Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.

OBJECTIVE: To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). METHODS: We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. RESULTS: Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9-14) and METRICS score of 67.6% (IQR, 58.8-76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. CONCLUSIONS: Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. CLINICAL RELEVANCE STATEMENT: Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. KEY POINTS: The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.

Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis.

OBJECTIVES: Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction. MATERIALS AND METHODS: Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I2 value. Publication bias was evaluated with a funnel plot. Subgroup analyses were performed to explore heterogeneity. RESULTS: Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76. CONCLUSION: MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed. CLINICAL RELEVANCE STATEMENT: Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements. KEY POINTS: • MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.

Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment.

OBJECTIVE: To systematically review and evaluate the methodological quality of studies using magnetic resonance imaging (MRI) and computed tomography (CT) radiomics for cardiac applications. METHODS: Multiple medical literature archives (PubMed, Web of Science, and EMBASE) were systematically searched to retrieve original studies focused on cardiac MRI and CT radiomics applications. Two researchers in consensus assessed each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to study aim, journal quartile, imaging modality, and first author category. RESULTS: From a total of 1961 items, 53 articles were finally included in the analysis. Overall, the studies reached a median total RQS of 7 (IQR, 4-12), corresponding to a percentage score of 19.4% (IQR, 11.1-33.3%). Item scores were particularly low due to lack of prospective design, cost-effectiveness analysis, and open science. Median RQS percentage score was significantly higher in papers where the first author was a medical doctor and in those published on first quartile journals. CONCLUSIONS: The overall methodological quality of radiomics studies in cardiac MRI and CT is still lacking. A higher degree of standardization of the radiomics workflow and higher publication standards for studies are required to allow its translation into clinical practice. KEY POINTS: • RQS has been recently proposed for the overall assessment of the methodological quality of radiomics-based studies. • The 53 included studies on cardiac MRI and CT radiomics applications reached a median total RQS of 7 (IQR, 4-12), corresponding to a percentage of 19.4% (IQR, 11.1-33.3%). • A more standardized methodology in the radiomics workflow is needed, especially in terms of study design, validation, and open science, in order to translate the results to clinical applications.

Cardiovascular magnetic resonance native T1 mapping in Anderson-Fabry disease: a systematic review and meta-analysis.

BACKGROUND: T1 mapping is an established cardiovascular magnetic resonance (CMR) technique that can characterize myocardial tissue. We aimed to determine the weighted mean native T1 values of Anderson-Fabry disease (AFD) patients and the standardized mean differences (SMD) as compared to healthy control subjects. METHODS: A comprehensive literature search of the PubMed, Scopus and Web of Science databases was conducted according to the PRISMA statement to retrieve original studies reporting myocardial native T1 values in AFD patients and healthy controls. A random effects model was used to calculate SMD, and meta-regression analysis was conducted to explore heterogeneity sources. Subgroup analysis was also performed according to scanner field strength and sequence type. RESULTS: From a total of 151 items, 14 articles were included in the final analysis accounting for a total population of 982 subjects. Overall, the weighted mean native T1 values was 984 ± 47 ms in AFD patients and 1016 ± 26 ms in controls (P < 0.0001) with a pooled SMD of - 2.38. In AFD patients there was an inverse correlation between native T1 values and male gender (P = 0.002) and left ventricular hypertrophy (LVH) (P < 0.001). Subgroup analyses confirmed lower T1 values in AFD patients compared to controls with a pooled SMD of -  2.54, -  2.28, -  2.46 for studies performed on 1.5T with modified Look-Locker inversion recovery (MOLLI), shortened MOLLI and saturation-recovery single-shot acquisition, respectively and of -  2.41 for studies conducted on 3T. CONCLUSIONS: Our findings confirm a reduction of native T1 values in AFD patients compared to healthy controls and point out that the degree of T1 shortening in AFD is influenced by gender and LVH. Although T1 mapping is useful in proving cardiac involvement in AFD patients, there is need to standardize shreshold values according to imaging equipment and protocols.

Multimodality imaging approach to Fabry cardiomyopathy: Any role for nuclear cardiology?

Anderson-Fabry disease (AFD) is a multisystem X-linked disorder of lipid metabolism frequently associated with progressive glycosphingolipid accumulation in cardiac, renal, and nervous cells. The diagnosis of AFD is usually assessed by enzyme assay and genetic tests, but advanced cardiac imaging can be useful in detecting early signs of the disease. Echocardiography and cardiac magnetic resonance are the first-line imaging modalities to investigate cardiac involvement in AFD, but the recent introduction of new molecular and hybrid imaging techniques opens to a wider range of diagnostic applications. This article aims to provide an overview of nuclear cardiology techniques in diagnosis and clinical management of AFD.

Deep Learning Whole-Gland and Zonal Prostate Segmentation on a Public MRI Dataset.

BACKGROUND: Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malignant pathology and can be used either alone or combined with other parameters such as prostate-specific antigen. PURPOSE: This study compared different deep learning methods for whole-gland and zonal prostate segmentation. STUDY TYPE: Retrospective. POPULATION: A total of 204 patients (train/test = 99/105) from the PROSTATEx public dataset. FIELD STRENGTH/SEQUENCE: A 3 T, TSE T2 -weighted. ASSESSMENT: Four operators performed manual segmentation of the whole-gland, central zone + anterior stroma + transition zone (TZ), and peripheral zone (PZ). U-net, efficient neural network (ENet), and efficient residual factorized ConvNet (ERFNet) were trained and tuned on the training data through 5-fold cross-validation to segment the whole gland and TZ separately, while PZ automated masks were obtained by the subtraction of the first two. STATISTICAL TESTS: Networks were evaluated on the test set using various accuracy metrics, including the Dice similarity coefficient (DSC). Model DSC was compared in both the training and test sets using the analysis of variance test (ANOVA) and post hoc tests. Parameter number, disk size, training, and inference times determined network computational complexity and were also used to assess the model performance differences. A P < 0.05 was selected to indicate the statistical significance. RESULTS: The best DSC (P < 0.05) in the test set was achieved by ENet: 91% ± 4% for the whole gland, 87% ± 5% for the TZ, and 71% ± 8% for the PZ. U-net and ERFNet obtained, respectively, 88% ± 6% and 87% ± 6% for the whole gland, 86% ± 7% and 84% ± 7% for the TZ, and 70% ± 8% and 65 ± 8% for the PZ. Training and inference time were lowest for ENet. DATA CONCLUSION: Deep learning networks can accurately segment the prostate using T2 -weighted images. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Machine learning solutions in radiology: does the emperor have no clothes?

KEY POINTS: • Interest in radiomics and machine learning is steadily increasing and is reflected both in research output and number of commercially available solutions.• Currently available commercial products using machine learning are often supported by limited evidence of clinical usefulness and studies are often of low methodological quality.• Ethical and regulatory issues remain open and hinder implementation of machine learning software packages in daily clinical practice.

MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study.

OBJECTIVES: To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions. METHODS: Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions' data and compared with a baseline reference and expert radiologist assessment of EPE. RESULTS: In total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81-83%, p = 0.39-1) and outperforming the baseline reference (p = 0.001-0.02). CONCLUSIONS: A ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task. KEY POINTS: • Predicting the presence of EPE in prostate cancer patients is a challenging task for radiologists. • A support vector machine algorithm achieved high diagnostic accuracy for EPE detection, with good generalizability when tested on multiple external datasets. • The performance of the algorithm was not significantly different from that of an experienced radiologist.

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions.

OBJECTIVES: We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML's accuracy with that of a breast radiologist, and verify if the radiologist's performance is improved by using ML. METHODS: Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar's test. RESULTS: After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70-90%) with an AUC of 0.82 (95% CI = 0.70-0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist's performance improved when using ML (80.2%), but not significantly (p = 0.508). CONCLUSIONS: A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images. KEY POINTS: • Machine learning showed good accuracy in discriminating benign from malignant breast lesions • The machine learning classifier's performance was comparable to that of a breast radiologist • The radiologist's accuracy improved with machine learning, but not significantly.

Focal reduction in left ventricular 123I-metaiodobenzylguanidine uptake and impairment in systolic function in patients with Anderson-Fabry disease.

BACKGROUND: Abnormalities of cardiac sympathetic innervation have been demonstrated in Anderson-Fabry disease (AFD). We aimed to investigate the relationship between regional left ventricular (LV) denervation and regional function abnormalities. METHODS: Twenty-four AFD patients (43.7 ± 12.8 years) were studied by 123I-metaiodobenzylguanidine (MIBG) cardiac imaging and speckle-tracking echocardiography. Segmental tracer uptake was estimated according to 0 to 4 score, and total defect score (TDS) was calculated for each patient. RESULTS: Segmental longitudinal strain worsened as MIBG uptake score increased (P < 0.001). By ROC analysis, a segmental longitudinal strain > - 16.2% predicted a segmental MIBG uptake score ≥1, with 79.7% sensitivity and 65.3% specificity. Segmental MIBG uptake defects were found in 13 out 24 AFD patients. LV mass index (60.8 ± 10.1 vs. 41.4 ± 9.8 g/h2.7), relative wall thickness (0.51 ± 0.06 vs. 0.40 ± 0.06), systolic pulmonary artery pressure (35.2 ± 6.7 vs. 27.2 ± 4.2 mmHg), and longitudinal strain (- 14.3 ± 2.7 vs. -19.4 ± 1.8%) were significantly higher in patients with segmental defect (all P < 0.01). At multivariate linear regression analysis, global longitudinal strain was independently associated with TDS (B = 3.007, 95% confidence interval 1.384 to 4.630, P = 0.001). CONCLUSIONS: Reduced cardiac MIBG uptake reflects the severity of cardiac involvement in AFD patients. LV longitudinal function impairment seems to be an earlier disease feature than regional myocardial denervation.

Clinically Significant Prostate Cancer Detection With Biparametric MRI: A Systematic Review and Meta-Analysis.

OBJECTIVE. The purpose of this study was to perform a systematic review and a meta-analysis of diagnostic accuracy studies that used biparametric MRI (bpMRI) for the detection of clinically significant prostate cancer (csPCa). MATERIALS AND METHODS. Multiple medical databases were systematically searched to identify articles using bpMRI for csPCa detection. Sensitivity, specificity, PPV, and NPV were calculated for each study after enough data were extracted to create a 2 × 2 contingency table. Risk of bias was assessed using the QUADAS-2 tool. Meta-analyses based on bivariate random-effects methods were used to calculate pooled sensitivity, specificity, and summary ROC (SROC) curves. A meta-regression analysis was performed to assess heterogeneity sources. RESULTS. A total of 17 studies (3964 patients) that adopted PI-RADS or other scoring systems were included. Sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio of bpMRI in the detection of csPCa were 0.83 (95% CI, 0.76-0.88), 0.71 (95% CI, 0.63-0.79), 2.9 (95% CI, 2.3-3.7), 0.24 (95% CI, 0.17-0.33), and 12 (95% CI, 8-19), respectively, with an area under the SROC curve of 0.84 (95% CI, 0.81-0.87). The overall quality of the included studies was heterogeneous. CONCLUSION. Our results confirm the feasibility of bpMRI for the detection of csPCa and for reducing acquisition time, patient discomfort, and costs. Nevertheless, the available studies proved to be heterogeneous, indicating a need for a more robust validation of this imaging protocol and a standardization of prostate bpMRI acquisition and reporting.

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.

Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.

OBJECTIVES: The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically significant prostate cancer (csPCa) identification on MRI. METHODS: Multiple medical databases were systematically searched for studies on ML applications in csPCa identification up to July 31, 2019. Two reviewers screened all papers independently for eligibility. The area under the receiver operating characteristic curves (AUC) was pooled to quantify predictive accuracy. A random-effects model estimated overall effect size while statistical heterogeneity was assessed with the I2 value. A funnel plot was used to investigate publication bias. Subgroup analyses were performed based on reference standard (biopsy or radical prostatectomy) and ML type (deep and non-deep). RESULTS: After the final revision, 12 studies were included in the analysis. Statistical heterogeneity was high both in overall and in subgroup analyses. The overall pooled AUC for ML in csPCa identification was 0.86, with 0.81-0.91 95% confidence intervals (95%CI). The biopsy subgroup (n = 9) had a pooled AUC of 0.85 (95%CI = 0.79-0.91) while the radical prostatectomy one (n = 3) of 0.88 (95%CI = 0.76-0.99). Deep learning ML (n = 4) had a 0.78 AUC (95%CI = 0.69-0.86) while the remaining 8 had AUC = 0.90 (95%CI = 0.85-0.94). CONCLUSIONS: ML pipelines using prostate MRI to identify csPCa showed good accuracy and should be further investigated, possibly with better standardisation in design and reporting of results. KEY POINTS: • Overall pooled AUC was 0.86 with 0.81-0.91 95% confidence intervals. • In the reference standard subgroup analysis, algorithm accuracy was similar with pooled AUCs of 0.85 (0.79-0.91 95% confidence intervals) and 0.88 (0.76-0.99 95% confidence intervals) for studies employing biopsies and radical prostatectomy, respectively. • Deep learning pipelines performed worse (AUC = 0.78, 0.69-0.86 95% confidence intervals) than other approaches (AUC = 0.90, 0.85-0.94 95% confidence intervals).

Identification and typing of cardiac amyloidosis by noninvasive imaging: Two cases for two patterns.

Cardiac amyloidosis is a restrictive infiltrative cardiomyopathy burdened by high mortality. The two more common forms are immunoglobulin light-chain amyloidosis and transthyretin-related amyloidosis with different prognoses and treatments. However, distinguishing between them is challenging. Appropriate utilization of the different available imaging techniques in the evaluation of patients with known or suspected cardiac amyloidosis is mandatory. We report two cases with cardiac amyloidosis of different etiology and with distinct imaging patterns. In the first case, the negative 99mTc-diphosphonate imaging was useful to support the diagnosis of cardiac amyloid light-chain; the second case emphasized the utility of whole-body scintigraphy in recognizing transthyretin-related cardiac amyloidosis and the potential role of cadmium-zinc-telluride SPECT imaging for the evaluation of segmental distribution of cardiac disease. Both cases support the growing interest in looking for noninvasive methods to type cardiac amyloidosis in the place of invasive myocardial biopsy highlighting both possibilities and limitations of available imaging techniques in diagnosis and treatment monitoring.

Aberrant right coronary artery in a grown up congenital cardiac patient, successfully treated 46 years earlier with a double Starr-Edwards silastic ball valve replacement: a case report.

BACKGROUND: The Starr-Edwards ball valve prosthesis was successfully introduced in 1961-62 and largely used for aortic and mitral valve replacement. Even if Starr-Edwards valves have been widely replaced in clinical practice by other mechanical valves, they define a standard concerning long-term durability. CASE PRESENTATION: We describe the case of a 55-year-old man referred to our Department to perform a cardiac computed tomography (CCT), to better evaluate a severe dilation of ascending aorta discovered at echocardiography. The patient had been surgically treated 46 years earlier to correct a supra-cristal type ventricular septal defect. Both mitral and aortic valves were replaced, respectively due to bacterial mitral endocarditis and a fibrous sub-valvular aortic stenosis. In addition, the right coronary artery (RCA) was found to arise from the left coronary sinus. CONCLUSION: We report the longest lasting durability (46 years) of aortic and mitral Starr-Edwards valves successfully implanted in a patient simultaneously carrying a malignant anomalous origin of RCA.

Octreotide-LAR in later-stage autosomal dominant polycystic kidney disease (ALADIN 2): A randomized, double-blind, placebo-controlled, multicenter trial.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is the most frequent genetically determined renal disease. In affected patients, renal function may progressively decline up to end-stage renal disease (ESRD), and approximately 10% of those with ESRD are affected by ADPKD. The somatostatin analog octreotide long-acting release (octreotide-LAR) slows renal function deterioration in patients in early stages of the disease. We evaluated the renoprotective effect of octreotide-LAR in ADPKD patients at high risk of ESRD because of later-stage ADPKD. METHODS AND FINDINGS: We did an internally funded, parallel-group, double-blind, placebo-controlled phase III trial to assess octreotide-LAR in adults with ADPKD with glomerular filtration rate (GFR) 15-40 ml/min/1.73 m2. Participants were randomized to receive 2 intramuscular injections of 20 mg octreotide-LAR (n = 51) or 0.9% sodium chloride solution (placebo; n = 49) every 28 days for 3 years. Central randomization was 1:1 using a computerized list stratified by center and presence or absence of diabetes or proteinuria. Co-primary short- and long-term outcomes were 1-year total kidney volume (TKV) (computed tomography scan) growth and 3-year GFR (iohexol plasma clearance) decline. Analyses were by modified intention-to-treat. Patients were recruited from 4 Italian nephrology units between October 11, 2011, and March 20, 2014, and followed up to April 14, 2017. Baseline characteristics were similar between groups. Compared to placebo, octreotide-LAR reduced median (95% CI) TKV growth from baseline by 96.8 (10.8 to 182.7) ml at 1 year (p = 0.027) and 422.6 (150.3 to 695.0) ml at 3 years (p = 0.002). Reduction in the median (95% CI) rate of GFR decline (0.56 [-0.63 to 1.75] ml/min/1.73 m2 per year) was not significant (p = 0.295). TKV analyses were adjusted for age, sex, and baseline TKV. Over a median (IQR) 36 (24 to 37) months of follow-up, 9 patients on octreotide-LAR and 21 patients on placebo progressed to a doubling of serum creatinine or ESRD (composite endpoint) (hazard ratio [HR] [95% CI] adjusted for age, sex, baseline serum creatinine, and baseline TKV: 0.307 [0.127 to 0.742], p = 0.009). One composite endpoint was prevented for every 4 treated patients. Among 63 patients with chronic kidney disease (CKD) stage 4, 3 on octreotide-LAR and 8 on placebo progressed to ESRD (adjusted HR [95% CI]: 0.121 [0.017 to 0.866], p = 0.036). Three patients on placebo had a serious renal cyst rupture/infection and 1 patient had a serious urinary tract infection/obstruction, versus 1 patient on octreotide-LAR with a serious renal cyst infection. The main study limitation was the small sample size. CONCLUSIONS: In this study we observed that in later-stage ADPKD, octreotide-LAR slowed kidney growth and delayed progression to ESRD, in particular in CKD stage 4. TRIAL REGISTRATION: ClinicalTrials.gov NCT01377246; EudraCT: 2011-000138-12.

Coronary vascular age: An alternate means for predicting stress-induced myocardial ischemia in patients with suspected coronary artery disease.

BACKGROUND: Coronary artery calcium (CAC) can be used to estimate vascular age in adults, providing a convenient transformation of CAC from Agatston units into a year's scale. We investigated the role of coronary vascular age in predicting stress-induced myocardial ischemia in subjects with suspected coronary artery disease (CAD). METHODS: A total of 717 subjects referred to CAC scoring and 82Rb PET/CT stress-rest myocardial perfusion imaging for suspected CAD were studied. CAC score was measured according to the Agatston method and coronary vascular age by equating estimated CAD risk for chronological age and CAC using the formula 39.1 + 7.25 × ln(CAC + 1). RESULTS: Stress-induced ischemia was present in 105 (15%) patients. Mean chronological age, CAC score, and coronary vascular age were higher (all P < .001) in patients with ischemia compared to those without. At incremental analysis, the global Chi square increased from 41.26 to 68.77 (P < .001) when chronological age was added to clinical variables. Including vascular age in the model, the global Chi square further increased from 68.77 to 106.38 (P < .001). Adding chronological age to clinical data, continuous net reclassification improvement (cNRI) was 0.57, while adding vascular age to clinical data and chronological age cNRI was 0.62. At decision curve analysis, the model including vascular age was associated with the highest net benefit compared to the model including only clinical data, to the model including chronological age and clinical data, and to a strategy considering that all patients had ischemia. The model including vascular age also showed the largest reduction in false-positive rate without missing any ischemic patients. CONCLUSIONS: In subjects with suspected CAD, coronary vascular age is strongly associated with stress-induced ischemia. The communication of a given vascular age would have a superior emotive impact improving observance of therapies and healthier lifestyles.

Assessment of acute myocarditis by cardiac magnetic resonance imaging: Comparison of qualitative and quantitative analysis methods.

BACKGROUND: To compare cardiac magnetic resonance (CMR) qualitative and quantitative analysis methods for the noninvasive assessment of myocardial inflammation in patients with suspected acute myocarditis (AM). METHODS: A total of 61 patients with suspected AM underwent coronary angiography and CMR. Qualitative analysis was performed applying Lake-Louise Criteria (LLC), followed by quantitative analysis based on the evaluation of edema ratio (ER) and global relative enhancement (RE). Diagnostic performance was assessed for each method by measuring the area under the curves (AUC) of the receiver operating characteristic analyses. The final diagnosis of AM was based on symptoms and signs suggestive of cardiac disease, evidence of myocardial injury as defined by electrocardiogram changes, elevated troponin I, exclusion of coronary artery disease by coronary angiography, and clinical and echocardiographic follow-up at 3 months after admission to the chest pain unit. RESULTS: In all patients, coronary angiography did not show significant coronary artery stenosis. Troponin I levels and creatine kinase were higher in patients with AM compared to those without (both P < .001). There were no significant differences among LLC, T2-weighted short inversion time inversion recovery (STIR) sequences, early (EGE), and late (LGE) gadolinium-enhancement sequences for diagnosis of AM. The AUC for qualitative (T2-weighted STIR 0.92, EGE 0.87 and LGE 0.88) and quantitative (ER 0.89 and global RE 0.80) analyses were also similar. CONCLUSIONS: Qualitative and quantitative CMR analysis methods show similar diagnostic accuracy for the diagnosis of AM. These findings suggest that a simplified approach using a shortened CMR protocol including only T2-weighted STIR sequences might be useful to rule out AM in patients with acute coronary syndrome and normal coronary angiography.

Layer-specific longitudinal strain in Anderson-Fabry disease at diagnosis: A speckle tracking echocardiography analysis.

BACKGROUND: Speckle tracking advancements make now available the analysis of layer-specific myocardial deformation. This study investigated multilayer longitudinal strain in Anderson-Fabry disease (AFD) patients at diagnosis. METHODS: In a case-control study, 33 newly diagnosed, untreated AFD patients and 33 healthy age- and sex-matched healthy controls underwent a complete echocardiogram, including assessment of left ventricular (LV) transmural global longitudinal strain (GLS), subendocardial longitudinal strain (LSsubendo), subepicardial longitudinal strain (LSsubepi), and strain gradient (LSsubendo-LSsubpepi). RESULTS: Anderson-Fabry disease patients had similar blood pressure, heart rate, and ejection fraction but higher body mass index in comparison with controls. LV mass index, maximal, and relative wall thickness were significantly greater in AFD patients. LSsubendo was significantly higher than LSsubepi in both groups, but GLS (P < 0.0001), LSsubendo (P = 0.003), and particularly LSsubepi (21.4 ± 1.7 vs 18.8 ± 1.4%, P < 0.0001) were lower in AFD patients than in controls. Accordingly, LS gradient was higher in AFD patients (P = 0.003). Three patients symptomatic for dyspnoea presented a combination of LV hypertrophy and reduced LSsubepi. After adjusting for confounders by multivariate analyses, LV mass index or maximal wall thickness were independently and inversely associated with transmural GLS and LSsubepi, but not with LSsubendo in the AFD group. At receiver operating curve curves, LSsubepi best discriminated AFD and normals. CONCLUSIONS: In newly diagnosed, untreated AFD patients, layer-specific strain imaging highlights an impairment of LV longitudinal deformation, mainly involving subepicardial strain and causing increase in longitudinal strain myocardial gradient. These findings could be useful for identifying the mechanisms underlying early LV dysfunction in AFD patients.

Clinical value of high-resolution (5-17 MHz) echo-color Doppler (ECD) for identifying filling materials and assessment of damage or complications in aesthetic medicine/surgery.

PURPOSE: The purpose of this retrospective study is to evaluate the role of echo-color-Doppler (ECD) imaging in identifying a series of characteristics pursuant to aesthetic filling material such as their degree of absorbability and their potential complications which include their propensity to stimulate the formation of encapsulated foreign-body granulomas. In the latter case, ECD can be of aid by giving indication for surgical therapy. MATERIALS AND METHODS: Over a 4-year period, we studied 180 patients (60 ♂) who underwent an aesthetic medical/surgical treatment. We used ECD to evaluate the implant material, its thickness, the injection site, the integrity of dermal layers and the presence of any associated complications. RESULTS: In 97% (174/180) of our patients, we were able to identify the type of material used; furthermore, 57% of patients had a hyaluronic acid implant, 14% a lipofilling and 29% a non-absorbable filler (with 10% of silicone). In 6/180 (3%), we could not recognize the material used; 89% (161/180) of our patients presented post-injection complications; moreover, 67% showed peri-implant dermal-hypodermal thickening areas with adjacent lymphostasis, 6% displayed an abnormal implant site, and 17% showed inflammation with encapsulated foreign-body granulomas that required subsequent surgical excision. Biopsy samples were obtained from 37/180 patients (21%); among these, 31 patients had an ECD evidence of granuloma and on 6 patients we were not able to define the injected material. Histopathological examination identified 29 granulomas, 5 sterile abscesses and 3 chronic inflammations in the absence of granuloma. ECD showed an overall 78% diagnostic accuracy, with 90% sensitivity and 37% specificity in detecting filler granulomas. CONCLUSION: ECD is a low-cost technique that allows to identify filling materials and to assess the complications of an esthetic medical/surgical treatment.