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- Christiansen, F., et al.
(författare)
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Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors : comparison with expert subjective assessment
- 2021
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Ingår i: Ultrasound in Obstetrics and Gynecology. - : Wiley. - 0960-7692 .- 1469-0705. ; 57:1, s. 155-163
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective assessment (SA) by an ultrasound expert. Methods: We included 3077 (grayscale, n = 1927; power Doppler, n = 1150) ultrasound images from 758 women with ovarian tumors, who were classified prospectively by expert ultrasound examiners according to IOTA (International Ovarian Tumor Analysis) terms and definitions. Histological outcome from surgery (n = 634) or long-term (>= 3 years) follow-up (n = 124) served as the gold standard. The dataset was split into a training set (n = 508; 314 benign and 194 malignant), a validation set (n = 100; 60 benign and 40 malignant) and a test set (n = 150; 75 benign and 75 malignant). We used transfer learning on three pre-trained DNNs: VGG16, ResNet50 and MobileNet. Each model was trained, and the outputs calibrated, using temperature scaling. An ensemble of the three models was then used to estimate the probability of malignancy based on all images from a given case. The DNN ensemble classified the tumors as benign or malignant (Ovry-Dx1 model); or as benign, inconclusive or malignant (Ovry-Dx2 model). The diagnostic performance of the DNN models, in terms of sensitivity and specificity, was compared to that of SA for classifying ovarian tumors in the test set. Results: At a sensitivity of 96.0%, Ovry-Dx1 had a specificity similar to that of SA (86.7% vs 88.0%; P = 1.0). Ovry-Dx2 had a sensitivity of 97.1% and a specificity of 93.7%, when designating 12.7% of the lesions as inconclusive. By complimenting Ovry-Dx2 with SA in inconclusive cases, the overall sensitivity (96.0%) and specificity (89.3%) were not significantly different from using SA in all cases (P = 1.0). Conclusion: Ultrasound image analysis using DNNs can predict ovarian malignancy with a diagnostic accuracy comparable to that of human expert examiners, indicating that these models may have a role in the triage of women with an ovarian tumor.
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- Olofsson, A M, et al.
(författare)
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Heparin-binding protein targeted to mitochondrial compartments protects endothelial cells from apoptosis
- 1999
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Ingår i: Journal of Clinical Investigation. - 0021-9738. ; 104:7, s. 885-894
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Tidskriftsartikel (refereegranskat)abstract
- Neutrophil-borne heparin-binding protein (HBP) is a multifunctional protein involved in the progression of inflammation. HBP is stored in neutrophil granules and released upon stimulation of the cells in proximity to endothelial cells. HBP affects endothelial cells in multiple ways; however, the molecular and cellular mechanisms underlying the interaction of HBP with these cells are unknown. Affinity isolation and enzymatic degradation demonstrated that HBP released from human neutrophils binds to endothelial cell-surface proteoglycans, such as syndecans and glypican. Flow cytometry indicated that a significant fraction of proteoglycan-bound HBP is taken up by the endothelial cells, and we used radiolabeled HBP to determine the internalization rate of surface-bound HBP. Confocal and electron microscopy revealed that internalized HBP is targeted to perinuclear compartments of endothelial cells, where it colocalizes with mitochondria. Western blotting of isolated mitochondria from HBP-treated endothelial cells showed that HBP is present in 2 forms - 28 and 22 kDa. Internalized HBP markedly reduced growth factor deprivation-induced caspase-3 activation and protected endothelial cells from apoptosis, suggesting that uptake and intracellular routing of exogenous HBP to mitochondria contributes to the sustained viability of endothelial cells in the context of locally activated neutrophils.
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