Primary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), a number of patients do not respond to HD–MTX-based chemotherapy (15–25%) or experience relapse (25–50%) after an initial response. The reasons underlying this poor response to therapy are unknown. Thus, there is an urgent need to develop improved predictive models for PCNSL. In this study, we investigated whether radiomics features can improve outcome prediction in patients with PCNSL. A total of 80 patients diagnosed with PCNSL were enrolled. A patient sub-group, with complete Magnetic Resonance Imaging (MRI) series, were selected for the stratification analysis. Following radiomics feature extraction and selection, different Machine Learning (ML) models were tested for OS and Progression-free Survival (PFS) prediction. To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and to evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images were significantly more stable than those extracted from non-normalized images with an improvement of about 38% on average (p-value < ${10}^{-12}$). The area under the ROC curve (AUC) showed that radiomics-based prediction overcame prediction based on current clinical prognostic factors with an improvement of 23% for OS and 50% for PFS, respectively. These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choice.

Background: Extraction of mandibular third molars (M3Ms) is a routine procedure in oral and maxillofacial surgery, often associated with postoperative symptoms like pain, facial swelling, and trismus. This study aimed to introduce a standardized and automated protocol for swelling analysis following M3M surgery, presenting results regarding clinical conditions immediately and one-week after surgery. Methods: In a prospective study, 35 patients were enrolled (mean age: 24.4 ± 5.8 years) for removal of 54 M3Ms. Facial swelling was evaluated through 3D facial scans before surgery (T0), at three days (T1), and seven days (T2) post-surgery. The open-source software 3DSlicer facilitated automated analysis, including data anonymization, orientation, surface registration, qualitative comparisons, linear measurements, and volumetric quantification. Pairwise superimposition of facial models enabled qualitative, vectorial, and quantitative assessments, comparing initial conditions with swelling development at T1 and T2. Additionally, changes between T1 and T2 were also evaluated. Secondary outcomes encompassed clinical evaluations of pain, trismus (maximum mouth opening), and surgery time. Statistical analysis involved the paired Student t-test to assess longitudinal changes and analysis of variance to evaluate outcome variables concerning difficulty scores. Linear regression models correlated primary outcome variables with secondary study variables (α < 0.05). Results: Longitudinal analysis demonstrated significant but variable facial swelling, pain, and trismus at T1, followed by improvement at T2 (p < 0.001). Linear and volumetric differences correlated positively with surgery time (p < 0.05). A direct proportionality between linear and volume differences was observed, higher values at T1 correlated with higher values at T2 (p < 0.05). Conclusions: An innovative digital workflow for precise quantification of postoperative facial changes was implemented, incorporating volumetric measurements that surpass linear assessments. Clinical conditions demonstrated a direct correlation with surgery time, deteriorating immediately and improving one-week after surgery.

Radiomics extracts quantitative features from magnetic resonance images (MRI) and is especially useful in the presence of subtle pathological changes within human soft tissues. This scenario, however, may not cover the effects of intrinsic, e.g., aging-related, (physiological) neurodegeneration of normal brain tissue. The aim of the work was to study the repeatability of radiomic features extracted from an apparently normal area in longitudinally acquired T1-weighted MR brain images using three different intensity normalization approaches typically used in radiomics: Z-score, WhiteStripe and Nyul. Fifty-nine images of hearing impaired, yet cognitively intact, patients were repeatedly acquired in two different time points within six months. Ninety-one radiomic features were obtained from an area within the pons region, considered to be a healthy brain tissue according to previous analyses and reports. The Intraclass Correlation Coefficient (ICC) and the Concordance Correlation Coefficient (CCC) in the repeatability study were used as metrics. Features extracted from the MRI normalized with Z-score showed results comparable in both ICC (0.90 (0.82-0.98)) and CCC (0.82 (0.69-0.95)) distribution values, in terms of median and quartiles, with those extracted from the images normalized with WhiteStripe (0.89 (0.84-0.92)) and (0.80 (0.73-0.84)), respectively. Our findings underline the importance of, providing useful guidelines for, the intensity normalization of brain MRI prior to a longitudinal radiomic analysis.

Coronary artery disease (CAD) is the most common cause of death globally. Typically, it occurs when part of the coronary artery develops atherosclerosis, which causes the narrowing of the vessel lumen. The decision to be taken to treat the stenotic artery vessel is often influenced by several factors. The aim of this study is to develop an application (app) to provide support to the physician in decision-making. The app has been developed with an Application Builder based on Java, as a result of the previous simulation of computational fluid dynamics (CFD). By using a standard coronary anatomy, through the app it is possible to vary the degree of the stenosis to obtain information about the hemodynamic variations of not easily accessible parameters.

In this study, we investigated whether radiomics features can improve outcome prediction in patients with Primary Central Nervous System Lymphoma (PCNSL). 80 patients diagnosed with PCNSL were enrolled. Of these, 56 patients with complete Magnetic Resonance Imaging (MRI) series (including T1-weighted, T2-weighted, 3D-T1 with gadolinium, and FLAIR) were selected for the stratification analysis. Following radiomic feature extraction and selection, different Machine Learning (ML) models were tested for Overall Survival (OS) and Progression-free Survival (PFS) prediction, and accuracy was computed in terms of F1-score and area under ROC curve (AUC). To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images resulted significantly more stable (ICC=0.80) than those extracted from non-normalized images (ICC=0.58), p-value<10 −12. For the best classifier, F1-score resulted 0.83/0.77-0.86 (median/interquartile range), and 0.80/0.67-0.88, for OS and PFS respectively. On average, AUC showed that radiomics based prediction (AUC=0.86 for OS, AUC=0.84 for PFS) overcomes prediction based on current clinical prognostic factors (AUC=0.70 for OS, AUC=0.56 for PFS). These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choise.