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| 3. |
- Agha, R. A., et al.
(författare)
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The SCARE 2018 statement: Updating consensus Surgical CAse REport (SCARE) guidelines
- 2018
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Ingår i: International Journal of Surgery. - : Ovid Technologies (Wolters Kluwer Health). - 1743-9191. ; 60, s. 132-136
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The SCARE Guidelines were published in 2016 to provide a structure for reporting surgical case reports. Since their publication, SCARE guidelines have been widely endorsed by authors, journal editors, and reviewers, and have helped to improve reporting transparency of case reports across a range of surgical specialties. In order to encourage further progress in reporting quality, the SCARE guidelines must themselves be kept up to date. We completed a Delphi consensus exercise to update the SCARE guidelines. Methods: A Delphi consensus exercise was undertaken. All members of the previous Delphi group were invited to participate, in addition to researchers who have previously studied case reports, and editors from the International Journal of Surgery Case Reports. The expert group was sent an online questionnaire where they were asked to rate their agreement with proposed changes to each of the 24 items. Results: 56 people agreed to participate and 45 (80%) invitees completed the survey which put forward modifications to the original guideline. The collated responses resulted in modifications. There was high agreement amongst the expert group. Conclusion: A modified and improved SCARE checklist is presented, after a Delphi consensus exercise was completed. The SCARE 2018 Statement: Updating Consensus Surgical CAse REport (SCARE) Guidelines. © 2018
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| 4. |
- Grill, G., et al.
(författare)
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Mapping the world's free-flowing rivers
- 2019
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Ingår i: Nature. - : Nature Publishing Group. - 0028-0836 .- 1476-4687. ; 569:7755, s. 215-221
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Tidskriftsartikel (refereegranskat)abstract
- Free-flowing rivers (FFRs) support diverse, complex and dynamic ecosystems globally, providing important societal and economic services. Infrastructure development threatens the ecosystem processes, biodiversity and services that these rivers support. Here we assess the connectivity status of 12 million kilometres of rivers globally and identify those that remain free-flowing in their entire length. Only 37 per cent of rivers longer than 1,000 kilometres remain free-flowing over their entire length and 23 per cent flow uninterrupted to the ocean. Very long FFRs are largely restricted to remote regions of the Arctic and of the Amazon and Congo basins. In densely populated areas only few very long rivers remain free-flowing, such as the Irrawaddy and Salween. Dams and reservoirs and their up- and downstream propagation of fragmentation and flow regulation are the leading contributors to the loss of river connectivity. By applying a new method to quantify riverine connectivity and map FFRs, we provide a foundation for concerted global and national strategies to maintain or restore them.
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| 5. |
- Evans, D. L., et al.
(författare)
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Sustainable futures over the next decade are rooted in soil science
- 2022
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Ingår i: European Journal of Soil Science. - : Wiley. - 1351-0754 .- 1365-2389. ; 73:1
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Tidskriftsartikel (refereegranskat)abstract
- The importance of soils to society has gained increasing recognition over the past decade, with the potential to contribute to most of the United Nations’ Sustainable Development Goals (SDGs). With unprecedented and growing demands for food, water and energy, there is an urgent need for a global effort to address the challenges of climate change and land degradation, whilst protecting soil as a natural resource. In this paper, we identify the contribution of soil science over the past decade to addressing gaps in our knowledge regarding major environmental challenges: climate change, food security, water security, urban development, and ecosystem functioning and biodiversity. Continuing to address knowledge gaps in soil science is essential for the achievement of the SDGs. However, with limited time and budget, it is also pertinent to identify effective methods of working that ensure the research carried out leads to real-world impact. Here, we suggest three strategies for the next decade of soil science, comprising a greater implementation of research into policy, interdisciplinary partnerships to evaluate function trade-offs and synergies between soils and other environmental domains, and integrating monitoring and modelling methods to ensure soil-based policies can withstand the uncertainties of the future. Highlights: We highlight the contributions of soil science to five major environmental challenges since 2010. Researchers have contributed to recommendation reports, but work is rarely translated into policy. Interdisciplinary work should assess trade-offs and synergies between soils and other domains. Integrating monitoring and modelling is key for robust and sustainable soils-based policymaking.
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| 6. |
- Gaetani, Lorenzo, et al.
(författare)
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Cerebrospinal fluid neurofilament light chain predicts disease activity after the first demyelinating event suggestive of multiple sclerosis
- 2019
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Ingår i: Multiple Sclerosis and Related Disorders. - : Elsevier BV. - 2211-0348 .- 2211-0356. ; 35, s. 228-232
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Tidskriftsartikel (refereegranskat)abstract
- Background: The prediction of disease activity in patients with a first demyelinating event suggestive of multiple sclerosis (MS) is of high clinical relevance. Cerebrospinal fluid (CSF) neurofilament light chain (NfL) has shown to have prognostic value in MS patients. In this work, we measured CSF NfL in patients at the first demyelinating event in order to find a cut-off value able to discriminate patients who will have disease activity from those who will remain stable during the follow-up. Methods: We included CSF samples collected within 30 days after the onset of the first demyelinating event from 32 patients followed-up for 3.8 ± 2.5 years. CSF NfL was measured with a newly developed in-house enzyme linked immunosorbent assay (ELISA). Results: At the first demyelinating event, patients with subsequent disease activity had significantly higher baseline CSF NfL values compared to clinically and radiologically stable patients (median 812.5 pg/mL, range 205–2359 pg/mL vs 329.5 pg/mL, range 156–3492 pg/mL, p = 0.002). A CSF NfL cut-off value of 500 pg/mL significantly discriminated these two groups of patients with a 90% sensitivity and an 83.3% specificity. Conclusion: Our results confirm that CSF NfL is a prognostic marker in the very early phases of MS. The validation of a cut-off value of 500 pg/mL could provide clinicians with a dichotomous variable that can simplify the prognostic assessment of patients at the first demyelinating event.
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| 7. |
- Gaetani, L., et al.
(författare)
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Cerebrospinal fluid neurofilament light chain tracks cognitive impairment in multiple sclerosis
- 2019
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Ingår i: Journal of Neurology. - : Springer Science and Business Media LLC. - 0340-5354 .- 1432-1459. ; 266:9, s. 2157-2163
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Tidskriftsartikel (refereegranskat)abstract
- Background Cognitive impairment (CI) is a disabling symptom of multiple sclerosis (MS). Axonal damage disrupts neural circuits and may play a role in determining CI, but its detection and monitoring are not routinely performed. Cerebrospinal fluid (CSF) neurofilament light chain (NfL) is a promising marker of axonal damage in MS. Objective To retrospectively examine the relationship between CSF NfL and CI in MS patients. Methods CSF NfL concentration was measured in 28 consecutive newly diagnosed MS patients who underwent a neuropsychological evaluation with the Brief Repeatable Battery of Neuropsychological tests (BRBN). Results CSF NfL was higher in patients with overall CI (947.8 +/- 400.7 vs 518.4 +/- 424.7 pg/mL, p < 0.01), and with impairment in information processing speed (IPS) (820.8 +/- 413.6 vs 513.6 +/- 461.4 pg/mL, p < 0.05) and verbal fluency (1292 +/- 511 vs 582.8 +/- 395.4 pg/mL, p < 0.05), and it positively correlated with the number of impaired BRBN tests (r = 0.48, p = 0.01) and cognitive domains (r = 0.47, p = 0.01). Multivariate analyses taking into account potential confounders confirmed these findings. Conclusion CSF NfL is higher in MS patients with CI and impaired IPS and verbal fluency. Large myelinated axons injury, causing neural disconnection, may be an important determinant of CI in MS and can be reliably measured through CSF NfL.
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| 10. |
- Polymeri, Erini, et al.
(författare)
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Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients
- 2021
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Ingår i: Scandinavian Journal of Urology. - : Medical Journals Sweden AB. - 2168-1805 .- 2168-1813. ; 55:6, s. 427-433
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Tidskriftsartikel (refereegranskat)abstract
- Objective Artificial intelligence (AI) offers new opportunities for objective quantitative measurements of imaging biomarkers from positron-emission tomography/computed tomography (PET/CT). Clinical image reporting relies predominantly on observer-dependent visual assessment and easily accessible measures like SUVmax, representing lesion uptake in a relatively small amount of tissue. Our hypothesis is that measurements of total volume and lesion uptake of the entire tumour would better reflect the disease`s activity with prognostic significance, compared with conventional measurements. Methods An AI-based algorithm was trained to automatically measure the prostate and its tumour content in PET/CT of 145 patients. The algorithm was then tested retrospectively on 285 high-risk patients, who were examined using F-18-choline PET/CT for primary staging between April 2008 and July 2015. Prostate tumour volume, tumour fraction of the prostate gland, lesion uptake of the entire tumour, and SUVmax were obtained automatically. Associations between these measurements, age, PSA, Gleason score and prostate cancer-specific survival were studied, using a Cox proportional-hazards regression model. Results Twenty-three patients died of prostate cancer during follow-up (median survival 3.8 years). Total tumour volume of the prostate (p = 0.008), tumour fraction of the gland (p = 0.005), total lesion uptake of the prostate (p = 0.02), and age (p = 0.01) were significantly associated with disease-specific survival, whereas SUVmax (p = 0.2), PSA (p = 0.2), and Gleason score (p = 0.8) were not. Conclusion AI-based assessments of total tumour volume and lesion uptake were significantly associated with disease-specific survival in this patient cohort, whereas SUVmax and Gleason scores were not. The AI-based approach appears well-suited for clinically relevant patient stratification and monitoring of individual therapy.
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| 11. |
- Polymeri, Erini, et al.
(författare)
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Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival
- 2020
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Ingår i: Clinical Physiology and Functional Imaging. - Chichester : Blackwell Publishing. - 1475-0961 .- 1475-097X. ; 40:2, s. 106-113
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine
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| 13. |
- Borrelli, P., et al.
(författare)
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AI-based detection of lung lesions in F-18 FDG PET-CT from lung cancer patients
- 2021
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Ingår i: Ejnmmi Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Background[F-18]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI's usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT.MethodsOne hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots.ResultsThe AI-tool's performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R-2 = 0.74). Bias was 42 g and 95% limits of agreement ranged from -736 to 819 g. Agreement was particularly high in smaller lesions.ConclusionsThe AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.
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| 14. |
- Borrelli, P., et al.
(författare)
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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study
- 2021
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Ingår i: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundBody composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p <0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of 20%.Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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| 15. |
- Borrelli, P., et al.
(författare)
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Automated classification of PET-CT lesions in lung cancer: An independent validation study
- 2022
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Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 42:5, s. 327-332
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Recently, a tool called the positron emission tomography (PET)-assisted reporting system (PARS) was developed and presented to classify lesions in PET/computed tomography (CT) studies in patients with lung cancer or lymphoma. The aim of this study was to validate PARS with an independent group of lung-cancer patients using manual lesion segmentations as a reference standard, as well as to evaluate the association between PARS-based measurements and overall survival (OS). Methods This study retrospectively included 115 patients who had undergone clinically indicated (18F)-fluorodeoxyglucose (FDG) PET/CT due to suspected or known lung cancer. The patients had a median age of 66 years (interquartile range [IQR]: 61-72 years). Segmentations were made manually by visual inspection in a consensus reading by two nuclear medicine specialists and used as a reference. The research prototype PARS was used to automatically analyse all the PET/CT studies. The PET foci classified as suspicious by PARS were compared with the manual segmentations. No manual corrections were applied. Total lesion glycolysis (TLG) was calculated based on the manual and PARS-based lung-tumour segmentations. Associations between TLG and OS were investigated using Cox analysis. Results PARS showed sensitivities for lung tumours of 55.6% per lesion and 80.2% per patient. Both manual and PARS TLG were significantly associated with OS. Conclusion Automatically calculated TLG by PARS contains prognostic information comparable to manually measured TLG in patients with known or suspected lung cancer. The low sensitivity at both the lesion and patient levels makes the present version of PARS less useful to support clinical reading, reporting and staging.
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| 16. |
- Borrelli, P., et al.
(författare)
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Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer
- 2022
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Ingår i: EJNMMI Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Metabolic positron emission tomography/computed tomography (PET/CT) parameters describing tumour activity contain valuable prognostic information, but to perform the measurements manually leads to both intra- and inter-reader variability and is too time-consuming in clinical practice. The use of modern artificial intelligence-based methods offers new possibilities for automated and objective image analysis of PET/CT data. Purpose: We aimed to train a convolutional neural network (CNN) to segment and quantify tumour burden in [18F]-fluorodeoxyglucose (FDG) PET/CT images and to evaluate the association between CNN-based measurements and overall survival (OS) in patients with lung cancer. A secondary aim was to make the method available to other researchers. Methods: A total of 320 consecutive patients referred for FDG PET/CT due to suspected lung cancer were retrospectively selected for this study. Two nuclear medicine specialists manually segmented abnormal FDG uptake in all of the PET/CT studies. One-third of the patients were assigned to a test group. Survival data were collected for this group. The CNN was trained to segment lung tumours and thoracic lymph nodes. Total lesion glycolysis (TLG) was calculated from the CNN-based and manual segmentations. Associations between TLG and OS were investigated using a univariate Cox proportional hazards regression model. Results: The test group comprised 106 patients (median age, 76years (IQR 61–79); n = 59 female). Both CNN-based TLG (hazard ratio 1.64, 95% confidence interval 1.21–2.21; p = 0.001) and manual TLG (hazard ratio 1.54, 95% confidence interval 1.14–2.07; p = 0.004) estimations were significantly associated with OS. Conclusion: Fully automated CNN-based TLG measurements of PET/CT data showed were significantly associated with OS in patients with lung cancer. This type of measurement may be of value for the management of future patients with lung cancer. The CNN is publicly available for research purposes. © 2022, The Author(s).
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| 17. |
- BORRELLI, S, et al.
(författare)
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Monoclonal antibodies against Haemophilus lipopolysaccharides: clone DP8 specific for Haemophilus ducreyi and clone DH24 binding to lacto-N-neotetraose
- 1995
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Ingår i: Infection and immunity. - : American Society for Microbiology. - 0019-9567 .- 1098-5522. ; 63:7, s. 2665-2673
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Tidskriftsartikel (refereegranskat)abstract
- Mouse monoclonal antibodies (MAbs) DP8 [immunoglobulin G1(kappa)] and DH24 [immunoglobulin M(kappa)], which are specific for Haemophilus ducreyi lipopolysaccharide (LPS), were generated by fusing mouse myeloma NS0 cells with spleen cells of BALB/c mice immunized with a total membrane preparation of H. ducreyi. MAb DP8 reacted in whole-cell enzyme immunoassay (EIA) and colony dot immunoblotting with all 50 strains of H. ducreyi but not with any other bacteria tested, which suggests an exposed and species-specific epitope on the H. ducreyi cell surface. This conclusion was supported by the finding that DP8 bound to all six H. ducreyi LPSs tested but not to any of the Haemophilus influenzae or enterobacterial LPSs or synthetic glycoconjugates. The MAb DH24 bound to 43 of 50 strains of H. ducreyi and to few strains of H. influenzae, Neisseria gonorrhoeae, and Neisseria meningitidis, as evaluated by whole-cell EIA and colony dot immunoblotting. The MAb DH24 reacted with five of the six H. ducreyi LPSs tested and with the lacto-N-neotetraose (Gal beta 1-->4GlcNAc beta 1-->3Gal beta 1-->4Glc) series of synthetic glycoconjugates, as determined by EIA. By using polysaccharides obtained after both mild acidic hydrolysis and strong alkali treatment and dephosphorylated samples as inhibitors of the MAbs binding to H. ducreyi LPS antigens, it could be shown that phosphate groups were essential for the binding of DP8 to LPS but that they did not affect antigenic recognition by DH24. None of the MAbs bound to isolated lipid A, but aggregation caused by the fatty acids of lipid A was essential for epitope recognition.
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| 18. |
- Cocozza, S., et al.
(författare)
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Redefining the Pulvinar Sign in Fabry Disease
- 2017
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Ingår i: American Journal of Neuroradiology. - 0195-6108 .- 1936-959X. ; 38:12, s. 2264-2269
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND PURPOSE:The pulvinar sign refers to exclusive T1WI hyperintensity of the lateral pulvinar. Long considered a common sign of Fabry disease, the pulvinar sign has been reported in many pathologic conditions. The exact incidence of the pulvinar sign has never been tested in representative cohorts of patients with Fabry disease. The aim of this study was to assess the prevalence of the pulvinar sign in Fabry disease by analyzing T1WI in a large Fabry disease cohort, determining whether relaxometry changes could be detected in this region independent of the pulvinar sign positivity.MATERIALS AND METHODS:We retrospectively analyzed brain MR imaging of 133 patients with Fabry disease recruited through specialized care clinics. A subgroup of 26 patients underwent a scan including 2 FLASH sequences for relaxometry that were compared with MRI scans of 34 healthy controls.RESULTS:The pulvinar sign was detected in 4 of 133 patients with Fabry disease (3.0%). These 4 subjects were all adult men (4 of 53, 7.5% of the entire male population) with renal failure and under enzyme replacement therapy. When we tested for discrepancies between Fabry disease and healthy controls in quantitative susceptibility mapping and relaxometry maps, no significant difference emerged for any of the tested variables.CONCLUSIONS:The pulvinar sign has a significantly lower incidence in Fabry disease than previously described. This finding, coupled with a lack of significant differences in quantitative MR imaging, allows hypothesizing that selective involvement of the pulvinar is a rare neuroradiologic sign of Fabry disease.
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| 21. |
- Mortensen, Mike A., et al.
(författare)
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Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study
- 2019
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Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 39:6, s. 399-406
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Tidskriftsartikel (refereegranskat)abstract
- Aim : To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). Methods : A convolutional neural network (CNN) was trained for automated measurements in 18F-choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUVmax), mean standardized uptake value of voxels considered abnormal (SUVmean) and volume of abnormal voxels (Volabn). The product SUVmean × Volabn was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed. CNN-estimated data were compared with the weighted surgically removed tissue specimens and manually derived data and related to clinical parameters assuming that 1 g ≈ 1 ml of tissue. Results : The mean (range) weight of the prostate specimens was 44 g (20–109), while CNN-estimated volume was 62 ml (31–108) with a mean difference of 13·5 g or ml (95% CI: 9·78–17·32). The two measures were significantly correlated (r = 0·77, P<0·001). Mean differences (95% CI) between CNN-based and manually derived PET measures of SUVmax, SUVmean, Volabn (ml) and TLU were 0·37 (−0·01 to 0·75), −0·08 (−0·30 to 0·14), 1·40 (−2·26 to 5·06) and 9·61 (−3·95 to 23·17), respectively. PET findings Volabn and TLU correlated with PSA (P<0·05), but not with Gleason score or stage. Conclusion : Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate-specific membrane antigen are warranted.
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| 22. |
- O'Rourke, Joseph G., et al.
(författare)
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Venus, the Planet : Introduction to the Evolution of Earth's Sister Planet
- 2023
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Ingår i: Space Science Reviews. - : Springer. - 0038-6308 .- 1572-9672. ; 219:1
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Forskningsöversikt (refereegranskat)abstract
- Venus is the planet in the Solar System most similar to Earth in terms of size and (probably) bulk composition. Until the mid-20th century, scientists thought that Venus was a verdant world-inspiring science-fictional stories of heroes battling megafauna in sprawling jungles. At the start of the Space Age, people learned that Venus actually has a hellish surface, baked by the greenhouse effect under a thick, CO2-rich atmosphere. In popular culture, Venus was demoted from a jungly playground to (at best) a metaphor for the redemptive potential of extreme adversity. However, whether Venus was much different in the past than it is today remains unknown. In this review, we show how now-popular models for the evolution of Venus mirror how the scientific understanding of modern Venus has changed over time. Billions of years ago, Venus could have had a clement surface with water oceans. Venus perhaps then underwent at least one dramatic transition in atmospheric, surface, and interior conditions before present day. This review kicks off a topical collection about all aspects of Venus's evolution and how understanding Venus can teach us about other planets, including exoplanets. Here we provide the general background and motivation required to delve into the other manuscripts in this collection. Finally, we discuss how our ignorance about the evolution of Venus motivated the prioritization of new spacecraft missions that will rediscover Earth's nearest planetary neighbor-beginning a new age of Venus exploration.
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| 23. |
- Trägårdh, Elin, et al.
(författare)
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RECOMIA-a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
- 2020
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Ingår i: Ejnmmi Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image. Results: The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones). Conclusion: The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request atfor research purposes.
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| 24. |
- Ying, T. M., et al.
(författare)
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Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
- 2021
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Ingår i: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Radical cystectomy for urinary bladder cancer is a procedure associated with a high risk of complications, and poor overall survival (OS) due to both patient and tumour factors. Sarcopenia is one such patient factor. We have developed a fully automated artificial intelligence (AI)-based image analysis tool for segmenting skeletal muscle of the torso and calculating the muscle volume. Methods All patients who have undergone radical cystectomy for urinary bladder cancer 2011-2019 at Sahlgrenska University Hospital, and who had a pre-operative computed tomography of the abdomen within 90 days of surgery were included in the study. All patients CT studies were analysed with the automated AI-based image analysis tool. Clinical data for the patients were retrieved from the Swedish National Register for Urinary Bladder Cancer. Muscle volumes dichotomised by the median for each sex were analysed with Cox regression for OS and logistic regression for 90-day high-grade complications. The study was approved by the Swedish Ethical Review Authority (2020-03985). Results Out of 445 patients who underwent surgery, 299 (67%) had CT studies available for analysis. The automated AI-based tool failed to segment the muscle volume in seven (2%) patients. Cox regression analysis showed an independent significant association with OS (HR 1.62; 95% CI 1.07-2.44; p = 0.022). Logistic regression did not show any association with high-grade complications. Conclusion The fully automated AI-based CT image analysis provides a low-cost and meaningful clinical measure that is an independent biomarker for OS following radical cystectomy.
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