186 research outputs found

    Maternal altitude and risk of low birthweight: A systematic review and meta-analyses

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    BACKGROUND: Previous studies conducted in high altitude regions showed that maternal altitude was associated with low birth weight. The effect size of birth weight reduction is inclusive with unknown effects due to preterm birth. We systematically reviewed the literature and synthesize evidence on associations between altitude elevation from sea level and birth weight. METHOD: We searched MEDLINE/PubMed, Embase, Scopus, Web of Science, and Cochrane database, from inception to May 5, 2020 for studies that reported maternal altitude and birth weight. Bayesian multilevel effect models were employed to estimate the effect size on birth weight (and gestational age) associated with altitude. Bayesian multilevel effect models were employed to estimate the effect size on birth weight (and gestational age) associated with altitude. RESULTS: The systematic search identified 1020 articles, with 52 articles meeting the inclusion criteria providing 207 estimates for the association of altitude and birth weight (n = 4,428,563), and with 22 articles providing 71 estimates for gestational age (n = 2,149,627). A reduction in mean birth weight of 96.98 g was associated with every 1000 m increase in altitude across 52 studies. A statistically significant but numerically minimal effect of maternal altitude elevation was observed on the gestational age (0.3 days), corresponding to a negligible estimation of 5 g lower birth weight. A relatively high heterogeneity of between-study association (I CONCLUSION: A clinically meaningful birth weight reduction was associated with maternal altitude elevation beginning from sea level. Future longitudinal studies are needed to elucidate the causal association and to understand the late effect of maternal altitude

    A Simple Ultrasound Based Classification Algorithm Allows Differentiation of Benign from Malignant Breast Lesions by Using Only Quantitative Parameters.

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    PURPOSE: We hypothesized that different quantitative ultrasound (US) parameters may be used as complementary diagnostic criteria and aimed to develop a simple classification algorithm to distinguish benign from malignant breast lesions and aid in the decision to perform biopsy or not. PROCEDURES: One hundred twenty-four patients, each with one biopsy-proven, sonographically evident breast lesion, were included in this prospective, IRB-approved study. Each lesion was examined with B-mode US, Color/Power Doppler US and elastography (Acoustic Radiation Force Impulse-ARFI). Different quantitative parameters were recorded for each technique, including pulsatility (PI) and resistive Index (RI) for Doppler US and lesion maximum, intermediate, and minimum shear wave velocity (SWVmax, SWVinterm, and SWVmin) as well as lesion-to-fat SWV ratio for ARFI. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of each quantitative parameter. Classification analysis was performed using the exhaustive chi-squared automatic interaction detection method. Results include the probability for malignancy for every descriptor combination in the classification algorithm. RESULTS: Sixty-five lesions were malignant and 59 benign. Out of all quantitative indices, maximum SWV (SWVmax), and RI were included in the classification algorithm, which showed a depth of three ramifications (SWVmax ≤ or > 3.16; if SWVmax ≤ 3.16 then RI ≤ 0.66, 0.66-0.77 or > 0.77; if RI ≤ 0.66 then SWVmax ≤ or > 2.71). The classification algorithm leads to an AUC of 0.887 (95 % CI 0.818-0.937, p < 0.0001), a sensitivity of 98.46 % (95 % CI 91.7-100 %), and a specificity of 61.02 % (95 % CI 47.4-73.5 %). By applying the proposed algorithm, a false-positive biopsy could have been avoided in 61 % of the cases. CONCLUSIONS: A simple classification algorithm incorporating two quantitative US parameters (SWVmax and RI) shows a high diagnostic performance, being able to accurately differentiate benign from malignant breast lesions and lower the number of unnecessary breast biopsies in up to 60 % of all cases, avoiding any subjective interpretation bias

    Virtual Touch IQ elastography reduces unnecessary breast biopsies by applying quantitative "rule-in" and "rule-out" threshold values.

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    Our purpose was to evaluate Virtual Touch IQ (VTIQ) elastography and identify quantitative "rule-in" and "rule-out" thresholds for the probability of malignancy, which can help avoid unnecessary breast biopsies. 189 patients with 196 sonographically evident lesions were included in this retrospective, IRB-approved study. Quantitative VTIQ images of each lesion measuring the respective maximum Shear Wave Velocity (SWV) were obtained. Paired and unpaired, non-parametric statistics were applied for comparisons as appropriate. ROC-curve analysis was used to analyse the diagnostic performance of VTIQ and to specify "rule-in" and "rule-out" thresholds for the probability of malignancy. The standard of reference was either histopathology or follow-up stability for >24 months. 84 lesions were malignant and 112 benign. Median SWV of benign lesions was significantly lower than that of malignant lesions (p 98% with a concomitant significant (p = 0.032) reduction in false positive cases of almost 15%, whereas a "rule-in" threshold of 6.5 m/s suggested a probability of malignancy of >95%. In conclusion, VTIQ elastography accurately differentiates malignant from benign breast lesions. The application of quantitative "rule-in" and "rule-out" thresholds is feasible and allows reduction of unnecessary benign breast biopsies by almost 15%

    Breast lesion detection and characterization with contrast-enhanced magnetic resonance imaging: Prospective randomized intraindividual comparison of gadoterate meglumine (0.15 mmol/kg) and gadobenate dimeglumine (0.075 mmol/kg) at 3T.

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    BACKGROUND: Contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast is highly sensitive for breast cancer detection. Multichannel coils and 3T scanners can increase signal, spatial, and temporal resolution. In addition, the T1 -reduction effect of a gadolinium-based contrast agent (GBCA) is higher at 3T. Thus, it might be possible to reduce the dose of GBCA at 3T without losing diagnostic information. PURPOSE: To compare a three-quarter (0.075 mmol/kg) dose of the high-relaxivity GBCA gadobenate dimeglumine, with a 1.5-fold higher than on-label dose (0.15 mmol/kg) of gadoterate meglumine for breast lesion detection and characterization at 3T CE-MRI. STUDY TYPE: Prospective, randomized, intraindividual comparative study. POPULATION: Eligible were patients with imaging abnormalities (BI-RADS 0, 4, 5) on conventional imaging. Each patient underwent two examinations, 24-72 hours apart, one with 0.075 mmol/kg gadobenate and the other with 0.15 mmol/kg gadoterate administered in a randomized order. In all, 109 patients were prospectively recruited. FIELD STRENGTH/SEQUENCE: 3T MRI with a standard breast protocol (dynamic-CE, T2 w-TSE, STIR-T2 w, DWI). ASSESSMENT: Histopathology was the standard of reference. Three blinded, off-site breast radiologists evaluated the examinations using the BI-RADS lexicon. STATISTICAL TESTS: Lesion detection, sensitivity, specificity, and diagnostic accuracy were calculated per-lesion and per-region, and compared by univariate and multivariate analysis (Generalized Estimating Equations, GEE). RESULTS: Five patients were excluded, leaving 104 women with 142 histologically verified breast lesions (109 malignant, 33 benign) available for evaluation. Lesion detection with gadobenate (84.5-88.7%) was not inferior to gadoterate (84.5-90.8%) (P ≥ 0.165). At per-region analysis, gadobenate demonstrated higher specificity (96.4-98.7% vs. 92.6-97.3%, P ≤ 0.007) and accuracy (96.3-97.8% vs. 93.6-96.1%, P ≤ 0.001) compared with gadoterate. Multivariate analysis demonstrated superior, reader-independent diagnostic accuracy with gadobenate (odds ratio = 1.7, P < 0.001 using GEE). DATA CONCLUSION: A 0.075 mmol/kg dose of the high-relaxivity contrast agent gadobenate was not inferior to a 0.15 mmol/kg dose of gadoterate for breast lesion detection. Gadobenate allowed increased specificity and accuracy. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1157-1165

    Microstructural breast tissue characterization: A head-to-head comparison of Diffusion Weighted Imaging and Acoustic Radiation Force Impulse elastography with clinical implications

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    Abstract Purpose Head-to-head comparison of Diffusion Weighted Imaging (DWI) and Acoustic Radiation Force Impulse (ARFI) elastography regarding the characterization of breast lesions in an assessment setting. Method Patients undergoing an ultrasound examination including ARFI and an MRI protocol including DWI for the characterization of a BI-RADS 3–5 breast lesion between 06/2013 and 10/2016 were eligible for inclusion in this retrospective, IRB-approved study. 60 patients (30–84 years, median 50) with a median lesion size of 16 mm (range 5–55 mm) were included. The maximum shear wave velocity (SWVmax) and mean apparent diffusion coefficient (ADCmean) for each lesion were retrospectively evaluated by a radiologist experienced in the technique. Histology was the reference standard. Diagnostic performances of ARFI and DWI were assessed using ROC curve analysis. Spearman's rank correlation coefficient and multivariate logistic regression were used to investigate the independence of both tests regarding their diagnostic information to distinguish benign from malignant lesions. Results Corresponding areas under the ROC curve for differentiation of benign (n = 16) and malignant (n = 49) lesions were 0.822 (ARFI) and 0.871 (DWI, p-value = 0.48). SWVmax and ADCmean values showed a significant negative correlation (ρ = −0.501, p-value Conclusion Significant correlation between quantitative findings of ARFI and DWI in breast lesions exists. Thus, ARFI provides similar diagnostic information as a DWI-including protocol of an additional "problem-solving" MRI for the characterization of a sonographically evident breast lesion, improving the immediate patient management in the assessment setting

    A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions.

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    OBJECTIVES: To assess whether using the Tree flowchart obviates unnecessary magnetic resonance imaging (MRI)-guided biopsies in breast lesions only visible on MRI. METHODS: This retrospective IRB-approved study evaluated consecutive suspicious (BI-RADS 4) breast lesions only visible on MRI that were referred to our institution for MRI-guided biopsy. All lesions were evaluated according to the Tree flowchart for breast MRI by experienced readers. The Tree flowchart is a decision rule that assigns levels of suspicion to specific combinations of diagnostic criteria. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy. To assess reproducibility by kappa statistics, a second reader rated a subset of 82 patients. RESULTS: There were 454 patients with 469 histopathologically verified lesions included (98 malignant, 371 benign lesions). The area under the curve (AUC) of the Tree flowchart was 0.873 (95% CI: 0.839-0.901). The inter-reader agreement was almost perfect (kappa: 0.944; 95% CI 0.889-0.998). ROC analysis revealed exclusively benign lesions if the Tree node was ≤2, potentially avoiding unnecessary biopsies in 103 cases (27.8%). CONCLUSIONS: Using the Tree flowchart in breast lesions only visible on MRI, more than 25% of biopsies could be avoided without missing any breast cancer. KEY POINTS: • The Tree flowchart may obviate >25% of unnecessary MRI-guided breast biopsies. • This decrease in MRI-guided biopsies does not cause any false-negative cases. • The Tree flowchart predicts 30.6% of malignancies with >98% specificity. • The Tree's high specificity aids in decision-making after benign biopsy results

    Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging

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    Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.European Unions Horizon 2020 Research and Innovation Programme under the Marie Skodowska-Curie grant agreement No. 656886Austrian National Bank "Jubilaeumsfond" Project 162192020-Research and Innovation Framework Programme PHC-11-2015 667211-2Siemens AustriaNovomedGuerbet, FranceNIH/NCI Cancer Center Support Grant P30CA00874

    Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

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    BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM. METHODS: Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05. RESULTS: The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists. CONCLUSIONS: The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation

    4D perfusion CT of prostate cancer for image-guided radiotherapy planning: A proof of concept study.

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    PURPOSE: Advanced forms of prostate cancer (PCa) radiotherapy with either external beam therapy or brachytherapy delivery techniques aim for a focal boost and thus require accurate lesion localization and lesion segmentation for subsequent treatment planning. This study prospectively evaluated dynamic contrast-enhanced computed tomography (DCE-CT) for the detection of prostate cancer lesions in the peripheral zone (PZ) using qualitative and quantitative image analysis compared to multiparametric magnet resonance imaging (mpMRI) of the prostate. METHODS: With local ethics committee approval, 14 patients (mean age, 67 years; range, 57-78 years; PSA, mean 8.1 ng/ml; range, 3.5-26.0) underwent DCE-CT, as well as mpMRI of the prostate, including standard T2, diffusion-weighted imaging (DWI), and DCE-MRI sequences followed by transrectal in-bore MRI-guided prostate biopsy. Maximum intensity projections (MIP) and DCE-CT perfusion parameters (CTP) were compared between healthy and malignant tissue. Two radiologists independently rated image quality and the tumor lesion delineation quality of PCa using a five-point ordinal scale. MIP and CTP were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/area under the curve (AUC) analysis. RESULTS: The PCa detection rate ranged between 57 to 79% for the two readers for DCE-CT and was 92% for DCE-MRI. DCE-CT perfusion parameters in PCa tissue in the PZ were significantly different compared to regular prostate tissue and benign lesions. Image quality and lesion visibility were comparable between DCE-CT and DCE-MRI (VGC: AUC 0.612 and 0.651, p>0.05). CONCLUSION: Our preliminary results suggest that it is feasible to use DCE-CT for identification and visualization, and subsequent segmentation for focal radiotherapy approaches to PCa
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