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- Berg, Aase, et al.
(författare)
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Complement Activation Correlates With Disease Severity and Contributes to Cytokine Responses in Plasmodium falciparum Malaria
- 2015
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Ingår i: The Internet Journal of Infectious Diseases. - : Oxford University Press (OUP). - 1528-8366 .- 0022-1899 .- 1537-6613. ; 212:11, s. 1835-1840
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Tidskriftsartikel (refereegranskat)abstract
- The impact of complement activation and its possible relation to cytokine responses during malaria pathology was investigated in plasma samples from patients with confirmed Plasmodium falciparum malaria and in human whole-blood specimens stimulated with malaria-relevant agents ex vivo. Complement was significantly activated in the malaria cohort, compared with healthy controls, and was positively correlated with disease severity and with certain cytokines, in particular interleukin 8 (IL-8)/CXCL8. This was confirmed in ex vivo-stimulated blood specimens, in which complement inhibition significantly reduced IL-8/CXCL8 release. P. falciparum malaria is associated with systemic complement activation and complement-dependent release of inflammatory cytokines, of which IL-8/CXCL8 is particularly prominent.
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| 2. |
- Holmstrom, Oscar, et al.
(författare)
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Detection of breast cancer lymph node metastases in frozen sections with a point-of care low-cost microscope scanner
- 2019
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- Background Detection of lymph node metastases is essential in breast cancer diagnostics and staging, affecting treatment and prognosis. lntraoperative microscopy analysis of sentinel lymph node frozen sections is standard for detection of axillary metastases but requires access to a pathologist for sample analysis. Remote analysis of digitized samples is an alternative solution but is limited by the requirement for high-end slide scanning equipment.Objective To determine whether the image quality achievable with a low-cost, miniature digital microscope scanner is sufficient for detection of metastases in breast cancer lymph node frozen sections.Methods Lymph node frozen sections from 79 breast cancer patients were digitized using a prototype miniature microscope scanner and a high-end slide scanner. Images were independently reviewed by two pathologists and results compared between devices with conventional light microscopy analysis as ground truth.Results Detection of metastases in the images acquired with the miniature scanner yielded an overall sensitivity of 91% and specificity of 99% and showed strong agreement when compared to light microscopy (k = 0.91). Strong agreement was also observed when results were compared to results from the high-end slide scanner (k = 0.94). A majority of discrepant cases were micrometastases and sections of which no anticytokeratin staining was available.ConclusionAccuracy of detection of metastatic cells in breast cancer sentinel lymph node frozen sections by visual analysis of samples digitized using low-cost, point-of-care microscopy is comparable to analysis of digital samples scanned using a high-end, whole slide scanner. This technique could potentially provide a workflow for digital diagnostics in resource-limited settings, facilitate sample analysis at the point-of-care and reduce the need for trained experts on-site during surgical procedures.
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| 3. |
- Turkki, Riku, et al.
(författare)
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Breast cancer outcome prediction with tumour tissue images and machine learning
- 2019
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Ingår i: Breast Cancer Research and Treatment. - : SPRINGER. - 0167-6806 .- 1573-7217. ; 177:1, s. 41-52
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.Methods: Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N=1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients.Results: In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p=0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p=0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples.Conclusions: Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.
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