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| 2. |
- H., Olsson, et al.
(författare)
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Tamoxifen treated patients have a better survival than patients treated with aromatase inhibitors - A population based registry study in Sweden
- 2015
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Ingår i: Cancer Research. - 0008-5472. ; 75:9 Suppl
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Konferensbidrag (refereegranskat)abstract
- Background. Randomised trials suggest that therapy with aromatase inhibitors improves survival in breast cancer compared with tamoxifen therapy in postmenopausal cases with hormone receptor positive breast cancer. Whether these results from randomized studies transform into the general population is unknown. We have therefore compared survival for all breast cancer cases in Sweden diagnosed 2000-2008 (n=54406) who received adjuvant antihormonal therapy. Material and methods. The study includes all women with BC diagnosed in Sweden between 2000 through 2008 (n=54406). The women had no previous cancer diagnosis during the period of 1958-1999. Dates of birth, BC diagnosis and TNM-stage where directly extracted from the cancer registry. The women's antihormonal therapy was gathered from the Swedish Prescription Registry (22213 women were on antihormonal therapy). Information regarding the cause of death and date of death was obtained from the Cause of Death Registry and tbe Swedish Population Register up until the 31st of December 2012 and 31st of December 2013 respectively. The breast cancer death and overall death have been calculated and the survival was compared between tamoxifen and aromatase inhibitor treated breast cancer patients. Analyses were adjusted for TNM-stage and age at diagnosis and restricted to women aged 50 and above. Results. Patients being treated with tamoxifen had a better breast cancer prognosis compared with aromatase inhibitor treated patients (HR 0.54, 95%CI 0.48-0.61). Restricting the analysis to stage 1 disease confirmed a better prognosis for tamoxifen treated women (HR 0.48, 95%CI 0.34-0.66). A better prognosis could be seen in all age strata studies, 50-60.61-70.71-90. The findings for overall survival gave similar results. Conclusion .This population based observational study show that women treated with aromatase inhibitors have a worse overall and breast cancer specific survival compared with tamoxifen treated women regardless of age and tumor stage.
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| 3. |
- H., Olsson, et al.
(författare)
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Worse breast cancer prognosis in insulin treated diabetic patients - A population based registry study in Sweden
- 2015
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Ingår i: Cancer Research. - 0008-5472. ; 75:9 Suppl
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Konferensbidrag (refereegranskat)abstract
- Background. Diabetes may be linked to incidence of different tumor diseases and prognosis through various mechanisms such as the disease itself, hyperglycemia, obesity and anti-diabetes therapy. Material and methods. The study includes all women with BC diagnosed in Sweden between 2000 through 2008 (n=54406). The women had no previous cancer diagnosis during the period of 1958-1999. Dates of birth, BC diagnosis and TNM-stage where directly extracted from the cancer registry. The women's anti-diabetes therapy was gathered from the Swedish Prescribed Drug Registry. Information regarding the cause of death and date of death was obtained from the Cause of Death Registry and tbe Swedish Population Register up until the 31st of December 2012 and 31st of December 2013 respectively. Analyses have been restricted to patients receiving insulin therapy (n=2463) and their breast cancer prognosis has been calculated in comparison with breast cancer patients without diabetes. All analyses were adjusted for TNM-stage and age at diagnosis. Results. Patients with insulin treated diabetes had a worse prognosis compared with other women with breast cancer (HR 1.7, 95%CI 1.5-2.0). The worse prognosis could be seen both for patients with ER+ and ER- tumors. The worst prognosis was seen for patients treated with NPH insulins (HR 2.8, 95% CI 2.4-3.3) while patients treated with long-acting insulin analogs had an intermediate prognosis (HR 1.6, 95% CI 1.2-2.2). Those women treated with NPH insulins and metformin had a slightly worse prognosis (HR 1.4, 95% CI 1.0-1.8). The results for breast cancer specific survival and total survival were similar. Conclusion. Our results imply that insulin treated breast cancer patients have a worse survival compared with other women with breast cancer regardless of tumor stage. Metformin therapy may partially counteract the association.
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- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification
- 2019
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11678 LNCS, s. 234-245
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Konferensbidrag (refereegranskat)abstract
- Diagnosis and timely treatment play an important role in preventing brain tumor growth. Deep learning methods have gained much attention lately. Obtaining a large amount of annotated medical data remains a challenging issue. Furthermore, high dimensional features of brain images could lead to over-fitting. In this paper, we address the above issues. Firstly, we propose an architecture for Generative Adversarial Networks to generate good quality synthetic 2D MRIs from multi-modality MRIs (T1 contrast-enhanced, T2, FLAIR). Secondly, we propose a deep learning scheme based on 3-streams of Convolutional Autoencoders (CAEs) followed by sensor information fusion. The rational behind using CAEs is that it may improve glioma classification performance (as comparing with conventional CNNs), since CAEs offer noise robustness and also efficient feature reduction hence possibly reduce the over-fitting. A two-round training strategy is also applied by pre-training on GAN augmented synthetic MRIs followed by refined-training on original MRIs. Experiments on BraTS 2017 dataset have demonstrated the effectiveness of the proposed scheme (test accuracy 92.04%). Comparison with several exiting schemes has provided further support to the proposed scheme.
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| 5. |
- Ge, Chenjie, 1991, et al.
(författare)
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Cross-Modality Augmentation of Brain Mr Images Using a Novel Pairwise Generative Adversarial Network for Enhanced Glioma Classification
- 2019
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880.
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Konferensbidrag (refereegranskat)abstract
- © 2019 IEEE. Brain Magnetic Resonance Images (MRIs) are commonly used for tumor diagnosis. Machine learning for brain tumor characterization often uses MRIs from many modalities (e.g., T1-MRI, Enhanced-T1-MRI, T2-MRI and FLAIR). This paper tackles two issues that may impact brain tumor characterization performance from deep learning: insufficiently large training dataset, and incomplete collection of MRIs from different modalities. We propose a novel pairwise generative adversarial network (GAN) architecture for generating synthetic brain MRIs in missing modalities by using existing MRIs in other modalities. By improving the training dataset, we aim to mitigate the overfitting and improve the deep learning performance. Main contributions of the paper include: (a) propose a pairwise generative adversarial network (GAN) for brain image augmentation via cross-modality image generation; (b) propose a training strategy to enhance the glioma classification performance, where GAN-augmented images are used for pre-training, followed by refined-training using real brain MRIs; (c) demonstrate the proposed method through tests and comparisons of glioma classifiers that are trained from mixing real and GAN synthetic data, as well as from real data only. Experiments were conducted on an open TCGA dataset, containing 167 subjects for classifying IDH genotypes (mutation or wild-type). Test results from two experimental settings have both provided supports to the proposed method, where glioma classification performance has consistently improved by using mixed real and augmented data (test accuracy 81.03%, with 2.57% improvement).
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| 7. |
- Munthe, Christian, 1962
(författare)
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The magic word? Ethical experience of prioritizing cancer-related health action in a Swedish context
- 2018
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Ingår i: What is so Special about Cancer? Perspectives from Clinical Research, Philosophy and Social Sciences, University of Cambridge, April 5-6, 2018.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Across health systems and the history of modern welfare societies, the experience of cancer as a privileged diagnostic category in the prioritizing of resources for different health actions is commonplace, although there are notable exceptions to be found in low resource settings. While this situation can often be criticised from an ethical standpoint, health resource allocation also has a political pragmatic side that may, if not justify, so at least partly excuse the way in which measures related to cancer are being given privileged access to healthcare and public health resources. This since democratically elected political representatives cannot completely ignore the iconic status of cancer in the public mind. I describe some of this dynamic based on the Swedish experience of introducing screening and testing programs, as well as new drugs for cancer treatment. While Sweden is certainly not immune to the privileged standing of cancer in health resource allocation, there is a development in public and popular attitude towards a more egalitarian conception of cancer disease compared to other diseases. Parts that explain this development have to do with a new and more systematic focus on assessing the effectiveness of and evidence for suggested health actions according to generic models, such as HTA, standardised rules how priority setting arguments must be shaped in order to have political validity, and a broader awareness of the phenomenon of opportunity cost in policy making generally. In addition, political agendas increasingly focused on cost cutting in public expenditure in spite of ever greater levels of societal wealth has certainly also contributed, albeit that mechanism may probably also be properly criticised from an ethical standpoint.
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| 9. |
- Salford, Leif, et al.
(författare)
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The mammalian brain in the electromagnetic fields designed by man with special reference to blood-brain barrier function, neuronal damage and possible physical mechanisms
- 2008
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Ingår i: PROGRESS OF THEORETICAL PHYSICS SUPPLEMENT. - 0375-9687. ; :173, s. 283-309
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Konferensbidrag (refereegranskat)abstract
- Life oil earth was formed during billions of years, exposed to, and shaped by the original physical forces such as gravitation, cosmic irradiation, atmospheric electric fields and the terrestrial magnetism. The Schumann resonances at 7.4 Hz are all example of oscillations possibly important for life.(1)) The existing organisms are created to function in harmony with these forces. However, in the late 19th century mankind introduced the use of electricity, in the early 20th century long-wave radio and in the 1940-ies short-wave radio. High frequency RF was introduced in the 50-ies as FM and television and during the very last decades, microwaves of the modern communication society spread around the world. Today, however, one third of the world's population is owner of the microwave-producing mobile phones and an even larger number is exposed to the cordless RF emitting systems. To what; extent are all living organisms affected by these, almost everywhere present radio frequency fields? And what will be the effects of many years of continuing exposure? Since 1989 Our group has studied the effects upon the mammalian blood-brain barrier (BBB) in rats by non-thermal radio frequency electromagnetic fields (RF-EMF). These have been shown to cause significantly increased leak-age of the rats' own blood albumin through the BBB of exposed rats, at energy levels of 1W/kg and below, as compared to non-exposed animals in a total series of about two thousand animals.(2)-6)) One remarkable observation is the fact that the lowest energy levels, with whole-body average power densities below 10mW/kg, give rise to the most pronounced albumin leakage. If mobile communication, even at extremely low energy levels, causes the users' own albumin to leak out through the BBB, also other unwanted and toxic molecules in the blood, may leak into the brain tissue and concentrate in and damage the neurons and glial cells of the brain. In later studies we have shown that a 2-h exposure to GSM 915 MHz, at non-thermal SAB-values of 0.2, 2 and 200 mW/kg, gives rise to significant neuronal damage, seen not only 50 days after the exposure 7) but also after 28 days but not after 14 days. Albumin extravasations and uptake into neurons was enhanced after 14 clays, but not after 28.(8)) in our continued research, also the non-thermal effects oil tissue structure and memory function of long-term exposure for 13 months are studied.(9)) We have also performed microarray analysis of brains from rats exposed to short term GSM both at 1,800 MHz and at 900MHz and have found significant effects upon gene expression of membrane associated genes as compared to control animals.(10),11)) Most of our findings support that living organisms are affected by the non-thermal radio frequency fields. Some other Studies agree while others find no effects. The mechanisms by which the EMFs may alter BBB permeability are not Well Understood. At low field strengths, the effects on body temperature are negligible and thus heating effects are not involved. A change in the physicochemical characteristics of membranes has been suggested as a cause.(12)) We have performed experiments to verify a quantum mechanical model for interaction with protein-bound ions. Our results show that controlled frequency and amplitude of ELF EM fields upon spinach plasma vesicles can steer transport over the membrane.(13)) This may be a first proof of a resonance phenomenon where appropriate levels of frequency and amplitude in the right combination have the potency to communicate with the biology of membranes and transport systems. Our study has prompted Lis to elaborate on magnetic resonance models; the Ion Cyclotron Resonance (ICR) model and the Ion Parametric Resonance (IPR) Model in an attempt to explain the occurrence of resonance frequencies. This is extensively described here under the heading: Mechanisms behind the effects of electromagnetical fields upon biology. We also bring forward the concept of solitons being active in membranes and DNA/RNA-transcription as a, possible mean to understand and prove the biological effects of EMF. The Nishinomiya-Yukawa International and Interdisciplinary Symposium 2007 raised the question: What is Life? An obvious and simple answer could be: It is DNA! The DNA strand can be looked upon as an antenna resonating in the microwave band 6GHz with its harmonics and subharmonics.(14)-18)) If this holds true, the dramatic situation might exist, that all living organisms have a receptor for the newly constructed and world-wide man-made microvaves, leading to a direct effect upon the function of DNA - in concordance with our experimental findings! Our generation invented the microwave emitters. We now have in imperative obligation to further investigate the links between EMF and biology in order to prevent possible detrimental effects of the microwaves.
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| 10. |
- Ge, Chenjie, 1991, et al.
(författare)
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3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images
- 2018
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479970612 ; , s. 141-145
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Konferensbidrag (refereegranskat)abstract
- This paper addresses issues of grading brain tumor, glioma, from Magnetic Resonance Images (MRIs). Although feature pyramid is shown to be useful to extract multi-scale features for object recognition, it is rarely explored in MRI images for glioma classification/grading. For glioma grading, existing deep learning methods often use convolutional neural networks (CNNs) to extract single-scale features without considering that the scales of brain tumor features vary depending on structure/shape, size, tissue smoothness, and locations. In this paper, we propose to incorporate the multi-scale feature learning into a deep convolutional network architecture, which extracts multi-scale semantic as well as fine features for glioma tumor grading. The main contributions of the paper are: (a) propose a novel 3D multi-scale convolutional network architecture for the dedicated task of glioma grading; (b) propose a novel feature fusion scheme that further refines multi-scale features generated from multi-scale convolutional layers; (c) propose a saliency-aware strategy to enhance tumor regions of MRIs. Experiments were conducted on an open dataset for classifying high/low grade gliomas. Performance on the test set using the proposed scheme has shown good results (with accuracy of 89.47%).
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