| 1. |
- Nyholm, Tufve, et al.
(författare)
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A national approach for automated collection of standardized and population-based radiation therapy data in Sweden
- 2016
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Ingår i: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140 .- 1879-0887. ; 119:2, s. 344-350
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To develop an infrastructure for structured and automated collection of interoperable radiation therapy (RT) data into a national clinical quality registry. Materials and methods: The present study was initiated in 2012 with the participation of seven of the 15 hospital departments delivering RT in Sweden. A national RT nomenclature and a database for structured unified storage of RT data at each site (Medical Information Quality Archive, MIQA) have been developed. Aggregated data from the MIQA databases are sent to a national RT registry located on the same IT platform (INCA) as the national clinical cancer registries. Results: The suggested naming convention has to date been integrated into the clinical workflow at 12 of 15 sites, and MIQA is installed at six of these. Involvement of the remaining 3/15 RT departments is ongoing, and they are expected to be part of the infrastructure by 2016. RT data collection from ARIA (R), Mosaiq (R), Eclipse (TM), and Oncentra (R) is supported. Manual curation of RT-structure information is needed for approximately 10% of target volumes, but rarely for normal tissue structures, demonstrating a good compliance to the RT nomenclature. Aggregated dose/volume descriptors are calculated based on the information in MIQA and sent to INCA using a dedicated service (MIQA2INCA). Correct linkage of data for each patient to the clinical cancer registries on the INCA platform is assured by the unique Swedish personal identity number. Conclusions: An infrastructure for structured and automated prospective collection of syntactically inter operable RT data into a national clinical quality registry for RT data is under implementation. Future developments include adapting MIQA to other treatment modalities (e.g. proton therapy and brachytherapy) and finding strategies to harmonize structure delineations. How the RT registry should comply with domain-specific ontologies such as the Radiation Oncology Ontology (ROO) is under discussion.
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| 2. |
- 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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| 3. |
- 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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| 4. |
- 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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| 5. |
- Dahlberg, Johan, 1988-
(författare)
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Genetic Cartography at Massively Parallel Scale
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massively parallel sequencing (MPS) is revolutionizing genomics. In this work we use, refine, and develop new tools for the discipline.MPS has led to the discovery of multiple novel subtypes in Acute Lymphoblastic Leukemia (ALL). In Study I we screen for fusion genes in 134 pediatric ALL patients, including patients without an assigned subtype. In approximately 80% of these patients we detect novel or known fusion gene families, most of which display distinct methylation and expression patterns. This shows the potential for improvements in the clinical stratification of ALL. Large sample sizes are important to detect recurrent somatic variation. In Study II we investigate if a non-index overlapping pooling schema can be used to increase sample size and detect somatic variation. We designed a schema for 172 ALL samples and show that it is possible to use this method to call somatic variants.Around the globe there are many ongoing and completed genome projects. In Study III we sequenced the genome of 1000 Swedes to create a reference data set for the Swedish population. We identified more than 10 million variants that were not present in publicly available databases, highlighting the need for population-specific resources. Data, and the tools developed during this study, have been made publicly available as a resource for genomics in Sweden and abroad.The increased amount of sequencing data has created a greater need for automation. In Study IV we present Arteria, a computational automation system for sequencing core facilities. This system has been adopted by multiple facilities and has been used to analyze thousands of samples. In Study V we developed CheckQC, a program that provides automated quality control of Illumina sequencing runs. These tools make scaling up MPS less labour intensive, a key to unlocking the full future potential of genomics.The tools, and data presented here are a valuable contribution to the scientific community. Collectively they showcase the power of MPS and genomics to bring about new knowledge of human health and disease.
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| 6. |
- Lindgren Belal, Sarah, et al.
(författare)
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3D skeletal uptake of F-18 sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer
- 2017
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Ingår i: EJNMMI Research. - : Springer Science and Business Media LLC. - 2191-219X. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Sodium fluoride (NaF) positron emission tomography combined with computer tomography (PET/CT) has shown to be more sensitive than the whole-body bone scan in the detection of skeletal uptake due to metastases in prostate cancer. We aimed to calculate a 3D index for NaF PET/CT and investigate its correlation to the bone scan index (BSI) and overall survival (OS) in a group of patients with prostate cancer. Methods: NaF PET/CT and bone scans were studied in 48 patients with prostate cancer. Automated segmentation of the thoracic and lumbar spines, sacrum, pelvis, ribs, scapulae, clavicles, and sternum were made in the CT images. Hotspots in the PET images were selected using both a manual and an automated method. The volume of each hotspot localized in the skeleton in the corresponding CT image was calculated. Two PET/CT indices, based on manual (manual PET index) and automatic segmenting using a threshold of SUV 15 (automated PET15 index), were calculated by dividing the sum of all hotspot volumes with the volume of all segmented bones. BSI values were obtained using a software for automated calculations. Results: BSI, manual PET index, and automated PET15 index were all significantly associated with OS and concordance indices were 0.68, 0.69, and 0.70, respectively. The median BSI was 0.39 and patients with a BSI > 0.39 had a significantly shorter median survival time than patients with a BSI 0.53 had a significantly shorter median survival time than patients with a manual PET index 0.11 had a significantly shorter median survival time than patients with an automated PET15 index
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| 7. |
- Lindgren Belal, Sarah, et al.
(författare)
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Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases
- 2019
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Ingår i: European Journal of Radiology. - : Elsevier BV. - 0720-048X .- 1872-7727. ; 113, s. 89-95
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden. Methods: Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone 18 F-choline-PET/CT and 18 F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network's performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method. Results: The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5–6% for the vertebral column and ribs and ≤3% for other bones. Conclusion: The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.
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| 8. |
- Singh, Priyanka, et al.
(författare)
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Gold nanoparticles in diagnostics and therapeutics for human cancer
- 2018
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Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1661-6596 .- 1422-0067. ; 19:7
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Forskningsöversikt (refereegranskat)abstract
- The application of nanotechnology for the treatment of cancer is mostly based on early tumor detection and diagnosis by nanodevices capable of selective targeting and delivery of chemotherapeutic drugs to the specific tumor site. Due to the remarkable properties of gold nanoparticles, they have long been considered as a potential tool for diagnosis of various cancers and for drug delivery applications. These properties include high surface area to volume ratio, surface plasmon resonance, surface chemistry and multi-functionalization, facile synthesis, and stable nature. Moreover, the non-toxic and non-immunogenic nature of gold nanoparticles and the high permeability and retention effect provide additional benefits by enabling easy penetration and accumulation of drugs at the tumor sites. Various innovative approaches with gold nanoparticles are under development. In this review, we provide an overview of recent progress made in the application of gold nanoparticles in the treatment of cancer by tumor detection, drug delivery, imaging, photothermal and photodynamic therapy and their current limitations in terms of bioavailability and the fate of the nanoparticles.
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| 9. |
- Turanli, Beste, et al.
(författare)
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Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer
- 2019
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Ingår i: Frontiers in Genetics. - : Frontiers Media SA. - 1664-8021. ; 10:MAY
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Tidskriftsartikel (refereegranskat)abstract
- Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.
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| 10. |
- Gustafsson, Christian, et al.
(författare)
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Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy
- 2017
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Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 44:11, s. 5563-5574
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The superior soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) has urged the integration of MRI and elimination of CT in radiotherapy treatment (RT) for prostate. An intraprostatic gold fiducial marker (GFM) appears hyperintense on CT. On T2-weighted (T2w) MRI target delineation images, the GFM appear as a small signal void similar to calcifications and post biopsy fibrosis. It can therefore be difficult to identify the markers without CT. Detectability of GFMs can be improved using additional MR images, which are manually registered to target delineation images. This task requires manual labor, and is associated with interoperator differences and image registration errors. The aim of this work was to develop and evaluate an automatic method for identification of GFMs directly in the target delineation images without the need for image registration.Methods: T2w images, intended for target delineation, and multiecho gradient echo (MEGRE) images intended for GFM identification, were acquired for prostate cancer patients. Signal voids in the target delineation images were identified as GFM candidates. The GFM appeared as round, symmetric, signal void with increasing area for increasing echo time in the MEGRE images. These image features were exploited for automatic identification of GFMs in a MATLAB model using a patient training dataset (n = 20). The model was validated on an independent patient dataset (n = 40). The distances between the identified GFM in the target delineation images and the GFM in CT images were measured. A human observatory study was conducted to validate the use of MEGRE images.Results: The sensitivity, specificity, and accuracy of the automatic method and the observatory study was 84%, 74%, 81% and 98%, 94%, 97%, respectively. The mean absolute difference in the GFM distances for the automatic method and observatory study was 1.28 1.25 mm and 1.14 +/- 1.06 mm, respectively.Conclusions: Multiecho gradient echo images were shown to be a feasible and reliable way to perform GFM identification. For clinical practice, visual inspection of the results from the automatic method is needed at the current stage.
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