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- Bychkov, D, et al.
(författare)
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Deep learning based tissue analysis predicts outcome in colorectal cancer
- 2018
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8:1, s. 3395-
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Tidskriftsartikel (refereegranskat)abstract
- Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79–3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28–2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30–2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.
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| 6. |
- Vakkilainen, S, et al.
(författare)
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The human long non-coding RNA gene RMRP has pleiotropic effects and regulates cell-cycle progression at G2
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 13758-
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Tidskriftsartikel (refereegranskat)abstract
- RMRP was the first non-coding nuclear RNA gene implicated in a disease. Its mutations cause cartilage-hair hypoplasia (CHH), an autosomal recessive skeletal dysplasia with growth failure, immunodeficiency, and a high risk for malignancies. This study aimed to gain further insight into the role of RNA Component of Mitochondrial RNA Processing Endoribonuclease (RMRP) in cellular physiology and disease pathogenesis. We combined transcriptome analysis with single-cell analysis using fibroblasts from CHH patients and healthy controls. To directly assess cell cycle progression, we followed CHH fibroblasts by pulse-labeling and time-lapse microscopy. Transcriptome analysis identified 35 significantly upregulated and 130 downregulated genes in CHH fibroblasts. The downregulated genes were significantly connected to the cell cycle. Multiple other pathways, involving regulation of apoptosis, bone and cartilage formation, and lymphocyte function, were also affected, as well as PI3K-Akt signaling. Cell-cycle studies indicated that the CHH cells were delayed specifically in the passage from G2 phase to mitosis. Our findings expand the mechanistic understanding of CHH, indicate possible pathways for therapeutic intervention and add to the limited understanding of the functions of RMRP.
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