| 11. |
- Bokhorst, John-Melle, et al.
(författare)
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Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer
- 2023
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Ingår i: Modern Pathology. - : ELSEVIER SCIENCE INC. - 0893-3952 .- 1530-0285. ; 36:9
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H & E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H & E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n 1/4 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H & E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. & COPY; 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
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| 12. |
- Bokhorst, John-Melle, et al.
(författare)
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Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer
- 2023
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Ingår i: Cancers. - : MDPI. - 2072-6694. ; 15:7
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.
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| 13. |
- Gremel, G., et al.
(författare)
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Functional and prognostic relevance of the homeobox protein MSX2 in malignant melanoma
- 2011
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Ingår i: British Journal of Cancer. - : Springer Science and Business Media LLC. - 0007-0920 .- 1532-1827. ; 105:4, s. 565-574
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The homeobox containing transcription factor MSX2 is a key regulator of embryonic development and has been implicated to have a role in breast and pancreatic cancer. METHODS: Using a selection of two-and three-dimensional in vitro assays and tissue microarrays (TMAs), the clinical and functional relevance of MSX2 in malignant melanoma was explored. A doxycyline-inducible over-expression system was applied to study the relevance of MSX2 in vitro. For TMA construction, tumour material from 218 melanoma patients was used. RESULTS: Ectopic expression of MSX2 resulted in the induction of apoptosis and reduced the invasive capacity of melanoma cells in three-dimensional culture. MSX2 over-expression was shown to affect several signalling pathways associated with cell invasion and survival. Downregulation of N-Cadherin, induction of p21 and inhibition of both BCL2 and Survivin were observed. Cytoplasmic MSX2 expression was found to correlate significantly with increased recurrence-free survival (P = 0.008). Nuclear expression of MSX2 did not result in significant survival correlations, suggesting that the beneficial effect of MSX2 may be independent of its DNA binding activity. CONCLUSIONS: MSX2 may be an important regulator of melanoma cell invasion and survival. Cytoplasmic expression of the protein was identified as biomarker for good prognosis in malignant melanoma patients.
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| 14. |
- Haddad, Tariq Sami, et al.
(författare)
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Improving tumor budding reporting in colorectal cancer: a Delphi consensus study
- 2021
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Ingår i: Virchows Archiv. - : SPRINGER. - 0945-6317 .- 1432-2307. ; 479:3, s. 459-469
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice.
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| 15. |
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| 17. |
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| 18. |
- Savolainen, Vincent, et al.
(författare)
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Phylogeny of the eudicots : a nearly complete familial analysis based on rbcL gene sequences
- 2000
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Ingår i: Kew bulletin. - 0075-5974 .- 1874-933X. ; 55:2, s. 257-309
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Tidskriftsartikel (refereegranskat)abstract
- A phylogenetic analysis of 589 plastid rbcL gene sequences representing nearly all eudicot families (a total of 308 families; seven photosynthetic and four parasitic families are missing) was performed, and bootstrap re-sampling was used to assess support for clades. Based on these data, the ordinal classification of eudicots is revised following the previous classification of angiosperms by the Angiosperm Phylogeny Group (APG). Putative additional orders are discussed (e.g. Dilleniales, Escalloniales, Vitales), and several additional families are assigned to orders for future updates of the APG classification. The use of rbcL alone in such a large matrix was found to be practical in discovering and providing bootstrap support for most orders. Combination of these data with other matrices for the rest of the angiosperms should provide the framework for a complete phylogeny to be used in macro-evolutionary studies.
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