable. This large variability was related to the variability in time elapsed between vaccination and PET/CT, and the criteria used to define positivity. In addition, vaccine-related findings were detected more frequently in young and immunocompetent patients than in elderly and immunocompromised ones. Discussion: Therefore, awareness on vaccination status (timing, patient characteristics, and concurrent therapies) and knowledge on patterns of radiopharmaceutical uptake are necessary to properly interpret PET/CT findings.

Body composition assessment: comparison of quantitative values between magnetic resonance imaging and computed tomography.

BACKGROUND: The primary objective of this study was to compare measurements of skeletal muscle index (SMI), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) at the level of L3, on subjects who underwent computed tomography (CT) and magnetic resonance imaging (MRI) examinations within a three-month period. The secondary objective was to compare the automatic and semi-automatic quantifications of the same values for CT images. METHODS: Among subjects who underwent CT and MRI at our Institution between 2011 and 2020, exclusion criteria were: presence of extensive artifacts; images not including the whole waist circumference; CT acquired with low-dose technique and lack of non-contrast images. A set of three axial images (CT, MRI T1-weighted and T2-weighted) were used to extract the following measurements with semi-automatic segmentations: SMI [calculated normalizing skeletal muscle area (SMA) by the square height], SAT, VAT. For the CT images only, the same values were also calculated by using automatic segmentation. Statistical analysis was performed comparing quantitative MRI and CT measurements by Pearson correlation analysis and by Bland-Altman agreement analysis. RESULTS: A total of 123 patients were included. By performing linear regression analysis, CT and MRI measurements of SMI showed a high correlation (r2=0.81 for T1, r2=0.89 for T2), with a mean logarithmic difference between CT and MRI quantitative values of 0.041 for T1-weighted and 0.072 for T2-weighted images. CT and MRI measurements of SAT showed high correlation (r2=0.81 for T1; r2=0.81 for T2), with a mean logarithmic difference between CT and MRI values of 0.0174 for T1-weighted and 0.201 for T2-weighted images. CT and MRI measurements of VAT showed high correlation (r2=0.94 for T1; r2=0.93 for T2), with a mean logarithmic difference of 0.040 for T1-weighted and -0.084 for T2-weighted images. The comparison of values extracted by semi-automatic and automatic segmentations were highly correlated. CONCLUSIONS: Quantification of body composition values at MRI from T1-weighted and T2-weighted images was highly correlated to same values at CT, therefore quantitative values of body composition among patients who underwent either one of the examinations may be compared. CT body composition values extracted by semi-automatic and automatic segmentations showed high correlation.

Methodological framework for radiomics applications in Hodgkin's lymphoma.

BACKGROUND: According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet. PURPOSE: The study aimed at setting up a methodological framework in radiomics applications in Hodgkin's lymphoma (HL), especially at (a) developing a novel feature selection approach, (b) evaluating radiomic intra-patient lesions' similarity, and (c) classifying relapsing refractory (R/R) vs non-(R/R) patients. METHODS: We retrospectively included 85 patients (male:female = 52:33; median age 35 years, range 19-74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints that were tested to assess lesions' similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersampling Boosting of Tree Ensemble (RUBTE). RESULTS: HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity). CONCLUSIONS: Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R performed the best when all the lesions were used.