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Deposition of amyloid in the buttock is a rare complication of dialysis related amyloidosis (DRA), but this localization is even rarer in other types of amyloidoses. We report here the clinical, radiological, and biochemical features of a patient who incurred into this complication after 27 years of hemodialysis. Imaging of the amyloid deposition by magnetic resonance imaging (MRI) documents the amyloid infiltration in the muscles of the buttock region and highlights a peculiar feature of amyloid fibrils deposition in the subcutaneous fat. The amyloid deposition is confirmed by biochemical and microscopic analysis of fibrils extracted from a biopsy specimen. Review of literature and the features of this case lead to speculation that the peculiar involvement of the buttock region including muscles and subcutaneous fat in DRA might derive from the propagation of amyloid initially deposited in the hip joint. [ABSTRACT FROM AUTHOR]

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model's use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior. [ABSTRACT FROM AUTHOR]

Objectives: Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa). Methods: This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis. Results: Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94. Conclusions: MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice. Key Points: • A radiomic model was used to classify PCa aggressiveness. • Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. • The most predictive features belong to the texture (57%) and intensity (43%) domains. [ABSTRACT FROM AUTHOR]

Objectives: To assess the diagnostic accuracy of MRI performed using surface coils, with and without contrast medium, in predicting thyroid and cricoid cartilage infiltration in laryngeal tumours, and to investigate whether the radiologist's experience influences diagnostic accuracy.Methods: We retrospectively enrolled patients with biopsy-proven laryngeal cancer who had undergone preoperative staging MRI and open surgery. Two radiologists with different experience (senior vs. junior) reviewed the MR images without (session A1) and with contrast medium (session A2) separately. We calculated the accuracy of MRI with and without contrast medium in detecting infiltration of the thyroid and cricoid cartilages. Interobserver agreement was calculated by Cohen's Kappa (k).Results: Forty-two patients were enrolled, for a total of 62 cartilages. In session A1 the senior and junior radiologists showed an accuracy of 85% and 71%, respectively, with k = 0.53 (0.33-0.72). In session A2 the senior and junior radiologists showed an accuracy of 84% and 77%, respectively, with k = 0.68 (0.49-0.86).Conclusions: Staging of laryngeal tumours with surface coil MRI showed good diagnostic accuracy in assessing cartilaginous infiltration. We observed similar values of diagnostic accuracy for the analysis performed with and without contrast medium for the senior radiologist.Key Points: • Surface coil MRI demonstrated good accuracy in assessing laryngeal cartilage invasion. • The radiologist's experience can influence the diagnostic accuracy. • Gadolinium administration may increase interobserver concordance. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether there is an increased signal intensity (SI) of dentate nucleus (DN) and globus pallidus (GP) on unenhanced T1-weighted magnetic resonance imaging (MRI), in patients who had undergone multiple administrations of gadoxetate disodium.Materials and Methods: We retrospectevely included stage III melanoma patients, who had been previously enrolled in a trial of adjuvant therapy and who had undergone whole-body contrast-enhanced MRIs with gadoxetate disodium every three months for their follow-up. The SI ratios of DN-to-pons and GP-to-thalamus on unenhanced T1-weighted images were calculated. The difference in SI ratios between the first and the last MRI examinations was assessed and a linear mixed model was performed to detect how SI ratios varied with the number of administrations.Results: Eighteen patients were included in our study. The number of gadoxetate disodium administrations ranged from 2 to 18. Paired t-test did not show any significant difference in DN-to-pons (p=0.21) and GP-to-thalamus (p=0.09) SI ratios by the end of the study. DN-to-pons SI ratio and GP-to-thalamus SI ratio did not significantly increase with increasing the number of administrations (p=0.14 and p=0.06, respectively).Conclusion: Multiple administrations of gadoxetate disodium are not associated with increased SI in DN and GP in the brain.Key Points: • Gadolinium may deposit in the human brain after multiple GBCA administrations. • Gadolinium deposition is associated with increased T1W signal intensity • Increase in signal intensity is most apparent within the DN and GP • Multiple administrations of gadoxetate disodium do not increase T1W signal. [ABSTRACT FROM AUTHOR]

[Display omitted] • Using different platforms for radiomic extraction affects models' performance. • Variables' relevance is inconsistent among platforms. • MRI features are correlated to radiosurgery response in brain metastases from NSCLC. • Higher number of radiomic features does not necessarily imply better performance. Radiomics enables the mining of quantitative features from medical images. The influence of the radiomic feature extraction software on the final performance of models is still a poorly understood topic. This study aimed to investigate the ability of radiomic features extracted by two different radiomic platforms to predict clinical outcomes in patients treated with radiosurgery for brain metastases from non-small cell lung cancer. We developed models integrating pre-treatment magnetic resonance imaging (MRI)-derived radiomic features and clinical data. Pre-radiotherapy gadolinium enhanced axial T1-weighted MRI scans were used. MRI images were re-sampled, intensity-shifted, and histogram-matched before radiomic extraction by means of two different platforms (PyRadiomics and SOPHiA Radiomics). We adopted LASSO Cox regression models for multivariable analyses by creating radiomic, clinical, and combined models using three survival clinical endpoints (local control, distant progression, and overall survival). The statistical analysis was repeated 50 times with different random seeds and the median concordance index was used as performance metric of the models. We analysed 276 metastases from 148 patients. The use of the two platforms resulted in differences in both the quality and the number of extractable features. That led to mismatches in terms of end-to-end performance, statistical significance of radiomic scores, and clinical covariates found significant in combined models. This study shed new light on how extracting radiomic features from the same images using two different platforms could yield several discrepancies. That may lead to acute consequences on drawing conclusions, comparing results across the literature, and translating radiomics into clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: To assess the independent prognostic value of standardized uptake value (SUV) and apparent diffusion coefficient (ADC), separately and combined, in order to evaluate if the combination of these two variables allows further prognostic stratification of patients with head and neck squamous cell carcinomas (HNSCC).Methods: Pretreatment SUV and ADC were calculated in 57 patients with HNSCC. Mean follow-up was 21.3 months. Semiquantitative analysis of primary tumours was performed using SUVmaxT/B, ADCmean, ADCmin and ADCmax. The prognostic value of SUVmaxT/B, ADCmean, ADCmin and ADCmax in predicting disease-free survival (DFS) was evaluated with log-rank test and Cox regression models.Results: Patients with SUVmaxT/B ≥5.75 had an overall worse prognosis (p = 0.003). After adjusting for lymph node status and diameter, SUVmaxT/B and ADCmin were both significant predictors of DFS with hazard ratio (HR) = 10.37 (95 % CI 1.22-87.95) and 3.26 (95 % CI 1.20-8.85) for SUVmaxT/B ≥5.75 and ADCmin ≥0.58 × 10-3 mm2/s, respectively. When the analysis was restricted to subjects with SUVmaxT/B ≥5.75, high ADCmin significantly predicted a worse prognosis, with adjusted HR = 3.11 (95 % CI 1.13-8.55).Conclusions: The combination of SUVmaxT/B and ADCmin improves the prognostic role of the two separate parameters; patients with high SUVmaxT/B and high ADCmin are associated with a poor prognosis.Key Points: • High SUV maxT/B is a poor prognostic factor in HNSCC • High ADC min is a poor prognostic factor in HNSCC • In patients with high SUV maxT/B , high ADC min identified those with worse prognosis. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate the prognostic performance of multiparametric magnetic resonance imaging (mpMRI) and Prostate Imaging Reporting and Data System (PIRADS) score in predicting pathologic features in a cohort of patients eligible for active surveillance who underwent radical prostatectomy. Methods: A total of 223 patients who fulfilled the criteria for “Prostate Cancer Research International: Active Surveillance”, were included. Mp–1.5 Tesla MRI examination staging with endorectal coil was performed at least 6–8 weeks after TRUS-guided biopsy. In all patients, the likelihood of the presence of cancer was assigned using PIRADS score between 1 and 5. Outcomes of interest were: Gleason score upgrading, extra capsular extension (ECE), unfavorable prognosis (occurrence of both upgrading and ECE), large tumor volume (≥0.5ml), and seminal vesicle invasion (SVI). Receiver Operating Characteristic (ROC) curves and Decision Curve Analyses (DCA) were performed for models with and without inclusion of PIRADS score. Results: Multivariate analysis demonstrated the association of PIRADS score with upgrading (P<0.0001), ECE (P<0.0001), unfavorable prognosis (P<0.0001), and large tumor volume (P = 0.002). ROC curves and DCA showed that models including PIRADS score resulted in greater net benefit for almost all the outcomes of interest, with the only exception of SVI. Conclusions: mpMRI and PIRADS scoring are feasible tools in clinical setting and could be used as decision-support systems for a more accurate selection of patients eligible for AS. [ABSTRACT FROM AUTHOR]

Simple Summary: Nowadays, the only widely recognized method for evaluating the efficacy of neoadjuvant chemotherapy is the assessment of the pathological response through surgery. However, delivering chemotherapy to not-responders could expose them to unnecessary drug toxicity with delayed access to other potentially effective therapies. Radiomics could be useful in the early detection of resistance to chemotherapy, which is crucial for switching treatment strategy. We determined whether tumor radiomic features extracted from a highly homogeneous database of breast MRI can improve the prediction of response to chemotherapy in patients with breast cancer, in addiction to biological characteristics, potentially avoiding unnecessary treatment. Objectives: We aimed to determine whether radiomic features extracted from a highly homogeneous database of breast MRI could non-invasively predict pathological complete responses (pCR) to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Methods: One hundred patients with breast cancer receiving NACT in a single center (01/2017–06/2019) and undergoing breast MRI were retrospectively evaluated. For each patient, radiomic features were extracted within the biopsy-proven tumor on T1-weighted (T1-w) contrast-enhanced MRI performed before NACT. The pCR to NACT was determined based on the final surgical specimen. The association of clinical/biological and radiomic features with response to NACT was evaluated by univariate and multivariable analysis by using random forest and logistic regression. The performances of all models were assessed using the areas under the receiver operating characteristic curves (AUC) with 95% confidence intervals (CI). Results: Eighty-three patients (mean (SD) age, 47.26 (8.6) years) were included. Patients with HER2+, basal-like molecular subtypes and Ki67 ≥ 20% presented a pCR to NACT more frequently; the clinical/biological model's AUC (95% CI) was 0.81 (0.71–0.90). Using 136 representative radiomics features selected through cluster analysis from the 1037 extracted features, a radiomic score was calculated to predict the response to NACT, with AUC (95% CI): 0.64 (0.51–0.75). After combining the clinical/biological and radiomics models, the AUC (95% CI) was 0.83 (0.73–0.92). Conclusions: MRI-based radiomic features slightly improved the pre-treatment prediction of pCR to NACT, in addiction to biological characteristics. If confirmed on larger cohorts, it could be helpful to identify such patients, to avoid unnecessary treatment. [ABSTRACT FROM AUTHOR]