Extra-axial cranial nerve enhancement: a pattern-based approach.

Cranial nerve enhancement is a common and challenging MRI finding that requires a meticulous and systematic evaluation to identify the correct diagnosis. Literature mainly describes the various pathologies with the associated clinic-radiological characteristics, while the radiologist often needs a reverse approach that starts from the radiological findings to reach the diagnosis. Therefore, our aim is to provide a new and practical pattern-based approach to cranial nerve enhancement, which starts from the radiological findings and follows pattern-driven pipelines to navigate through multiple differential diagnoses, guiding the radiologist to reach the proper diagnosis. Firstly, we reviewed the literature and identified four patterns to categorize the main pathologies presenting with cranial nerve enhancement: unilateral linear pattern, bilateral linear pattern, unilateral thickened pattern, and bilateral thickened pattern. For each pattern, we describe the underlying pathogenic origin, and the main radiological features are displayed through high-quality MRI images and illustrative panels. A suggested MRI protocol for studying cranial nerve enhancement is also provided. In conclusion, our approach for cranial nerve enhancement aims to be an easy tool immediately applicable to clinical practice for converting challenging findings into specific pathological patterns.

Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan.

New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >-275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs.

Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients.

Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection (FBP) were reconstructed with 10 levels of ASIR-V (10-100%). CT texture analysis (CTTA) of liver, kidney, spleen and paravertebral muscle for all datasets was performed. Six radiomic features (mean intensity, standard deviation (SD), entropy, mean of positive pixel (MPP), skewness, kurtosis) were extracted and compared between FBP and all ASIR-V levels, with and without altering the spatial scale filter (SSF). CTTA of all organs revealed significant differences between FBP and all ASIR-V reconstructions for mean intensity, SD, entropy and MPP (all p < 0.0001), while no significant differences were observed for skewness and kurtosis between FBP and all ASIR-V reconstructions (all p > 0.05). A per-filter analysis was also performed comparing FBP with all ASIR-V reconstructions for all six SSF separately (SSF0-SSF6). Results showed significant differences between FBP and all ASIR-V reconstruction levels for mean intensity, SD, and MPP (all filters p < 0.0315). Skewness and kurtosis showed no differences for all comparisons performed (all p > 0.05). The application of incremental ASIR-V levels affects CTTA across various filters. Skewness and kurtosis are not affected by IR and may be reliable quantitative parameters for radiomic analysis.