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Liu, Xijia
Alternative names
Publications (9 of 9) Show all publications
Wu, W.-Y. Y., Törnberg, S., Elfström, K. M., Liu, X., Nyström, L. & Jonsson, H. (2018). Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up. Breast Cancer Research, 20, Article ID 153.
Open this publication in new window or tab >>Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up
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2018 (English)In: Breast Cancer Research, ISSN 1465-5411, E-ISSN 1465-542X, Vol. 20, article id 153Article in journal (Refereed) Published
Abstract [en]

Background: Overdiagnosis, defined as the detection of a cancer that would not become clinically apparent in a woman’s lifetime without screening, has become a growing concern. Similar underlying risk of breast cancer in the screened and control groups is a prerequisite for unbiased estimates of overdiagnosis, but a contemporary control group is usually not available in organized screening programs.

Methods: We estimated the frequency of overdiagnosis of breast cancer due to screening in women 50–69 years old by using individual screening data from the population-based organized screening program in Stockholm County 1989–2014. A hidden Markov model with four latent states and three observed states was constructed to estimate the natural progression of breast cancer and the test sensitivity. Piecewise transition rates were used to consider the time-varying transition rates. The expected number of detected non-progressive breast cancer cases was calculated.

Results: During the study period, 2,333,153 invitations were sent out; on average, the participation rate in the screening program was 72.7% and the average recall rate was 2.48%. In total, 14,648 invasive breast cancer cases were diagnosed; among the 8305 screen-detected cases, the expected number of non-progressive breast cancer cases was 35.9, which is equivalent to 0.43% (95% confidence interval (CI) 0.10%–2.2%) overdiagnosis. The corresponding estimates for the prevalent and subsequent rounds were 15.6 (0.87%, 95% CI 0.20%–4.3%) and 20.3 (0.31%, 95% CI 0.07%–1.6%), respectively. The likelihood ratio test showed that the non-homogeneous model fitted the data better than an age-homogeneous model (P<0.001).

Conclusions: Our findings suggest that overdiagnosis in the organized biennial mammographic screening for women 50–69 in Stockholm County is a minor phenomenon. The frequency of overdiagnosis in the prevalent screening round was higher than that in subsequent rounds. The non-homogeneous model performed better than the simpler, traditional homogeneous model.

Place, publisher, year, edition, pages
BioMed Central, 2018
Keywords
Overdiagnosis, Breast cancer, Organized screening program, Mammography, Multi-state model
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-155103 (URN)10.1186/s13058-018-1082-z (DOI)000453551900001 ()30558679 (PubMedID)
Funder
Swedish Research CouncilVästerbotten County Council
Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-01-10Bibliographically approved
Bayisa, F., Liu, X., Garpebring, A. & Yu, J. (2018). Statistical learning in computed tomography image estimation. Medical physics (Lancaster), 45(12), 5450-5460
Open this publication in new window or tab >>Statistical learning in computed tomography image estimation
2018 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, no 12, p. 5450-5460Article in journal (Refereed) Published
Abstract [en]

Purpose: There is increasing interest in computed tomography (CT) image estimations from magneticresonance (MR) images. The estimated CT images can be utilized for attenuation correction, patientpositioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introducea novel statistical learning approach for improving CT estimation from MR images and to compare theperformance of our method with the existing model-based CT image estimation methods.

Methods: The statistical learning approach proposed here consists of two stages. At the trainingstage, prior knowledge about tissue types from CT images was used together with a Gaussian mixturemodel (GMM) to explore CT image estimations from MR images. Since the prior knowledge is notavailable at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimatethe tissue types from MR images. For a new patient, the trained classifier and GMMs were used topredict CT image from MR images. The classifier and GMMs were validated by using voxel-leveltenfold cross-validation and patient-level leave-one-out cross-validation, respectively.

Results: The proposed approach has outperformance in CT estimation quality in comparison withthe existing model-based methods, especially on bone tissues. Our method improved CT image estimationby 5% and 23% on the whole brain and bone tissues, respectively.

Conclusions: Evaluation of our method shows that it is a promising method to generate CTimage substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
Computed tomography, CT image estimation, Gaussian mixture model, magnetic resonance imaging, supervised learning
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-153283 (URN)10.1002/mp.13204 (DOI)000452799400010 ()30242845 (PubMedID)2-s2.0-85056189706 (Scopus ID)
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2019-01-07Bibliographically approved
Wang, J., Garpebring, A., Brynolfsson, P., Liu, X. & Yu, J. (2016). Contrast agent quantification by using spatial information in dynamic contrast enhanced MRI.
Open this publication in new window or tab >>Contrast agent quantification by using spatial information in dynamic contrast enhanced MRI
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2016 (English)Manuscript (preprint) (Other academic)
Abstract [en]

The purpose of this study is to investigate a method, using simulations, toimprove contrast agent quantication in Dynamic Contrast Enhanced MRI.Bayesian hierarchical models (BHMs) are applied to smaller images such that spatial information can be incorporated. Then exploratory analysisis done for larger images by using maximum a posteriori (MAP).

For smaller images: the estimators of proposed BHMs show improvementsin terms of the root mean squared error compared to the estimators in existingmethod for a noise level equivalent of a 12-channel head coil at 3T. Moreover,Leroux model outperforms Besag models. For larger images: MAP estimatorsalso show improvements by assigning Leroux prior.

Publisher
p. 12
Keywords
Contrast agent quantication, BHM, Besag, Leroux, INLA, MAP
National Category
Probability Theory and Statistics Medical Image Processing
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-141525 (URN)
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2019-11-19
Liu, X. & Preve, D. (2016). Measure of location-based estimators in simple linear regression. Journal of Statistical Computation and Simulation, 86(9), 1771-1784
Open this publication in new window or tab >>Measure of location-based estimators in simple linear regression
2016 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 86, no 9, p. 1771-1784Article in journal (Refereed) Published
Abstract [en]

In this note we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The median-unbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the MLBEs. In the first case, the regressor and error is assumed to follow a symmetric stable distribution. In the second, other types of regressions, with potentially contaminated errors, are considered. For both cases the consistency and exact finite-sample distributions of the MLBEs are established. Some results for the corresponding limiting distributions are also provided. In addition, we illustrate how our results can be extended to include certain heteroskedastic and multiple regressions. Finite-sample properties of the MLBEs in comparison to the LSE are investigated in a simulation study.

Place, publisher, year, edition, pages
Taylor & Francis, 2016
Keywords
simple linear regression, robust estimators, measure of location, stable distribution, contaminated error, finite-sample, exact distribution, special functions
National Category
Probability Theory and Statistics Computer Sciences
Identifiers
urn:nbn:se:umu:diva-130037 (URN)10.1080/00949655.2015.1082131 (DOI)000372035600009 ()
Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2018-06-09Bibliographically approved
Meyer, L., Bröms, J., Liu, X., Rottenberg, M. & Sjöstedt, A. (2015). Microinjection of Francisella tularensis and Listeria monocytogenes reveals the importance of bacterial and host factors for successful replication. Infection and Immunity, 83(8), 3233-3242
Open this publication in new window or tab >>Microinjection of Francisella tularensis and Listeria monocytogenes reveals the importance of bacterial and host factors for successful replication
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2015 (English)In: Infection and Immunity, ISSN 0019-9567, E-ISSN 1098-5522, Vol. 83, no 8, p. 3233-3242Article in journal (Other academic) Published
Abstract [en]

Certain intracellular bacteria use the host cell cytosol as the replicative niche. Although it has been hypothesized that the successful exploitation of this compartment requires a unique metabolic adaptation, supportive evidence is lacking. For Francisella tularensis, many genes of the Francisella pathogenicity island (FPI) are essential for intracellular growth, and therefore, FPI mutants are useful tools for understanding the prerequisites of intracytosolic replication. We compared the growth of bacteria taken up by phagocytic or nonphagocytic cells with that of bacteria microinjected directly into the host cytosol, using the live vaccine strain (LVS) of F. tularensis; five selected FPI mutants thereof, i.e., Delta iglA, Delta iglC, Delta iglG, Delta iglI, and Delta pdpE strains; and Listeria monocytogenes. After uptake in bone marrow-derived macrophages (BMDM), ASC(-/-) BMDM, MyD88(-/-) BMDM, J774 cells, or HeLa cells, LVS, Delta pdpE and Delta iglG mutants, and L. monocytogenes replicated efficiently in all five cell types, whereas the Delta iglA and Delta iglC mutants showed no replication. After microinjection, all 7 strains showed effective replication in J774 macrophages, ASC(-/-) BMDM, and HeLa cells. In contrast to the rapid replication in other cell types, L. monocytogenes showed no replication in MyD88(-/-) BMDM and LVS showed no replication in either BMDM or MyD88(-/-) BMDM after microinjection. Our data suggest that the mechanisms of bacterial uptake as well as the permissiveness of the cytosolic compartment per se are important factors for the intracytosolic replication. Notably, none of the investigated FPI proteins was found to be essential for intracytosolic replication after microinjection.

National Category
Microbiology in the medical area
Identifiers
urn:nbn:se:umu:diva-101520 (URN)10.1128/IAI.00416-15 (DOI)000357618300023 ()
Note

Originally included in thesis in manuscript form.

Available from: 2015-04-01 Created: 2015-04-01 Last updated: 2018-06-07Bibliographically approved
Andersson-Evelönn, E., Vidman, L., Källberg, D., Landfors, M., Liu, X., Ljungberg, B., . . . Rydén, P.Combining epigenetic and clinicopathological variables improves prognostic prediction in clear cell Renal Cell Carcinoma.
Open this publication in new window or tab >>Combining epigenetic and clinicopathological variables improves prognostic prediction in clear cell Renal Cell Carcinoma
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(English)Manuscript (preprint) (Other academic)
Keywords
DNA methylation, cancer, cluster analysis, classification, clear cell renal cell carcinoma
National Category
Cancer and Oncology Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-167269 (URN)
Available from: 2020-01-14 Created: 2020-01-14 Last updated: 2020-01-31Bibliographically approved
Bayisa, F. L., Liu, X., Garpebring, A. & Yu, J.Computed Tomography Image Estimation by Statistical Learning Methods.
Open this publication in new window or tab >>Computed Tomography Image Estimation by Statistical Learning Methods
(English)Manuscript (preprint) (Other academic)
Abstract [en]

There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images canbe utilised for attenuation correction, patient positioning, and dose planningin diagnostic and radiotherapy workflows. This study presents a statisticallearning method for CT image estimation. We have used predefined tissuetype information in a Gaussian mixture model to explore the estimation.The performance of our method was evaluated using cross-validation on realdata. In comparison with the existing model-based CT image estimationmethods, the proposed method has improved the estimation, particularly inbone tissues. Evaluation of our method shows that it is a promising methodto generate CT image substitutes for the implementation of fully MR-basedradiotherapy and PET/MRI applications.

Keywords
Computed tomography, magnetic resonance imaging, CT image estimation, pseudo-CT image, supervised learning, Gaussian mixture model
National Category
Probability Theory and Statistics Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-147720 (URN)
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-06-09
wang, j., Garpebring, A., Brynolfsson, P., Liu, X. & Yu, J.Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI.
Open this publication in new window or tab >>Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI
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(English)Manuscript (preprint) (Other academic)
National Category
Natural Sciences
Identifiers
urn:nbn:se:umu:diva-133609 (URN)
Available from: 2017-04-13 Created: 2017-04-13 Last updated: 2018-06-09Bibliographically approved
Furberg, M., Xijia, L., Ahlm, C., Nystedt, A., Stenmark, S., Elisasson, M., . . . Johansson, A. Tularemia in northern Sweden - sero-prevalence and a case-control study of risk factors.
Open this publication in new window or tab >>Tularemia in northern Sweden - sero-prevalence and a case-control study of risk factors
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(English)Article in journal (Refereed) Submitted
National Category
Infectious Medicine Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-126960 (URN)
Available from: 2016-10-24 Created: 2016-10-24 Last updated: 2018-06-09
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