5,502 research outputs found

    Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review

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    Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting >2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems

    A Comparative Analysis of Health-Related Quality of Life 1 Year Following Myomectomy or Uterine Artery Embolization: Findings from the COMPARE-UF Registry

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    Objective: To compare 12-month post-treatment health-related quality of life (HR-QoL) and symptom severity (SS) changes among patients with symptomatic uterine fibroids (SUF) not seeking fertility and undergo a hysterectomy, abdominal myomectomy (AM), or uterine artery embolization (UAE). Materials and Methods: The Comparing Options for Management: Patient-Centered Results for Uterine Fibroids (COMPARE-UF) Registry is a multi-institutional prospective observational cohort study of patients treated for SUF. A subset of 1465 women 31-45 years of age, who underwent either hysterectomy (n = 741), AM (n = 446), or UAE (n = 155) were included in this analysis. Demographics, fibroid history, and symptoms were obtained by baseline questionnaires and at 1 year post-treatment. Results were stratified by all treatments and propensity score weighting to adjust for differences in baseline characteristics. Results: Women undergoing UAE reported the lowest baseline HR-QoL and highest SS scores (mean = 40.6 [standard deviation (SD) = 23.8]; 62.3 [SD = 24.2]) followed by hysterectomy (44.3 [24.3]; 59.8 [SD = 24.1]). At 12 months, women who underwent a hysterectomy experienced the largest change in both HR-QoL (48.7 [26.2]) and SS (51.9 [25.6]) followed by other uterine-sparing treatments. Propensity score weighting revealed all treatments produced substantial improvement, with hysterectomy patients reporting the highest HR-QoL score (92.0 [17.8]) compared with myomectomy (86.7 [17.2]) and UAE (82.6 [21.5]) (p \u3c 0.0001). Similarly, hysterectomy patients reported the lowest SS scores (8.2 [15.1]) compared with myomectomy (16.5 [15.1]) and UAE (19.6 [17.5]) (p \u3c 0.0001). Conclusion: All procedures showed improvement in HR-QoL and reduction in SS score at 12 months, hysterectomy showing maximum improvement. Of importance, at 12 months, patients who underwent either a myomectomy or UAE reported comparable symptom relief and HR-QoL. Clinicaltrials.Gov Identifier: NCT02260752

    Associations of height, body mass index, and weight gain with breast cancer risk in carriers of a pathogenic variant in BRCA1 or BRCA2: the BRCA1 and BRCA2 Cohort Consortium

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    Abstract Introduction Height, body mass index (BMI), and weight gain are associated with breast cancer risk in the general population. It is unclear whether these associations also exist for carriers of pathogenic variants in the BRCA1 or BRCA2 genes. Patients and methods An international pooled cohort of 8091 BRCA1/2 variant carriers was used for retrospective and prospective analyses separately for premenopausal and postmenopausal women. Cox regression was used to estimate breast cancer risk associations with height, BMI, and weight change. Results In the retrospective analysis, taller height was associated with risk of premenopausal breast cancer for BRCA2 variant carriers (HR 1.20 per 10 cm increase, 95% CI 1.04–1.38). Higher young-adult BMI was associated with lower premenopausal breast cancer risk for both BRCA1 (HR 0.75 per 5 kg/m2, 95% CI 0.66–0.84) and BRCA2 (HR 0.76, 95% CI 0.65–0.89) variant carriers in the retrospective analysis, with consistent, though not statistically significant, findings from the prospective analysis. In the prospective analysis, higher BMI and adult weight gain were associated with higher postmenopausal breast cancer risk for BRCA1 carriers (HR 1.20 per 5 kg/m2, 95% CI 1.02–1.42; and HR 1.10 per 5 kg weight gain, 95% CI 1.01–1.19, respectively). Conclusion Anthropometric measures are associated with breast cancer risk for BRCA1 and BRCA2 variant carriers, with relative risk estimates that are generally consistent with those for women from the general population

    Machine learning augmented interpretation of chest X-rays: A systematic review

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    : Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation. A systematic search strategy was executed to identify research into machine learning algorithms capable of detecting \u3e2 radiographic findings on CXRs published between January 2020 and September 2022. Model details and study characteristics, including risk of bias and quality, were summarized. Initially, 2248 articles were retrieved, with 46 included in the final review. Published models demonstrated strong standalone performance and were typically as accurate, or more accurate, than radiologists or non-radiologist clinicians. Multiple studies demonstrated an improvement in the clinical finding classification performance of clinicians when models acted as a diagnostic assistance device. Device performance was compared with that of clinicians in 30% of studies, while effects on clinical perception and diagnosis were evaluated in 19%. Only one study was prospectively run. On average, 128,662 images were used to train and validate models. Most classified less than eight clinical findings, while the three most comprehensive models classified 54, 72, and 124 findings. This review suggests that machine learning devices designed to facilitate CXR interpretation perform strongly, improve the detection performance of clinicians, and improve the efficiency of radiology workflow. Several limitations were identified, and clinician involvement and expertise will be key to driving the safe implementation of quality CXR machine learning systems

    Strong Carbon Features and a Red Early Color in the Underluminous Type Ia SN 2022xkq

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    We present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining type Ia supernova (SN Ia) in NGC 1784 (D31\mathrm{D}\approx31 Mpc), from <1<1 to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess which is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived C I 1.0693 μ\mum feature which persists until 5 days post-maximum. We also detect C II λ\lambda6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic dataset of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes which produce faint SNe Ia.Comment: 38 pages, 16 figures, accepted for publication in ApJ, the figure 15 input models and synthetic spectra are now available at https://zenodo.org/record/837925

    A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants:Application to BRCA1 and BRCA2

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    A large number of variants identified through clinical genetic testing in disease susceptibility genes are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion) can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analysis of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC) and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared with classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and preformatted Excel calculators for implementation of the method for rare variants in BRCA1, BRCA2, and other high-risk genes with known penetrance.</p

    Breast cancer risks associated with missense variants in breast cancer susceptibility genes

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    10.1186/s13073-022-01052-8GENOME MEDICINE14

    A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

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    Background Genome-wide studies of gene-environment interactions (GxE) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide GxE analysis of similar to 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. Methods Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. Results Assuming a 1 x 10(-5) prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability &lt; 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94). Conclusions Overall, the contribution of GxE interactions to the heritability of breast cancer is very small. At the population level, multiplicative GxE interactions do not make an important contribution to risk prediction in breast cancer
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