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Background:Atypical cartilaginous tumour (ACT) and grade II chondrosarcoma (CS2) of long bones are respectively managed with watchful waiting or curettage and wide resection. Preoperatively, imaging diagnosis can be challenging due to interobserver variability and biopsy suffers from sample errors. The aim of this study is to determine diagnostic performance of MRI radiomics-based machine learning in differentiating ACT from CS2 of long bones. Methods:One-hundred-fifty-eight patients with surgically treated and histology-proven cartilaginous bone tumours were retrospectively included at two tertiary bone tumour centres. The training cohort consisted of 93 MRI scans from centre 1 (n=74 ACT; n=19 CS2). The external test cohort consisted of 65 MRI scans from centre 2 (n=45 ACT; n=20 CS2). Bidimensional segmentation was performed on T1-weighted MRI. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, a machine-learning classifier (Extra Trees Classifier) was tuned on the training cohort using 10-fold cross-validation and tested on the external test cohort. In centre 2, its performance was compared with an experienced musculoskeletal oncology radiologist using McNemar's test. Findings:After tuning on the training cohort (AUC=0.88), the machine-learning classifier had 92% accuracy (60/65, AUC=0.94) in identifying the lesions in the external test cohort. Its accuracies in correctly classifying ACT and CS2 were 98% (44/45) and 80% (16/20), respectively. The radiologist had 98% accuracy (64/65) with no difference compared to the classifier (p=0.134). Interpretation:Machine learning showed high accuracy in classifying ACT and CS2 of long bones based on MRI radiomic features. Funding:ESSR Young Researchers Grant.

In the last decades a variety of innovative craniofacial approaches has been adopted to entire skull base. The endonasal endoscopic route has emerged as a suitable methodology for several skull base lesions. An effective watertight closure is essential to isolate the intracranial cavity in order to restore the natural intra and extradural compartment division, necessary to prevent postoperative cerebrospinal fluid (CSF) leakage and complications such as meningitis, brain herniation, and tension pneumocephalus. The reconstruction can be performed using different materials, both autologous (autologous grafts) and non-autologous, individually or combined in a multilayer fashion. The harvesting a nasoseptal flap is one of the most effective techniques: it reinforces the skull base closure granting isolation of the surgical field. The current study was focused on the development of new advanced devices and techniques, aiding in reducing postoperative CSF leak, which is one of the most feared complication of this surgical procedure. Additive manufacturing allows to design devices with tailored structural and functional features, in order to satisfy all the requirements. On the other hand, the development of injectable semi-IPNs and composites clearly benefits from specific mechanical/rheological and injectability studies. Accordingly, starting from some basic concepts, innovative principles and strategies were also proposed towards the design of additively manufactured and injectable devices.

Machine learning (ML) is a software solution with the ability of making predictions without prior explicit programming, aiding in the analysis of large amounts of data. These algorithms can be trained through supervised or unsupervised learning. Cardiology is one of the fields of medicine with the highest interest in its applications. They can facilitate every step of patient care, reducing the margin of error and contributing to precision medicine. In particular, ML has been proposed for cardiac imaging applications such as automated computation of scores, differentiation of prognostic phenotypes, quantification of heart function and segmentation of the heart. These tools have also demonstrated the capability of performing early and accurate detection of anomalies in electrocardiographic exams. ML algorithms can also contribute to cardiovascular risk assessment in different settings and perform predictions of cardiovascular events. Another interesting research avenue in this field is represented by genomic assessment of cardiovascular diseases. Therefore, ML could aid in making earlier diagnosis of disease, develop patient-tailored therapies and identify predictive characteristics in different pathologic conditions, leading to precision cardiology.

Background: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest. Purpose/Hypothesis: To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach. Study Type: Retrospective, observational study. Population/Subjects/Phantom/Specimen/Animal Model: Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. Field Strength/Sequence: Unenhanced T -weighted in-phase (IP) and out-of-phase (OP) as well as T -weighted (T -w) MR images acquired at 3T. Assessment: Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T -w images. Different selection methods were trained and tested using the J48 machine-learning classifiers. Statistical Tests: The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. Results: A total of 138 TA-derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and T -w (Standard_Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist. Data Conclusion: Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions. Level of Evidence: 4. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018.

The effects were studied of two i.v. anaesthetics (Althesin and ketamine) on resting and activated adrenocortical function in the rat. Small doses of Althesin and ketamine comparable to those required to produce induction of anaesthesia in man evoked adrenocortical stimulation, but the stressing effects of ketamine were more prolonged. During deep anaesthesia with either anaesthetic there was an inhibition of pituitary adrenal activation induced by handling and surgery. However, during recovery, ketamine was associated with intense and prolonged adrenocortical stimulation. The stress of laparotomy in control rats and in rats pretreated with the anaesthetics caused a depletion of noradrenaline in the hypothalamus; this suggests that both anaesthetics inhibit adrenocortical activation by mechanisms different from adrenergic inhibition.

Purpose: To test whether T2 mapping of the sacro-iliac joints (SIJs) might help identifying patients with spondyloarthritis. Method: This study included 20 biologic-naive patients with axial spondyloarthritis (10 females; mean age: 38 ± 9years; range, 19–47) and 27 controls (16 males; mean age = 39 ± 13years; range = 28–71) who prospectively underwent SIJs MRI at 1.5 T, including a multislice multiecho spin-echo sequence. Standard MRIs were reviewed to assess the SIJs according to the Assessment of SpondyloArthritis International Society (ASAS) criteria and SPondyloArthritis Research Consortium of Canada (SPARCC) MRI index. T2 maps obtained from multiecho sequences were used to draw regions of interests in the cartilaginous part of the SIJs. Disease activity was assessed using BASDAI questionnaire. Bland-Altman method, ROC curve analysis, Chi square, Mann-Whitney U, Pearson's and Spearman's correlation coefficient were used for data analysis. Results: According to ASAS criteria, MRI was positive for sacroiliitis in 5/20 patients (25 %). Inter-observer reproducibility of T2 values was 87 % (coefficient of repeatability = 7.0; bias = 0.49; p < .001). Mean T2 values of patients (58.5 ± 4.4 ms, range: 52.6–68.2 ms) were significantly higher (p < .001) than those of controls (44.1 ± 6.6 ms, range: 33.6–67.2 ms). A T2 value of 52.51 ms yielded 100 % sensitivity and 91.7 % specificity to differentiate patients from controls. No statistically significant association/correlation was found between T2 values and BASDAI (r=˗.026, p = .827), disease duration (r = .024, p = .871), SPARCC (r=-.004, p = .981), ASAS criteria (p = .476), HLA-B27-positivity (p = .139), age (r=-.2.53, p = .891), and gender (p = .404). Conclusions: T2 relaxation times of the SIJs were significantly higher in patients than in healthy controls, making this tool potentially helpful to early identify patients with spondyloarthritis.

Objectives To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality of CT and MRI radiomics studies. Methods A systematic search was conducted on PubMed/Medline, Web of Science, and Scopus databases to identify original studies assessing radiomics of cholangiocarcinoma on CT and/or MRI. Three readers with different experience levels independently assessed quality of the studies using the radiomics quality score (RQS). Subgroup analyses were performed according to journal type, year of publication, quartile and impact factor (from the Journal Citation Report database), type of cholangiocarcinoma, imaging modality, and number of patients. Results A total of 38 original studies including 6242 patients (median 134 patients) were selected. The median RQS was 9 (corresponding to 25.0% of the total RQS; IQR 1–13) for reader 1, 8 (22.2%, IQR 3–12) for reader 2, and 10 (27.8%; IQR 5–14) for reader 3. The inter-reader agreement was good with an ICC of 0.75 (95% CI 0.62–0.85) for the total RQS. All studies were retrospective and none of them had phantom assessment, imaging at multiple time points, nor performed cost-effectiveness analysis. The RQS was significantly higher in studies published in journals with impact factor > 4 (median 11 vs. 4, p = 0.048 for reader 1) and including more than 100 patients (median 11.5 vs. 0.5, p Conclusions Quality of radiomics studies on cholangiocarcinoma is insufficient based on the radiomics quality score. Future research should consider prospective studies with a standardized methodology, validation in multi-institutional external cohorts, and open science data.

BackgroundRecently, it was defined that the sellar barrier entity could be identified as a predictor of cerebrospinal fluid (CSF) intraoperative leakage. The aim of this study is to validate the application of the sellar barrier concept for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning approach.MethodsWe conducted a prospective cohort study, from June 2019 to September 2020: data from 155 patients with pituitary subdiaphragmatic adenoma operated through endoscopic approach at the Division of Neurosurgery, Università degli Studi di Napoli “Federico II,” were included. Preoperative magnetic resonance images (MRI) and intraoperative findings were analyzed. After processing patient data, the experiment was conducted as a novelty detection problem, splitting outliers (i.e., patients with intraoperative fistula, n = 11/155) and inliers into separate datasets, the latter further separated into training (n = 115/144) and inlier test (n = 29/144) datasets. The machine learning analysis was performed using different novelty detection algorithms [isolation forest, local outlier factor, one-class support vector machine (oSVM)], whose performance was assessed separately and as an ensemble on the inlier and outlier test sets.ResultsAccording to the type of sellar barrier, patients were classified into two groups, i.e., strong and weak barrier; a third category of mixed barrier was defined when a case was neither weak nor strong. Significant differences between the three datasets were found for Knosp classification score (p = 0.0015), MRI barrier: strong (p = 1.405 × 10−6), MRI barrier: weak (p = 4.487 × 10−8), intraoperative barrier: strong (p = 2.788 × 10−7), and intraoperative barrier: weak (p = 2.191 × 10−10). We recorded 11 cases of intraoperative leakage that occurred in the majority of patients presenting a weak sellar barrier (p = 4.487 × 10−8) at preoperative MRI. Accuracy, sensitivity, and specificity for outlier detection were 0.70, 0.64, and 0.72 for IF; 0.85, 0.45, and 1.00 for LOF; 0.83, 0.64, and 0.90 for oSVM; and 0.83, 0.55, and 0.93 for the ensemble, respectively.ConclusionsThere is a true correlation between the type of sellar barrier at MRI and its in vivo features as observed during endoscopic endonasal surgery. The novelty detection models highlighted differences between patients who developed an intraoperative CSF leak and those who did not.

Rationale and Objectives To evaluate an MRI radiomics-powered machine learning (ML) model's performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability. Materials and Methods Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars's test was employed to compare the two readings. Results Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48). Conclusion We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance.

Alagille syndrome (ALGS) is a multisystemic disease caused by mutations in genes of Notch pathway, which regulates embryonic cell differentiation and angiogenesis. Clinically, ALGS is characterized by cholestasis, cardiac defects, characteristic facial features, skeletal and ophthalmologic abnormalities. The aim of this review is to illustrate neuroradiological findings in ALGS, which are less well-known and prevalent, including cerebrovascular anomalies (such as aneurysms, dolichoectasia, Moyamoya syndrome and venous peculiarities), Chiari 1 malformation, craniosynostosis, intracranial hypertension, and vertebral anomalies (namely butterfly vertebra, hemivertebra, and craniocervical junction anomalies). Rarer cerebral midline malformations and temporal bone anomalies have also been described.

Purpose A strong prognostic score that enables a stratification of newly diagnosed Hodgkin Lymphoma (HL) to identify patients at high risk of refractory/relapsed disease is still needed. Our aim was to investigate the potential value of a radiomics analysis pipeline from whole-body MRI (WB-MRI) exams for clinical outcome prediction in patients with Hodgkin Lymphoma (HL). Materials and methods Index lesions from baseline WB-MRIs of 40 patients (22 females; mean age 31.7 ± 11.4 years) with newly diagnosed HL treated by ABVD chemotherapy regimen were manually segmented on T1-weighted, STIR, and DWI images for texture analysis feature extraction. A machine learning approach based on the Extra Trees classifier and incorporating clinical variables, 18F-FDG-PET/CT-derived metabolic tumor volume, and WB-MRI radiomics features was tested using cross-validation to predict refractory/relapsed disease. Results Relapsed disease was observed in 10/40 patients (25%), two of whom died due to progression of disease and graft versus host disease, while eight reached the complete remission. In total, 1403 clinical and radiomics features were extracted, of which 11 clinical variables and 171 radiomics parameters from both original and filtered images were selected. The 3 best performing Extra Trees classifier models obtained an equivalent highest mean accuracy of 0.78 and standard deviation of 0.09, with a mean AUC of 0.82 and standard deviation of 0.08. Conclusions Our preliminary results demonstrate that a combined machine learning and texture analysis model to predict refractory/relapsed HL on WB-MRI exams is feasible and may help in the clinical outcome prediction in HL patients.

Purpose: To assess the feasibility and reproducibility of T2 relaxation time measurements of the trapeziometacarpal joint (TM) and triangular fibrocartilage complex (TFCC) on healthy subjects at 1.5 T MR. Materials and methods: Thirty-four healthy volunteers underwent an axial oblique multislice multiecho spin-echo sequence of the wrist at 1.5 T, with 10 of them having performed another MR scan on a different 1.5 T scanner. Regions of interest were independently manually drawn by two musculoskeletal radiologists to include the cartilaginous part of the TM and TFCC. Intra-observer, inter-observer and inter-scanner reproducibility of T2 relaxation time measurements was tested using the Bland–Altman method. Results: The mean T2 values obtained by the two radiologists were 29.9 ± 6.5 ms and 30.0 ± 6.1 ms in the TM and 24.5 ± 2.3 ms and 24.6 ± 2.8 ms in the TFCC, respectively. The mean values of the second series of T2 measurements obtained by the senior radiologist were 29.9 ± 6.5 ms and 30.0 ± 6.3 ms in the TM and 24.3 ± 2.9 ms in the TFCC. Inter-observer reproducibility in the TM and in the TFCC was 76% and 82%, respectively. Intra-observer reproducibility in the TM and TFCC was 71% and 76%, respectively. Inter-scanner reproducibility of T2 measurements was 36% in the TM and 85% in the TFCC, respectively. Conclusion: The assessment of T2 relaxation time measurements of the cartilage of the TM and the TFCC seems to be feasible and reproducible, although the inter-scanner reproducibility of T2 measurements of the TM is suboptimal. Further studies including patients are warranted to prove the utility of this tool.

Background/Aim: The differentiation between cerebral metastases (CM) and high-grade gliomas (HGG) can be difficult on magnetic resonance imaging (MRI). The aim of this study was to evaluate the usefulness of searching two MRI signs (signal alteration in the adjacent cortex, SAAC, and peripheral rim sign, PRS), in order to distinguish between these entities. Patients and Methods: A total of 61 patients were retrospectively enrolled (28 HGG, 33 CM). Fluid Attenuated Inversion Recovery (FLAIR) sequences were used to assess SAAC and contrast-enhanced T1-weighted sequences for PRS. Results: A positive SAAC sign was present in 61% of HGG, and 12% of CM. Conversely, in SAAC-negative lesions, PRS was observed in 78% of CM and in 32% of HGG. Their association had a higher frequency in HGG than in the CM group (21 vs. 3%). Conclusion: While SAAC is specific for HGG and PRS, in the absence of SAAC, is relatively specific for CMs, their combined presence is highly suggestive of HGG.

Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker which correlates with pituitary adenoma aggressiveness. Aim of our study was to assess the accuracy of machine learning analysis of texture-derived parameters from pituitary adenomas preoperative MRI for the prediction of ki-67 proliferation index class. A total of 89 patients who underwent an endoscopic endonasal procedure for pituitary adenoma removal with available ki-67 labeling index were included. From T2w MR images, 1128 quantitative imaging features were extracted. To select the most informative features, different supervised feature selection methods were employed. Subsequently, a k-nearest neighbors (k-NN) classifier was employed to predict macroadenoma high or low proliferation index. Algorithm validation was performed with a train-test approach. Of the 12 subsets derived from feature selection, the best performing one was constituted by the 4 highest correlating parameters at Pearson’s test. These all showed very good (ICC ≥ 0.85) inter-observer reproducibility. The overall accuracy of the k-NN in the test group was of 91.67% (33/36) of correctly classified patients. Machine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.