| 1. |
- 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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| 2. |
- 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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| 3. |
- Campbell, Heather, et al.
(författare)
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COSMOLOGY WITH PHOTOMETRICALLY CLASSIFIED TYPE Ia SUPERNOVAE FROM THE SDSS-II SUPERNOVA SURVEY
- 2013
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Ingår i: Astrophysical Journal. - 0004-637X .- 1538-4357. ; 763:2, s. 88-
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Tidskriftsartikel (refereegranskat)abstract
- We present the cosmological analysis of 752 photometrically classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the SDSS-III Baryon Oscillation Spectroscopic Survey. Our photometric-classification method is based on the SN classification technique of Sako et al., aided by host-galaxy redshifts (0.05 < z < 0.55). SuperNova ANAlysis simulations of our methodology estimate that we have an SN Ia classification efficiency of 70.8%, with only 3.9% contamination from core-collapse (non-Ia) SNe. We demonstrate that this level of contamination has no effect on our cosmological constraints. We quantify and correct for our selection effects (e. g., Malmquist bias) using simulations. When fitting to a flat.CDM cosmological model, we find that our photometric sample alone gives Omega(m) = 0.24(-0.05)(+0.07) (statistical errors only). If we relax the constraint on flatness, then our sample provides competitive joint statistical constraints on Omega(m) and Omega(Lambda), comparable to those derived from the spectroscopically confirmed Three-year Supernova Legacy Survey (SNLS3). Using only our data, the statistics-only result favors an accelerating universe at 99.96% confidence. Assuming a constant wCDM cosmological model, and combining with H-0, cosmic microwave background, and luminous red galaxy data, we obtain w = -0.96(-0.10)(+0.10), Omega(m) = 0.29(-0.02)(+0.02), and Omega(k) = 0.00(-0.02)(+0.03)(statistical errors only), which is competitive with similar spectroscopically confirmed SNe Ia analyses. Overall this comparison is reassuring, considering the lower redshift leverage of the SDSS-II SN sample (z < 0.55) and the lack of spectroscopic confirmation used herein. These results demonstrate the potential of photometrically classified SN Ia samples in improving cosmological constraints.
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| 4. |
- Nguyen, Huu-Giao, et al.
(författare)
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Image-based assessment of extracellular mucin-to-tumor area predicts consensus molecular subtypes (CMS) in colorectal cancer
- 2022
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Ingår i: Modern Pathology. - : Elsevier BV. - 0893-3952 .- 1530-0285. ; 35, s. 240-248
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Tidskriftsartikel (refereegranskat)abstract
- The backbone of all colorectal cancer classifications including the consensus molecular subtypes (CMS) highlights microsatellite instability (MSI) as a key molecular pathway. Although mucinous histology (generally defined as >50% extracellular mucin-to-tumor area) is a “typical” feature of MSI, it is not limited to this subgroup. Here, we investigate the association of CMS classification and mucin-to-tumor area quantified using a deep learning algorithm, and the expression of specific mucins in predicting CMS groups and clinical outcome. A weakly supervised segmentation method was developed to quantify extracellular mucin-to-tumor area in H&E images. Performance was compared to two pathologists’ scores, then applied to two cohorts: (1) TCGA (n = 871 slides/412 patients) used for mucin-CMS group correlation and (2) Bern (n = 775 slides/517 patients) for histopathological correlations and next-generation Tissue Microarray construction. TCGA and CPTAC (n = 85 patients) were used to further validate mucin detection and CMS classification by gene and protein expression analysis for MUC2, MUC4, MUC5AC and MUC5B. An excellent inter-observer agreement between pathologists’ scores and the algorithm was obtained (ICC = 0.92). In TCGA, mucinous tumors were predominantly CMS1 (25.7%), CMS3 (24.6%) and CMS4 (16.2%). Average mucin in CMS2 was 1.8%, indicating negligible amounts. RNA and protein expression of MUC2, MUC4, MUC5AC and MUC5B were low-to-absent in CMS2. MUC5AC protein expression correlated with aggressive tumor features (e.g., distant metastases (p = 0.0334), BRAF mutation (p < 0.0001), mismatch repair-deficiency (p < 0.0001), and unfavorable 5-year overall survival (44% versus 65% for positive/negative staining). MUC2 expression showed the opposite trend, correlating with less lymphatic (p = 0.0096) and venous vessel invasion (p = 0.0023), no impact on survival.The absence of mucin-expressing tumors in CMS2 provides an important phenotype-genotype correlation. Together with MSI, mucinous histology may help predict CMS classification using only histopathology and should be considered in future image classifiers of molecular subtypes.
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| 5. |
- Sako, Masao, et al.
(författare)
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The Data Release of the Sloan Digital Sky Survey-II Supernova Survey
- 2018
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Ingår i: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 130:988
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg(2) area along the celestial equator. This data release is comprised of all transient sources brighter than r similar or equal to 22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically, using host galaxy redshift information when available. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1364 SN. Ia with spectroscopic redshifts as well as photometric redshifts for a further 624 purely photometric SN. Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN. Ia sample and assuming a flat.CDM cosmology, we determine Omega(M) = 0.315 +/- 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 sigma.
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