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- Thomas, HS, et al.
(författare)
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- 2019
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swepub:Mat__t
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- Li, W, et al.
(författare)
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Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL
- 2020
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Ingår i: NPJ breast cancer. - : Springer Science and Business Media LLC. - 2374-4677. ; 6:1, s. 63-
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Tidskriftsartikel (refereegranskat)abstract
- Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.
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- Jain, M, et al.
(författare)
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A systematic survey of lipids across mouse tissues
- 2014
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Ingår i: American journal of physiology. Endocrinology and metabolism. - : American Physiological Society. - 1522-1555 .- 0193-1849. ; 306:8, s. E854-E868
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Tidskriftsartikel (refereegranskat)abstract
- Lipids are a diverse collection of macromolecules essential for normal physiology, but the tissue distribution and function for many individual lipid species remain unclear. Here, we report a mass spectrometry survey of lipid abundance across 18 mouse tissues, detecting ∼1,000 mass spectrometry features, of which we identify 179 lipids from the glycerolipids, glycerophospholipids, lysophospholipids, acylcarnitines, sphingolipids, and cholesteryl ester classes. Our data reveal tissue-specific organization of lipids and can be used to generate testable hypotheses. For example, our data indicate that circulating triglycerides positively and negatively associated with future diabetes in humans are enriched in mouse adipose tissue and liver, respectively, raising hypotheses regarding the tissue origins of these diabetes-associated lipids. We also integrate our tissue lipid data with gene expression profiles to predict a number of substrates of lipid-metabolizing enzymes, highlighting choline phosphotransferases and sterol O-acyltransferases. Finally, we identify several tissue-specific lipids not present in plasma under normal conditions that may be of interest as biomarkers of tissue injury, and we show that two of these lipids are released into blood following ischemic brain injury in mice. This resource complements existing compendia of tissue gene expression and may be useful for integrative physiology and lipid biology.
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