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The variance in intensities of MRI scans is a fundamental impediment for quantitative MRI analysis. Intensity values are not only highly dependent on acquisition parameters, but also on the subject and body region being scanned. This warrants the need for image normalization techniques to ensure that intensity values are consistent within tissues across different subjects and visits. Many intensity normalization methods have been developed and proven successful for the analysis of brain pathologies, but evaluation of these methods for images of the prostate region is lagging. In this paper, we compare four different normalization methods on 49 T2-w scans of prostate cancer patients: 1) the well-established histogram normalization, 2) the generalized scale normalization, 3) an extension of generalized scale normalization called generalized ball-scale normalization, and 4) a custom normalization based on healthy prostate tissue intensities. The methods are compared qualitatively and quantitatively in terms of behaviors of intensity distributions as well as impact on radiomic features. Our findings suggest that normalization based on prior knowledge of the healthy prostate tissue intensities may be the most effective way of acquiring the desired properties of normalized images. In addition, the histogram normalization method outperform the generalized scale and generalized ball-scale methods which have proven superior for other body regions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

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.

Background Diffusion-weighted imaging obtained with magnetic resonance (DW-MRI) is a non-invasive imaging tool potentially able to provide information about microstructural tumor characteristics. Purpose To prospectively analyze the correlation between the apparent diffusion coefficient (ADC) and clinical-histologic characteristics of squamous cell carcinoma (SCCA) of the oral cavity and oropharynx. Material and Methods Sixty-seven patients with untreated, histologically proven SCCA of the oral cavity and oropharynx underwent conventional and diffusion-weighted (b-values 0, 50, 250, 500, and 900 s/mm 2 ) MRI. Tumor ADC was calculated from regions of interest drawn manually on the highest b-value images using ImageJ (ImageJ, NIH) and fsl (fsl 4, University of Oxford) image processing packages. ADC was calculated in two ways: standard ADC using all b-values; and ADC High using only b-values ≥ 250 s/mm 2 . We assessed the correlations between both ADC and ADC High and the clinical-histological characteristics of SCCA. Results Fifty-two patients (36 men, 16 women; mean age, 55 ± 13 years) were suitable for ADC calculation. Mean ADC was 1136.0 ± 108.5 × 10 -6  mm 2 /s. Mean tumor ADC High was 991.2 ± 152.1 × 10 -6  mm 2 /s. Mean tumor size was 32.3 ± 13.4 mm (range, 14.0-69.0 mm). We observed no correlation of either ADC or ADC High values with any of the clinical-histological tumor characteristics. Undifferentiated tumors (G3) showed lower apparent diffusion coefficient values compared to differentiated ones (G1-G2), without reaching statistical significance. Conclusion We did not observe any statistically significant correlation between ADC values and clinical-histological characteristics of SCCA of the oral cavity and oropharynx.

Purpose: To investigate whether use of multiparametric magnetic resonance (MR) imaging-directed intraoperative frozen-section (IFS) analysis during nerve-sparing robot-assisted radical prostatectomy reduces the rate of positive surgical margins., Materials and Methods: This retrospective analysis of prospectively acquired data was approved by an institutional ethics committee, and the requirement for informed consent was waived. Data were reviewed for 134 patients who underwent preoperative multiparametric MR imaging (T2 weighted, diffusion weighted, and dynamic contrast-material enhanced) and nerve-sparing robot-assisted radical prostatectomy, during which IFS analysis was used, and secondary resections were performed when IFS results were positive for cancer. Control patients (n = 134) matched for age, prostate-specific antigen level, and stage were selected from a pool of 322 patients who underwent nerve-sparing robot-assisted radical prostatectomy without multiparametric MR imaging and IFS analysis. Rates of positive surgical margins were compared by means of the McNemar test, and a multivariate conditional logistic regression model was used to estimate the odds ratio of positive surgical margins for patients who underwent MR imaging and IFS analysis compared with control subjects., Results: Eighteen patients who underwent MR imaging and IFS analysis underwent secondary resections, and 13 of these patients were found to have negative surgical margins at final pathologic examination. Positive surgical margins were found less frequently in the patients who underwent MR imaging and IFS analysis than in control patients (7.5% vs 18.7%, P = .01). When the differences in risk factors are taken into account, patients who underwent MR imaging and IFS had one-seventh the risk of having positive surgical margins relative to control patients (adjusted odds ratio: 0.15; 95% confidence interval: 0.04, 0.61)., Conclusion: The significantly lower rate of positive surgical margins compared with that in control patients provides preliminary evidence of the positive clinical effect of multiparametric MR imaging-directed IFS analysis for patients who undergo prostatectomy., (© RSNA, 2014.)

Background: Implant rupture is one of the most important sequelae of breast reconstruction after mastectomy. The primary aim of this study was to compare magnetic resonance imaging and ultrasound evaluation with intraoperative findings and provide a reliable description of the occurrence of each radiological sign., Methods: The authors prospectively recruited a consecutive series of 102 postmastectomy patients requiring implant change for aesthetic purposes. Magnetic resonance imaging and ultrasound evaluation results were compared with intraoperative findings. Sensitivity, specificity, positive predictive value, negative predictive value, and the overall accuracy of magnetic resonance imaging and ultrasound in detecting ruptured implants were calculated, along with their corresponding 95 percent confidence intervals., Results: Magnetic resonance imaging performs better than ultrasound for diagnosis of breast implant rupture, with overall accuracies of 94 and 72 percent, respectively. The negative predictive value of ultrasound was 85 percent, meaning that in the case of negative ultrasound findings, magnetic resonance imaging may be avoided. Teardrop sign and water droplets are the most common findings on magnetic resonance imaging., Conclusions: Magnetic resonance imaging should be considered the method of choice for investigating silicone gel implant rupture in postmastectomy patients, and the standardization of magnetic resonance imaging criteria may improve magnetic resonance imaging accuracy. The authors therefore suggest a strategy of screening asymptomatic women with ultrasound every year and with magnetic resonance imaging every 5 years., Clinical Question/level of Evidence: Diagnostic, II.

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. [ABSTRACT FROM AUTHOR]

Background: Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs. Methods: This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables. Results: The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables. Conclusions: Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855 - 0.887 Dice). [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]

[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]