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- 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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| 3. |
- 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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| 4. |
- 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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| 6. |
- 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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| 7. |
- 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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| 8. |
- Montelius, Mikael, 1979, et al.
(författare)
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Multiparametric MRI with spatiotemporal evaluation reveals potential therapy response biomarkers for 177Lu-octreotate therapy of mice with human neuroendocrine tumor
- 2017
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Ingår i: ISMRM 25th Annual Meeting. 22-27 April 2017, Honolulu, Hawaii, USA.
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Konferensbidrag (refereegranskat)abstract
- Tissue parameters derived from multiparametric MRI were evaluated as potential imaging biomarkers for therapy response assessment in mice with human neuroendocrine tumor treated with 177Lu-octreotate. Animals were imaged before and repeatedly after 177Lu-octreotate treatment, using T2w, IVIM-DWI, DCE-MRI, T1- and T2*-mapping techniques. MR-parameters were evaluated regionally and longitudinally, and quantitative proteomics was used to evaluate underlying biological response in central and peripheral tumor separately. Several MR-parameters showed strong correlation with tumor response, as verified by MRI-based tumor volume measurements, but also with proteins associated with radiobiological effects on tumor tissue. Spatial and temporal evaluation increased sensitivity of the methods.
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| 9. |
- Spetz, Johan, et al.
(författare)
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Spatial proteomic analysis of GOT1 human small intestine neuroendocrine tumor in nude mice following 177Lu-octreotate therapy
- 2016
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Ingår i: 62nd Annual International Meeting Radiation Research Society, Waikoloa, HI, USA, October 16-19, 2016.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: 177Lu-octreotate can be used for therapy of somatostatin receptor expressing neuroendocrine tumors (NET). 177Lu-octreotate therapy has achieved high cure rates in GOT1 (human small intestine NET) transplanted to nude mice. However, clinical studies result in moderate response, and complete tumor remission is rarely seen. Solid tumors often develop necrotic cores during growth due to e.g. insufficient vascularization, which may account for the different uptake kinetics and varying therapeutic success seen after 177Lu-octreotate treatment. It is therefore important to explore the cellular effects of 177Lu-octreotate to further optimize treatment parameters and identify biomarkers for treatment response assessment. Aim: To detect significant changes in protein profiles from peripheral and central samples of GOT1 tumor after 177Lu-octreotate therapy. Methods: GOT1 bearing BALB/c nude mice were i.v. injected with 15 MBq 177Lu-octreotate (corresponding to 4 Gy tumor absorbed dose) and killed after 13 days. Total cellular proteins were extracted from the peripheral and central parts of surgically excised tumor samples. Proteomic profiling was generated using liquid chromatography tandem-mass spectrometry (LC-MS/MS), followed by database-based protein identification and relative quantification. Functional annotation of proteins was performed using the DAVID bioinformatics resource tool. Results: The LC-MS/MS analysis identified 58 differentially expressed proteins (p<0.05, Fold Change>1.2) between the peripheral and central parts of tumor samples. Forty of these showed higher levels in the peripheral compared with the central samples, and among them were proteins associated with blood vessel/vasculature development (e.g. FAK1, H6ST1, LMA4, and SYWM), regulation of cell cycle and apoptosis (e.g. FAK1 and MK01), and cellular integrity (e.g. PDLI5, CALD1, FERM2, and NEB2). Conclusions: Taken together, these findings suggest spatial differences in tumor response to 177Lu-octreotate, mainly constituted by differences in vascularization and cellular integrity. These findings indicate potential venues for prognostic evaluation of NET therapy using 177Lu-octreotate. However, further studies are needed to determine whether these effects are time- and/or dose-dependent.
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| 10. |
- Cardilin, Tim, 1989, et al.
(författare)
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Modeling of radiation therapy and radiosensitizing agents in tumor xenografts
- 2018
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: To conceptually and mathematically describe the treatment effects of radiation and radiosensitizing agents on tumor volume in xenografts with respect to short- and long-term effects. Methods: Data were generated in FaDu xenograft mouse models, where animals were treated with radiation given either as monotherapy (2 Gy per dose) or together with an early-discovery radiosensitizing agent (25 or 100 mg/kg per dose) that interferes with the repair of the DNA damage induced by irradiation. Animals received treatment following a clinically-relevant administration schedule with doses five days a week for six weeks. Tumor diameters were measured by caliper twice a week for up to 140 days. A pharmacodynamic tumor model was adapted from a previously-published model [1,2]. The improved model captures both short- and long-term treatment effects including tumor eradication and tumor regrowth. Short-term radiation effects are described by allowing lethally irradiated cells up to one more cell division before apoptosis. Long-term radiation effects are described by an irreversible decrease in tumor growth rate. The radiosensitizing agent was assumed to stimulate both processes. The model also includes a natural death rate of cancer cells. The model was calibrated to the xenograft data using a mixed-effects approach based on the FOCE method that was implemented in Mathematica [3]. Between-subject variability was accounted for in initial tumor volume, as well as in the short- and long-term radiation effects. Results: Data across all treatment groups were well-described by the model. All model parameters were estimated with acceptable precision and biologically reasonable values. Vehicle growth was approximately exponential during the observed time period with an estimated tumor doubling time of approximately 5 days. Tumor growth following radiation therapy resulted in significant tumor regression followed by either tumor eradication (2 animals) or slow regrowth (7 animals). The short- and long-term effects incorporated into the tumor model were able to account for both of these scenarios. A simple analysis shows that if the tumor growth rate is decreased below the natural death rate, the tumor will be eradicated. Otherwise, the tumor will regrow but at a slower rate compared to pre-treatment. The model predicts that each fraction of radiation (2 Gy) results in lethal damage in 15 % of viable cells, and that a total dose above 120 Gy will eradicate the tumor. Tumor growth following combination therapy with a lower dose (25 mg/kg) resulted in more cases of tumor eradication (6 animals) and fewer cases of regrowth (3 animals), whereas combination therapy with the higher dose (100 mg/kg) resulted in tumor eradication in all 9 animals. When radiation therapy was complemented by radiosensitizing treatment (100 mg/kg per dose), each fraction of 2 Gy was estimated to kill 25 % of viable cells, and the total radiation dose required for tumor eradication was decreased by a factor four to 30 Gy. Conclusions: A tumor model has been developed to describe the treatment effects of radiation therapy, as well as combination therapies involving radiation, in tumor xenografts. The model distinguishes between short- and long-term effects of radiation treatment and can describe different tumor dynamics, including tumor eradication and tumor regrowth at different rates. The novel tumor model can be used to predict treatment outcomes for a broad range of treatments including radiation therapy and combination therapies with different radiosensitizing agents.
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