| 1. |
- Colvill, Emma, et al.
(författare)
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A dosimetric comparison of real-time adaptive and non-adaptive radiotherapy : A multi-institutional study encompassing robotic, gimbaled, multileaf collimator and couch tracking
- 2016
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Ingår i: Radiotherapy and Oncology. - : Elsevier BV. - 0167-8140. ; 119:1, s. 159-165
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Tidskriftsartikel (refereegranskat)abstract
- Purpose A study of real-time adaptive radiotherapy systems was performed to test the hypothesis that, across delivery systems and institutions, the dosimetric accuracy is improved with adaptive treatments over non-adaptive radiotherapy in the presence of patient-measured tumor motion. Methods and materials Ten institutions with robotic(2), gimbaled(2), MLC(4) or couch tracking(2) used common materials including CT and structure sets, motion traces and planning protocols to create a lung and a prostate plan. For each motion trace, the plan was delivered twice to a moving dosimeter; with and without real-time adaptation. Each measurement was compared to a static measurement and the percentage of failed points for γ-tests recorded. Results For all lung traces all measurement sets show improved dose accuracy with a mean 2%/2 mm γ-fail rate of 1.6% with adaptation and 15.2% without adaptation (p < 0.001). For all prostate the mean 2%/2 mm γ-fail rate was 1.4% with adaptation and 17.3% without adaptation (p < 0.001). The difference between the four systems was small with an average 2%/2 mm γ-fail rate of <3% for all systems with adaptation for lung and prostate. Conclusions The investigated systems all accounted for realistic tumor motion accurately and performed to a similar high standard, with real-time adaptation significantly outperforming non-adaptive delivery methods.
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| 2. |
- Brodin, N Patrik, et al.
(författare)
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Hippocampal sparing radiotherapy for pediatric medulloblastoma: impact of treatment margins and treatment technique.
- 2014
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Ingår i: Neuro-oncology. - : Oxford University Press (OUP). - 1523-5866 .- 1522-8517. ; 16:4, s. 594-602
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundWe investigated how varying the treatment margin and applying hippocampal sparing and proton therapy impact the risk of neurocognitive impairment in pediatric medulloblastoma patients compared with current standard 3D conformal radiotherapy.MethodsWe included 17 pediatric medulloblastoma patients to represent the variability in tumor location relative to the hippocampal region. Treatment plans were generated using 3D conformal radiotherapy, hippocampal sparing intensity-modulated radiotherapy, and spot-scanned proton therapy, using 3 different treatment margins for the conformal tumor boost. Neurocognitive impairment risk was estimated based on dose-response models from pediatric CNS malignancy survivors and compared among different margins and treatment techniques.ResultsMean hippocampal dose and corresponding risk of cognitive impairment were decreased with decreasing treatment margins (P < .05). The largest risk reduction, however, was seen when applying hippocampal sparing proton therapy-the estimated risk of impaired task efficiency (95% confidence interval) was 92% (66%-98%), 81% (51%-95%), and 50% (30%-70%) for 3D conformal radiotherapy, intensity-modulated radiotherapy, and proton therapy, respectively, for the smallest boost margin and 98% (78%-100%), 90% (60%-98%), and 70% (39%-90%) if boosting the whole posterior fossa. Also, the distance between the closest point of the planning target volume and the center of the hippocampus can be used to predict mean hippocampal dose for a given treatment technique.ConclusionsWe estimate a considerable clinical benefit of hippocampal sparing radiotherapy. In choosing treatment margins, the tradeoff between margin size and risk of neurocognitive impairment quantified here should be considered.
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| 3. |
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| 4. |
- Lempart, Michael, et al.
(författare)
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A deeply supervised convolutional neural network ensemble for multilabel segmentation of pelvic OARs
- 2021
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Ingår i: Radiotherapy and Oncology. - 1879-0887. ; 161:Suppl 1, s. 1417-1418
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Accurate delineation of organs at risk (OAR) is a crucial step in radiation therapy (RT) treatment planning but is a manual and time-consuming process. Deep learning-based methods have shown promising results for medical image segmentation and can be used to accelerate this task. Nevertheless, it is rarely applied to complex structures found in the pelvis region, where manual segmentation can be difficult, costly and is not always feasible. The aim of this study was to train and validate a model, based on a modified U-Net architecture, for automated and improved multilabel segmentation of 10 pelvic OAR structures (total bone marrow, lower pelvis bone marrow, iliac bone marrow, lumosacral bone marrow, bowel cavity, bowel, small bowel, large bowel, rectum, and bladder).
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| 5. |
- Lempart, Michael, et al.
(författare)
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Pelvic U-Net : multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network
- 2022
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Ingår i: Radiation Oncology. - : Springer Science and Business Media LLC. - 1748-717X. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. Methods: A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1–4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. Results: Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. Conclusions: Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.
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| 6. |
- Lempart, Michael, et al.
(författare)
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Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model
- 2021
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Ingår i: Physics and imaging in radiation oncology. - : Elsevier BV. - 2405-6316. ; 19, s. 112-119
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose: Radiation therapy treatment planning is a manual, time-consuming task that might be accelerated using machine learning algorithms. In this study, we aimed to evaluate if a triplet-based deep learning model can predict volumetric modulated arc therapy (VMAT) dose distributions for prostate cancer patients. Materials and methods: A modified U-Net was trained on triplets, a combination of three consecutive image slices and corresponding segmentations, from 160 patients, and compared to a baseline U-Net. Dose predictions from 17 test patients were transformed into deliverable treatment plans using a novel planning workflow. Results: The model achieved a mean absolute dose error of 1.3%, 1.9%, 1.0% and ≤ 2.6% for clinical target volume (CTV) CTV_D100%, planning target volume (PTV) PTV_D98%, PTV_D95% and organs at risk (OAR) respectively, when compared to the clinical ground truth (GT) dose distributions. All predicted distributions were successfully transformed into deliverable treatment plans and tested on a phantom, resulting in a passing rate of 100% (global gamma, 3%, 2 mm, 15% cutoff). The dose difference between deliverable treatment plans and GT dose distributions was within 4.4%. The difference between the baseline model and our improved model was statistically significant (p < 0.05) for CVT_D100%, PTV_D98% and PTV_D95%. Conclusion: Triplet-based training improved VMAT dose distribution predictions when compared to 2D. Dose predictions were successfully transformed into deliverable treatment plans using our proposed treatment planning procedure. Our method may automate parts of the workflow for external beam prostate radiation therapy and improve the overall treatment speed and plan quality.
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| 7. |
- Rosenkilde, Carola E. H., et al.
(författare)
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Collateral sensitivity constrains resistance evolution of the CTX-M-15 beta-lactamase
- 2019
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Ingår i: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Antibiotic resistance is a major challenge to global public health. Discovery of new antibiotics is slow and to ensure proper treatment of bacterial infections new strategies are needed. One way to curb the development of antibiotic resistance is to design drug combinations where the development of resistance against one drug leads to collateral sensitivity to the other drug. Here we study collateral sensitivity patterns of the globally distributed extended-spectrum beta-lactamase CTX-M-15, and find three non-synonymous mutations with increased resistance against mecillinam or piperacillin-tazobactam that simultaneously confer full susceptibility to several cephalosporin drugs. We show in vitro and in mice that a combination of mecillinam and cefotaxime eliminates both wild-type and resistant CTX-M-15. Our results indicate that mecillinam and cefotaxime in combination constrain resistance evolution of CTX-M-15, and illustrate how drug combinations can be rationally designed to limit the resistance evolution of horizontally transferred genes by exploiting collateral sensitivity patterns.
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| 8. |
- Rugbjerg, Peter, 1988, et al.
(författare)
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Short and long-read ultra-deep sequencing profiles emerging heterogeneity across five platform Escherichia coli strains
- 2021
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Ingår i: Metabolic Engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 65, s. 197-206
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Tidskriftsartikel (refereegranskat)abstract
- Reprogramming organisms for large-scale bioproduction counters their evolutionary objectives of fast growth and often leads to mutational collapse of the engineered production pathways during cultivation. Yet, the mutational susceptibility of academic and industrial Escherichia coli bioproduction host strains are poorly understood. In this study, we apply 2nd and 3rd generation deep sequencing to profile simultaneous modes of genetic heterogeneity that decimate engineered biosynthetic production in five popular E. coli hosts BL21(DE3), TOP10, MG1655, W, and W3110 producing 2,3-butanediol and mevalonic acid. Combining short-read and longread sequencing, we detect strain and sequence-specific mutational modes including single nucleotide polymorphism, inversion, and mobile element transposition, as well as complex structural variations that disrupt the integrity of the engineered biosynthetic pathway. Our analysis suggests that organism engineers should avoid chassis strains hosting active insertion sequence (IS) subfamilies such as IS1 and IS10 present in popular E. coli TOP10. We also recommend monitoring for increased mutagenicity in the pathway transcription initiation regions and recombinogenic repeats. Together, short and long sequencing reads identified latent low-frequency mutation events such as a short detrimental inversion within a pathway gene, driven by 8-bp short inverted repeats. This demonstrates the power of combining ultra-deep DNA sequencing technologies to profile genetic heterogeneities of engineered constructs and explore the markedly different mutational landscapes of common E. coli host strains. The observed multitude of evolving variants underlines the usefulness of early mutational profiling for new synthetic pathways designed to sustain in organisms over long cultivation scales.
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| 9. |
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| 10. |
- Sommer, Morten O. A., et al.
(författare)
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Prediction of antibiotic resistance : time for a new preclinical paradigm?
- 2017
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Ingår i: Nature Reviews Microbiology. - : Springer Science and Business Media LLC. - 1740-1526 .- 1740-1534. ; 15:11, s. 688-695
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Forskningsöversikt (refereegranskat)abstract
- Predicting the future is difficult, especially for evolutionary processes that are influenced by numerous unknown factors. Still, this is what is required of drug developers when they assess the risk of resistance arising against a new antibiotic candidate during preclinical development. In this Opinion article, we argue that the traditional procedures that are used for the prediction of antibiotic resistance today could be markedly improved by including a broader analysis of bacterial fitness, infection dynamics, horizontal gene transfer and other factors. This will lead to more informed preclinical decisions for continuing or discontinuing the development of drug candidates.
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