Scoliosis is a complex three dimensional (3D) deformity: the current lack of a 3D classification could hide something fundamental for scoliosis prognosis and treatment. A clear picture of the actually existing 3D classifications lacks. The aim of this systematic review was to identify all the 3D classification systems proposed until now in the literature with the aim to identify similarities and differences mainly in a clinical perspective. After a MEDLINE Data Base review, done in November 2013 using the search terms “Scoliosis/classification” [Mesh] and “scoliosis/classification and Imaging, three dimensional” [Mesh], 8 papers were included with a total of 1164 scoliosis patients, 23 hyperkyphosis and 25 controls, aged between 8 and 20 years, with curves from 10° to 81° Cobb, and various curve patterns. Six studies looked at the whole 3D spine and found classificatory parameters according to planes, angles and rotations, including: Plane of Maximal Curvature (PMC), Best Fit Plane, Cobb angles in bodily plane and PMC, Axial rotation of the apical vertebra and of the PMC, and geometric 3D torsion. Two studies used the regional (spinal) Top View of the spine and found classificatory parameters according to its geometrical properties (area, direction and barycenter) including: Ratio of the frontal and the sagittal size, Phase, Directions (total, thoracic and lumbar), and Shift. It was possible to find similarities among 10 out of the 16 the sub-groups identified by different authors with different methods in different populations. In summation, the state of the art of 3D classification systems include 8 studies which showed some comparability, even though of low level. The most useful one in clinical everyday practice, is far from being defined. More than 20 years passed since the definition of the third dimension of the scoliosis deformity, now the time has come for clinicians and bioengineers to start some real clinical application, and develop means to make this approach an everyday tool.

We present two cases of caseous calcification of the mitral annulus studied by Cardiovascular Magnetic Resonance; the diagnostic feature of this rare cardiac mass are described.

Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2–3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.

The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx^®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.