Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance

Purpose To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI). Material and methods This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consisted of stability-based (ICC > 0.75) and significance-based selections (ranking features by decreasing Mann-Whitney p-value). Class balancing was performed to oversample the minority (i.e., benign) class. Selected features were used to train and test a support vector machine (SVM) to discriminate benign from malignant spine tumors using tenfold cross-validation. Results A total of 76.4% radiomic features were stable. The quality metrics for the SVM were evaluated as a function of the number of selected features. The radiomic model with the best performance and the lowest number of features for classifying tumor types included 8 features. The metrics were 78% sensitivity, 68% specificity, 76% accuracy and AUC 0.78. Conclusion SVM classifiers based on radiomic features extracted from T2- and diffusion-weighted imaging with ADC map are promising for classification of spine bone tumors. Radiomic features of spine bone tumors show good reproducibility rates

Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors

This study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied to both 2D and 3D segmentations to evaluate the influence of segmentation margins. A total of 783 and 1132 features were extracted from original and filtered 2D and 3D images, respectively. Intraclass correlation coefficient ≥ 0.75 defined feature stability. In 2D vs. 3D contour-focused segmentation, the rates of stable features were 74.71% vs. 86.57% ( p < 0.001), 77.14% vs. 80.04% ( p = 0.142), and 95.66% vs. 94.97% ( p = 0.554) for CT and T1-weighted and T2-weighted images, respectively. Margin shrinkage did not improve 2D ( p = 0.343) and performed worse than 3D ( p < 0.001) contour-focused segmentation in terms of feature stability. In 2D vs. 3D contour-focused segmentation, matching stable features derived from CT and MRI were 65.8% vs. 68.7% ( p = 0.191), and those derived from T1-weighted and T2-weighted images were 76.0% vs. 78.2% ( p = 0.285). 2D and 3D radiomic features of cartilaginous bone tumors extracted from unenhanced CT and MRI are reproducible, although some degree of interobserver segmentation variability highlights the need for reliability analysis in future studies

[Basic symptoms and neurocognition: preliminary comparison of first-episode psychosis vs multi-episode long-term illness].

Schizophrenia is preceded by basic symptoms which may persist after long time and include subjective cognitive impairment. Furthermore, it is characterised by cognitive deficits that may deteriorate with the progression of illness. To examine the relationship between neurocognition and basic symptoms along the course of schizophrenia, we compared the cognitive performance and the basic symptoms of one population with first episode psychosis (FEP) and one with a chronic, multi-episode course (MEP).We tested 8 FEP (5 male) and 7 MEP (7 male) in- and outpatients, for basic symptoms with the Schizophrenia Proneness Instrument-Adult version (SPI-A) and for neurocognition with Raven's Color Progressive Matrices (CPM), Rey-Osterrieth's complex figure (Rey), Corsi's and Buschke-Fuld tests, the Wisconsin Card Sorting Test (WCST), the Stroop test, and the Trail Making Test (TMT).FEP patients did not differ from MEP patients as for SPI-A scores. MEP patients were significantly more impaired on several subtests of Buschke-Fuld, the Rey, and the WCST with respect to FEP. Impairment on the cognitive subscale of the SPI-A correlated with non-perseverative WCST errors, and on the self subscale of the SPI-A with impaired performance on the Buschke-Fuld. Further, in MEP, impairment on the body subscale of the SPI-A correlated inversely with number of categories completed of the WCST.Basic symptoms persist throughout the phases of schizophrenia and are relatively independent of cognitive performance. A chronic, multi-episode course is associated with increased cognitive impairment in schizophrenia

Linguaggi per una scuola inclusiva

I contributi presenti in Linguaggi per una scuola inclusiva si collocano dentro la cornice dell\u2019individualizzazione e personalizzazione degli insegnamenti/apprendi\uad menti. La finalit\ue0 della presente pubblicazione \ue8 proporre ai docenti, attraverso il rac\uadconto di esperienze concrete o riflessioni, alcuni linguaggi e strumenti da integrare nella propria didattica. Tali contributi offrono al lettore suggestioni e spunti di ri\uad flessione, dai quali poter partire per una progettazione pi\uf9 inclusiva. Nello specifi\uadco, i primi due saggi presentano il paradigma inclusivo e delineano la cornice con\uadcettuale di riferimento al fine di fornire alcune chiavi di lettura dei saggi che sono stati raggruppati nelle seguenti parti: i nuovi linguaggi della lettura; i linguaggi dell\u2019espressivit\ue0 e della relazione; i linguaggi del gioco e del videogioco; la scuola e i linguaggi digitali

The role of radiomics in tongue cancer: A new tool for prognosis prediction

Background: Radiomics represents an emerging field of precision-medicine. Its application in head and neck is still at the beginning. Methods: Retrospective study about magnetic resonance imaging (MRI) based radiomics in oral tongue squamous cell carcinoma (OTSCC) surgically treated (2010-2019; 79 patients). All preoperative MRIs include different sequences (T1, T2, DWI, ADC). Tumor volume was manually segmented and exported to radiomic-software, to perform feature extraction. Statistically significant variables were included in multivariable analysis and related to survival endpoints. Predictive models were elaborated (clinical, radiomic, clinical-radiomic models) and compared using C-index. Results: In almost all clinical-radiomic models radiomic-score maintained statistical significance. In all cases C-index was higher in clinical-radiomic models than in clinical ones. ADC provided the best fit to the models (C-index 0.98, 0.86, 0.84 in loco-regional recurrence, cause-specific mortality, overall survival, respectively). Conclusion: MRI-based radiomics in OTSCC represents a promising noninvasive method of precision medicine, improving prognosis prediction before surgery