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420 results on '"Yuan, Yinyin"'

1. The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

Author

Pan, Xiaoxi, AbdulJabbar, Khalid, Coelho-Lima, Jose, Grapa, Anca-Ioana, Zhang, Hanyun, Cheung, Alvin Ho Kwan, Baena, Juvenal, Karasaki, Takahiro, Wilson, Claire Rachel, Sereno, Marco, Veeriah, Selvaraju, Aitken, Sarah J., Hackshaw, Allan, Nicholson, Andrew G., Jamal-Hanjani, Mariam, Swanton, Charles, Yuan, Yinyin, Le Quesne, John, and Moore, David A.

Published
2024
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2. Bridging clinic and wildlife care with AI-powered pan-species computational pathology

Author

AbdulJabbar, Khalid, Castillo, Simon P, Hughes, Katherine, Davidson, Hannah, Boddy, Amy M, Abegglen, Lisa M, Minoli, Lucia, Iussich, Selina, Murchison, Elizabeth P, Graham, Trevor A, Spiro, Simon, Maley, Carlo C, Aresu, Luca, Palmieri, Chiara, and Yuan, Yinyin

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Life on Land, Male, Animals, Humans, Dogs, Animals, Wild, Artificial Intelligence, Prostatic Neoplasms, Neural Networks, Computer, Pan troglodytes
Abstract

Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We build a pan-species cancer digital pathology atlas (panspecies.ai) and conduct a pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94; Tasmanian devil facial tumour disease, 0.88). In 18 other vertebrate species (mammalia = 11, reptilia = 4, aves = 2, and amphibia = 1), accuracy (range 0.57-0.94) is influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. Furthermore, a spatial immune score based on artificial intelligence and spatial statistics is associated with prognosis in canine melanoma and prostate tumours. A metric, named morphospace overlap, is developed to guide veterinary pathologists towards rational deployment of this technology on new samples. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on understanding of morphological conservation, which could vastly accelerate developments in veterinary medicine and comparative oncology.

Published
2023

4. Cell abundance aware deep learning for cell detection on highly imbalanced pathological data

Author

Hagos, Yeman Brhane, Lecat, Catherine SY, Patel, Dominic, Lee, Lydia, Tran, Thien-An, Justo, Manuel Rodriguez, Yong, Kwee, and Yuan, Yinyin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated analysis of tissue sections allows a better understanding of disease biology and may reveal biomarkers that could guide prognosis or treatment selection. In digital pathology, less abundant cell types can be of biological significance, but their scarcity can result in biased and sub-optimal cell detection model. To minimize the effect of cell imbalance on cell detection, we proposed a deep learning pipeline that considers the abundance of cell types during model training. Cell weight images were generated, which assign larger weights to less abundant cells and used the weights to regularize Dice overlap loss function. The model was trained and evaluated on myeloma bone marrow trephine samples. Our model obtained a cell detection F1-score of 0.78, a 2% increase compared to baseline models, and it outperformed baseline models at detecting rare cell types. We found that scaling deep learning loss function by the abundance of cells improves cell detection performance. Our results demonstrate the importance of incorporating domain knowledge on deep learning methods for pathological data with class imbalance., Comment: Accepted at The IEEE International Symposium on Biomedical Imaging (ISBI) 2021, 5 pages, 5 figures

Published
2021

5. Glioma Classification Using Multimodal Radiology and Histology Data

Author

Hamidinekoo, Azam, Pieciak, Tomasz, Afzali, Maryam, Akanyeti, Otar, and Yuan, Yinyin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologists diagnose and categorise these tumours based on visual inspection of radiology and histology data. However, this process can be time-consuming and subjective. The computer-assisted methods can help clinicians to make better and faster decisions. In this paper, we propose a pipeline for automatic classification of gliomas into three sub-types: oligodendroglioma, astrocytoma, and glioblastoma, using both radiology and histopathology images. The proposed approach implements distinct classification models for radiographic and histologic modalities and combines them through an ensemble method. The classification algorithm initially carries out tile-level (for histology) and slice-level (for radiology) classification via a deep learning method, then tile/slice-level latent features are combined for a whole-slide and whole-volume sub-type prediction. The classification algorithm was evaluated using the data set provided in the CPM-RadPath 2020 challenge. The proposed pipeline achieved the F1-Score of 0.886, Cohen's Kappa score of 0.811 and Balance accuracy of 0.860. The ability of the proposed model for end-to-end learning of diverse features enables it to give a comparable prediction of glioma tumour sub-types.

Published
2020

7. ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images

Author

Hagos, Yeman Brhane, Narayanan, Priya Lakshmi, Akarca, Ayse U., Marafioti, Teresa, and Yuan, Yinyin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In digital pathology, cell detection and classification are often prerequisites to quantify cell abundance and explore tissue spatial heterogeneity. However, these tasks are particularly challenging for multiplex immunohistochemistry (mIHC) images due to high levels of variability in staining, expression intensity, and inherent noise as a result of preprocessing artefacts. We proposed a deep learning method to detect and classify cells in mIHC whole-tumour slide images of breast cancer. Inspired by inception-v3, we developed Cell COunt RegularizeD Convolutional neural Network (ConCORDe-Net) which integrates conventional dice overlap and a new cell count loss function for optimizing cell detection, followed by a multi-stage convolutional neural network for cell classification. In total, 20447 cells, belonging to five cell classes were annotated by experts from 175 patches extracted from 6 whole-tumour mIHC images. These patches were randomly split into training, validation and testing sets. Using ConCORDe-Net, we obtained a cell detection F1 score of 0.873, which is the best score compared to three state of the art methods. In particular, ConCORDe-Net excels at detecting closely located and weakly stained cells compared to other methods. Incorporating cell count loss in the objective function regularizes the network to learn weak gradient boundaries and separate weakly stained cells from background artefacts. Moreover, cell classification accuracy of 96.5% was achieved. These results support that incorporating problem-specific knowledge such as cell count into deep learning-based cell detection architectures improve the robustness of the algorithm., Comment: MICCAI2019 accepted, 3 figures,8.5 pages

Published
2019

9. DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence

Author

Hagos, Yeman Brhane, Sobhani, Faranak, Castillo, Simon P., Hall, Allison H., AbdulJabbar, Khalid, Salgado, Roberto, Harmon, Bryan, Gallagher, Kristalyn, Kilgore, Mark, King, Lorraine M., Marks, Jeffrey R., Maley, Carlo, Horlings, Hugo M., West, Robert, Hwang, E. Shelley, Yuan, Yinyin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kakileti, Siva Teja, editor, Gabrani, Maria, editor, Manjunath, Geetha, editor, Rosen-Zvi, Michal, editor, Braman, Nathaniel, editor, Schwartz, Robert G., editor, Frangi, Alejandro F., editor, Chung, Pau-Choo, editor, Weight, Christopher, editor, and Jagadish, Vekataraman, editor

Published
2022
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10. Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns

Author

Pan, Xiaoxi, Zhang, Hanyun, Grapa, Anca-Ioana, AbdulJabbar, Khalid, Raza, Shan E Ahmed, Cheung, Ho Kwan Alvin, Karasaki, Takahiro, Quesne, John Le, Moore, David A., Swanton, Charles, Yuan, Yinyin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qin, Wenjian, editor, Zaki, Nazar, editor, Zhang, Fa, editor, Wu, Jia, editor, and Yang, Fan, editor

Published
2022
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11. DeepMIF: Deep Learning Based Cell Profiling for Multispectral Immunofluorescence Images with Graphical User Interface

Author

Hagos, Yeman Brhane, Akarca, Ayse U, Ramsay, Alan, Rossi, Riccardo L, Pomplun, Sabine, Moioli, Alessia, Gianatti, Andrea, Mcnamara, Christopher, Rambaldi, Alessandro, Quezada, Sergio A., Linch, David, Gritti, Giuseppe, Marafioti, Teresa, Yuan, Yinyin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Linwei, editor, Dou, Qi, editor, Fletcher, P. Thomas, editor, Speidel, Stefanie, editor, and Li, Shuo, editor

Published
2022
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12. Self-supervised Antigen Detection Artificial Intelligence (SANDI)

Author

Zhang, Hanyun, AbdulJabbar, Khalid, Grunewald, Tami, Akarca, Ayse, Hagos, Yeman, Lecat, Catherine, Pate, Dominic, Lee, Lydia, Rodriguez-Justo, Manuel, Yong, Kwee, Ledermann, Jonathan, Le Quesne, John, Marafioti, Teresa, Yuan, Yinyin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Xu, Xinxing, editor, Li, Xiaomeng, editor, Mahapatra, Dwarikanath, editor, Cheng, Li, editor, Petitjean, Caroline, editor, and Fu, Huazhu, editor

Published
2022
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13. Capturing global spatial context for accurate cell classification in skin cancer histology

Author

Zormpas-Petridis, Konstantinos, Failmezger, Henrik, Roxanis, Ioannis, Blackledge, Matthew, Jamin, Yann, and Yuan, Yinyin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The spectacular response observed in clinical trials of immunotherapy in patients with previously uncurable Melanoma, a highly aggressive form of skin cancer, calls for a better understanding of the cancer-immune interface. Computational pathology provides a unique opportunity to spatially dissect such interface on digitised pathological slides. Accurate cellular classification is a key to ensure meaningful results, but is often challenging even with state-of-art machine learning and deep learning methods. We propose a hierarchical framework, which mirrors the way pathologists perceive tumour architecture and define tumour heterogeneity to improve cell classification methods that rely solely on cell nuclei morphology. The SLIC superpixel algorithm was used to segment and classify tumour regions in low resolution H&E-stained histological images of melanoma skin cancer to provide a global context. Classification of superpixels into tumour, stroma, epidermis and lumen/white space, yielded a 97.7% training set accuracy and 95.7% testing set accuracy in 58 whole-tumour images of the TCGA melanoma dataset. The superpixel classification was projected down to high resolution images to enhance the performance of a single cell classifier, based on cell nuclear morphological features, and resulted in increasing its accuracy from 86.4% to 91.6%. Furthermore, a voting scheme was proposed to use global context as biological a priori knowledge, pushing the accuracy further to 92.8%. This study demonstrates how using the global spatial context can accurately characterise the tumour microenvironment and allow us to extend significantly beyond single-cell morphological classification., Comment: Accepted by MICCAI COMPAY 2018 workshop

Published
2018

14. DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images

Author

Narayanan, Priya Lakshmi, Raza, Shan E Ahmed, Dodson, Andrew, Gusterson, Barry, Dowsett, Mitchell, and Yuan, Yinyin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Ki67 is an important biomarker for breast cancer. Classification of positive and negative Ki67 cells in histology slides is a common approach to determine cancer proliferation status. However, there is a lack of generalizable and accurate methods to automate Ki67 scoring in large-scale patient cohorts. In this work, we have employed a novel deep learning technique based on hypercolumn descriptors for cell classification in Ki67 images. Specifically, we developed the Simultaneous Detection and Cell Segmentation (DeepSDCS) network to perform cell segmentation and detection. VGG16 network was used for the training and fine tuning to training data. We extracted the hypercolumn descriptors of each cell to form the vector of activation from specific layers to capture features at different granularity. Features from these layers that correspond to the same pixel were propagated using a stochastic gradient descent optimizer to yield the detection of the nuclei and the final cell segmentations. Subsequently, seeds generated from cell segmentation were propagated to a spatially constrained convolutional neural network for the classification of the cells into stromal, lymphocyte, Ki67-positive cancer cell, and Ki67-negative cancer cell. We validated its accuracy in the context of a large-scale clinical trial of oestrogen-receptor-positive breast cancer. We achieved 99.06% and 89.59% accuracy on two separate test sets of Ki67 stained breast cancer dataset comprising biopsy and whole-slide images.

Published
2018

15. Deconvolving convolution neural network for cell detection

Author

Raza, Shan E Ahmed, AbdulJabbar, Khalid, Jamal-Hanjani, Mariam, Veeriah, Selvaraju, Quesne, John Le, Swanton, Charles, and Yuan, Yinyin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic cell detection in histology images is a challenging task due to varying size, shape and features of cells and stain variations across a large cohort. Conventional deep learning methods regress the probability of each pixel belonging to the centre of a cell followed by detection of local maxima. We present deconvolution as an alternate approach to local maxima detection. The ground truth points are convolved with a mapping filter to generate artifical labels. A convolutional neural network (CNN) is modified to convolve it's output with the same mapping filter and is trained for the mapped labels. Output of the trained CNN is then deconvolved to generate points as cell detection. We compare our method with state-of-the-art deep learning approaches where the results show that the proposed approach detects cells with comparatively high precision and F1-score.

Published
2018

17. Glioma Classification Using Multimodal Radiology and Histology Data

Author

Hamidinekoo, Azam, Pieciak, Tomasz, Afzali, Maryam, Akanyeti, Otar, Yuan, Yinyin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
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18. Classifying the evolutionary and ecological features of neoplasms

Author

Maley, Carlo C, Aktipis, Athena, Graham, Trevor A, Sottoriva, Andrea, Boddy, Amy M, Janiszewska, Michalina, Silva, Ariosto S, Gerlinger, Marco, Yuan, Yinyin, Pienta, Kenneth J, Anderson, Karen S, Gatenby, Robert, Swanton, Charles, Posada, David, Wu, Chung-I, Schiffman, Joshua D, Hwang, E Shelley, Polyak, Kornelia, Anderson, Alexander RA, Brown, Joel S, Greaves, Mel, and Shibata, Darryl

Subjects
Biomedical and Clinical Sciences, Health Sciences, Cancer, Antigens, Neoplasm, Biological Evolution, Ecological and Environmental Phenomena, Gene Expression, Genetic Variation, Humans, Microbiota, Neoplasms, Phenotype, Reactive Oxygen Species, Time Factors, Tumor Hypoxia, Tumor Microenvironment, Medical and Health Sciences, Oncology & Carcinogenesis, Biomedical and clinical sciences, Health sciences
Abstract

Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research.

Published
2017

19. DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence

Author

Hagos, Yeman Brhane, primary, Sobhani, Faranak, additional, Castillo, Simon P., additional, Hall, Allison H., additional, AbdulJabbar, Khalid, additional, Salgado, Roberto, additional, Harmon, Bryan, additional, Gallagher, Kristalyn, additional, Kilgore, Mark, additional, King, Lorraine M., additional, Marks, Jeffrey R., additional, Maley, Carlo, additional, Horlings, Hugo M., additional, West, Robert, additional, Hwang, E. Shelley, additional, and Yuan, Yinyin, additional

Published
2022
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21. Self-supervised Antigen Detection Artificial Intelligence (SANDI)

Author

Zhang, Hanyun, primary, AbdulJabbar, Khalid, additional, Grunewald, Tami, additional, Akarca, Ayse, additional, Hagos, Yeman, additional, Lecat, Catherine, additional, Pate, Dominic, additional, Lee, Lydia, additional, Rodriguez-Justo, Manuel, additional, Yong, Kwee, additional, Ledermann, Jonathan, additional, Le Quesne, John, additional, Marafioti, Teresa, additional, and Yuan, Yinyin, additional

Published
2022
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22. Supplementary Table 10 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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23. Table 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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24. Figure 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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25. Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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26. Figure 3 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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27. Figure 5 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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28. Figure 4 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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29. Table 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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30. Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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31. Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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32. Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Author

Hagos, Yeman Brhane, primary, Lecat, Catherine S.Y., primary, Patel, Dominic, primary, Mikolajczak, Anna, primary, Castillo, Simon P., primary, Lyon, Emma J., primary, Foster, Kane, primary, Tran, Thien-An, primary, Lee, Lydia S.H., primary, Rodriguez-Justo, Manuel, primary, Yong, Kwee L., primary, and Yuan, Yinyin, primary

Published
2024
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33. Dysregulated SASS6 expression promotes increased ciliogenesis and cell invasion phenotypes

Author

Hargreaves, Eleanor, primary, Jenks, Andrew D, additional, Staszewski, Adina, additional, Tsalikis, Athanasios, additional, Bodoque, Raquel, additional, Arias-Garcia, Mar, additional, Abdi, Yasmin, additional, Al-Malki, Abdulaziz, additional, Yuan, Yinyin, additional, Natrajan, Rachael, additional, Haider, Syed, additional, Iskratsch, Thomas, additional, Wang, Won-Jing, additional, Godinho, Susana, additional, Palaskas, Nicolaos J, additional, Calvo, Fernando, additional, Zech, Tobias, additional, and Tanos, Barbara, additional

Published
2024
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34. Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer

Author

Jahangir, Chowdhury Arif, primary, Page, David B, additional, Broeckx, Glenn, additional, Gonzalez, Claudia A, additional, Burke, Caoimbhe, additional, Murphy, Clodagh, additional, Reis‐Filho, Jorge S, additional, Ly, Amy, additional, Harms, Paul W, additional, Gupta, Rajarsi R, additional, Vieth, Michael, additional, Hida, Akira I, additional, Kahila, Mohamed, additional, Kos, Zuzana, additional, van Diest, Paul J, additional, Verbandt, Sara, additional, Thagaard, Jeppe, additional, Khiroya, Reena, additional, Abduljabbar, Khalid, additional, Acosta Haab, Gabriela, additional, Acs, Balazs, additional, Adams, Sylvia, additional, Almeida, Jonas S, additional, Alvarado‐Cabrero, Isabel, additional, Azmoudeh‐Ardalan, Farid, additional, Badve, Sunil, additional, Baharun, Nurkhairul Bariyah, additional, Bellolio, Enrique R, additional, Bheemaraju, Vydehi, additional, Blenman, Kim RM, additional, Botinelly Mendonça Fujimoto, Luciana, additional, Burgues, Octavio, additional, Chardas, Alexandros, additional, Cheang, Maggie Chon U, additional, Ciompi, Francesco, additional, Cooper, Lee AD, additional, Coosemans, An, additional, Corredor, Germán, additional, Dantas Portela, Flavio Luis, additional, Deman, Frederik, additional, Demaria, Sandra, additional, Dudgeon, Sarah N, additional, Elghazawy, Mahmoud, additional, Fernandez‐Martín, Claudio, additional, Fineberg, Susan, additional, Fox, Stephen B, additional, Giltnane, Jennifer M, additional, Gnjatic, Sacha, additional, Gonzalez‐Ericsson, Paula I, additional, Grigoriadis, Anita, additional, Halama, Niels, additional, Hanna, Matthew G, additional, Harbhajanka, Aparna, additional, Hart, Steven N, additional, Hartman, Johan, additional, Hewitt, Stephen, additional, Horlings, Hugo M, additional, Husain, Zaheed, additional, Irshad, Sheeba, additional, Janssen, Emiel AM, additional, Kataoka, Tatsuki R, additional, Kawaguchi, Kosuke, additional, Khramtsov, Andrey I, additional, Kiraz, Umay, additional, Kirtani, Pawan, additional, Kodach, Liudmila L, additional, Korski, Konstanty, additional, Akturk, Guray, additional, Scott, Ely, additional, Kovács, Anikó, additional, Lænkholm, Anne‐Vibeke, additional, Lang‐Schwarz, Corinna, additional, Larsimont, Denis, additional, Lennerz, Jochen K, additional, Lerousseau, Marvin, additional, Li, Xiaoxian, additional, Madabhushi, Anant, additional, Maley, Sai K, additional, Manur Narasimhamurthy, Vidya, additional, Marks, Douglas K, additional, McDonald, Elizabeth S, additional, Mehrotra, Ravi, additional, Michiels, Stefan, additional, Kharidehal, Durga, additional, Minhas, Fayyaz ul Amir Afsar, additional, Mittal, Shachi, additional, Moore, David A, additional, Mushtaq, Shamim, additional, Nighat, Hussain, additional, Papathomas, Thomas, additional, Penault‐Llorca, Frederique, additional, Perera, Rashindrie D, additional, Pinard, Christopher J, additional, Pinto‐Cardenas, Juan Carlos, additional, Pruneri, Giancarlo, additional, Pusztai, Lajos, additional, Rajpoot, Nasir Mahmood, additional, Rapoport, Bernardo Leon, additional, Rau, Tilman T, additional, Ribeiro, Joana M, additional, Rimm, David, additional, Vincent‐Salomon, Anne, additional, Saltz, Joel, additional, Sayed, Shahin, additional, Hytopoulos, Evangelos, additional, Mahon, Sarah, additional, Siziopikou, Kalliopi P, additional, Sotiriou, Christos, additional, Stenzinger, Albrecht, additional, Sughayer, Maher A, additional, Sur, Daniel, additional, Symmans, Fraser, additional, Tanaka, Sunao, additional, Taxter, Timothy, additional, Tejpar, Sabine, additional, Teuwen, Jonas, additional, Thompson, E Aubrey, additional, Tramm, Trine, additional, Tran, William T, additional, van der Laak, Jeroen, additional, Verghese, Gregory E, additional, Viale, Giuseppe, additional, Wahab, Noorul, additional, Walter, Thomas, additional, Waumans, Yannick, additional, Wen, Hannah Y, additional, Yang, Wentao, additional, Yuan, Yinyin, additional, Bartlett, John, additional, Loibl, Sibylle, additional, Denkert, Carsten, additional, Savas, Peter, additional, Loi, Sherene, additional, Specht Stovgaard, Elisabeth, additional, Salgado, Roberto, additional, Gallagher, William M, additional, and Rahman, Arman, additional

Published
2024
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35. Deciphering the diversity and sequence of extracellular matrix and cellular spatial patterns in lung adenocarcinoma using topological data analysis

Author

Yoon, Iris H.R., primary, Jenkins, Robert, additional, Colliver, Emma, additional, Zhang, Hanyun, additional, Novo, David, additional, Moore, David, additional, Ramsden, Zoe, additional, Rullan, Antonio, additional, Fu, Xiao, additional, Yuan, Yinyin, additional, Harrington, Heather A., additional, Swanton, Charles, additional, Byrne, Helen M., additional, and Sahai, Erik, additional

Published
2024
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38. Artificial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer

Author

Sobral-Leite, Marcelo, primary, Castillo, Simon, additional, Vonk, Shiva, additional, Melillo, Xenia, additional, Lam, Noomie, additional, de Bruijn, Brandi, additional, Hagos, Yeman, additional, Sanders, Joyce, additional, Almekinders, Mathilde, additional, Visser, Lindy, additional, Groen, Emma, additional, Kristel, Petra, additional, Ercan, Caner, additional, Azarang, Leyla, additional, Yuan, Yinyin, additional, Consortium, Grand Challenge PRECISION, additional, Menezes, Renee, additional, Lips, Esther, additional, and Wesseling, Jelle, additional

Published
2023
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39. Deep learning enables spatial mapping of the mosaic microenvironment of myeloma bone marrow trephine biopsies

Author

Hagos, Yeman B., primary, Lecat, Catherine S.Y., additional, Patel, Dominic, additional, Mikolajczak, Anna, additional, Castillo, Simon P., additional, Lyon, Emma J., additional, Foster, Kane, additional, Tran, Thien-An, additional, Lee, Lydia S.H., additional, Rodriguez-Justo, Manuel, additional, Yong, Kwee L., additional, and Yuan, Yinyin, additional

Published
2023
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42. The T cell differentiation landscape is shaped by tumour mutations in lung cancer

Author

Ghorani, Ehsan, Reading, James L., Henry, Jake Y., Massy, Marc Robert de, Rosenthal, Rachel, Turati, Virginia, Joshi, Kroopa, Furness, Andrew J. S., Ben Aissa, Assma, Saini, Sunil Kumar, Ramskov, Sofie, Georgiou, Andrew, Sunderland, Mariana Werner, Wong, Yien Ning Sophia, Mucha, Maria Vila De, Day, William, Galvez-Cancino, Felipe, Becker, Pablo D., Uddin, Imran, Oakes, Theres, Ismail, Mazlina, Ronel, Tahel, Woolston, Annemarie, Jamal-Hanjani, Mariam, Veeriah, Selvaraju, Birkbak, Nicolai J., Wilson, Gareth A., Litchfield, Kevin, Conde, Lucia, Guerra-Assunção, José Afonso, Blighe, Kevin, Biswas, Dhruva, Salgado, Roberto, Lund, Tom, Bakir, Maise Al, Moore, David A., Hiley, Crispin T., Loi, Sherene, Sun, Yuxin, Yuan, Yinyin, AbdulJabbar, Khalid, Turajilic, Samra, Herrero, Javier, Enver, Tariq, Hadrup, Sine R., Hackshaw, Allan, Peggs, Karl S., McGranahan, Nicholas, Chain, Benny, Swanton, Charles, and Quezada, Sergio A.

Published
2020
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43. A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes

Author

Yuan, Yinyin, Curtis, Christina, Caldas, Carlos, and Markowetz, Florian

Subjects
Quantitative Biology - Molecular Networks, Statistics - Applications
Abstract

The influence of DNA cis-regulatory elements on a gene's expression has been intensively studied. However, little is known about expressions driven by trans-acting DNA hotspots. DNA hotspots harboring copy number aberrations are recognized to be important in cancer as they influence multiple genes on a global scale. The challenge in detecting trans-effects is mainly due to the computational difficulty in detecting weak and sparse trans-acting signals amidst co-occuring passenger events. We propose an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream targets in a breast cancer dataset. Information from this network helps distinguish copy-number driven from copy-number independent expression changes on a global scale. Our result further delineates cis- and trans-effects in a breast cancer dataset, for which important oncogenes such as ESR1 and ERBB2 appear to be highly copy-number dependent. Further, our model is shown to be efficient and in terms of goodness of fit no worse than other state-of the art predictors and network reconstruction models using both simulated and real data., Comment: Accepted at IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2010)

Published
2010
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44. Spatial analyses of immune cell infiltration in cancer: current methods and future directions. A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer

Author

Page, David B, primary, Broeckx, Glenn, additional, Jahangir, Chowdhury Arif, additional, Verbandt, Sara, additional, Gupta, Rajarsi R, additional, Thagaard, Jeppe, additional, Khiroya, Reena, additional, Kos, Zuzana, additional, Abduljabbar, Khalid, additional, Acosta Haab, Gabriela, additional, Acs, Balazs, additional, Almeida, Jonas S, additional, Alvarado‐Cabrero, Isabel, additional, Azmoudeh‐Ardalan, Farid, additional, Badve, Sunil, additional, Baharun, Nurkhairul Bariyah, additional, Bellolio, Enrique R, additional, Bheemaraju, Vydehi, additional, Blenman, Kim RM, additional, Botinelly Mendonça Fujimoto, Luciana, additional, Burgues, Octavio, additional, Cheang, Maggie Chon U, additional, Ciompi, Francesco, additional, Cooper, Lee AD, additional, Coosemans, An, additional, Corredor, Germán, additional, Dantas Portela, Flavio Luis, additional, Deman, Frederik, additional, Demaria, Sandra, additional, Dudgeon, Sarah N, additional, Elghazawy, Mahmoud, additional, Ely, Scott, additional, Fernandez‐Martín, Claudio, additional, Fineberg, Susan, additional, Fox, Stephen B, additional, Gallagher, William M, additional, Giltnane, Jennifer M, additional, Gnjatic, Sacha, additional, Gonzalez‐Ericsson, Paula I, additional, Grigoriadis, Anita, additional, Halama, Niels, additional, Hanna, Matthew G, additional, Harbhajanka, Aparna, additional, Hardas, Alexandros, additional, Hart, Steven N, additional, Hartman, Johan, additional, Hewitt, Stephen, additional, Hida, Akira I, additional, Horlings, Hugo M, additional, Husain, Zaheed, additional, Hytopoulos, Evangelos, additional, Irshad, Sheeba, additional, Janssen, Emiel AM, additional, Kahila, Mohamed, additional, Kataoka, Tatsuki R, additional, Kawaguchi, Kosuke, additional, Kharidehal, Durga, additional, Khramtsov, Andrey I, additional, Kiraz, Umay, additional, Kirtani, Pawan, additional, Kodach, Liudmila L, additional, Korski, Konstanty, additional, Kovács, Anikó, additional, Laenkholm, Anne‐Vibeke, additional, Lang‐Schwarz, Corinna, additional, Larsimont, Denis, additional, Lennerz, Jochen K, additional, Lerousseau, Marvin, additional, Li, Xiaoxian, additional, Ly, Amy, additional, Madabhushi, Anant, additional, Maley, Sai K, additional, Manur Narasimhamurthy, Vidya, additional, Marks, Douglas K, additional, McDonald, Elizabeth S, additional, Mehrotra, Ravi, additional, Michiels, Stefan, additional, Minhas, Fayyaz ul Amir Afsar, additional, Mittal, Shachi, additional, Moore, David A, additional, Mushtaq, Shamim, additional, Nighat, Hussain, additional, Papathomas, Thomas, additional, Penault‐Llorca, Frederique, additional, Perera, Rashindrie D, additional, Pinard, Christopher J, additional, Pinto‐Cardenas, Juan Carlos, additional, Pruneri, Giancarlo, additional, Pusztai, Lajos, additional, Rahman, Arman, additional, Rajpoot, Nasir Mahmood, additional, Rapoport, Bernardo Leon, additional, Rau, Tilman T, additional, Reis‐Filho, Jorge S, additional, Ribeiro, Joana M, additional, Rimm, David, additional, Salomon, Anne‐Vincent, additional, Salto‐Tellez, Manuel, additional, Saltz, Joel, additional, Sayed, Shahin, additional, Siziopikou, Kalliopi P, additional, Sotiriou, Christos, additional, Stenzinger, Albrecht, additional, Sughayer, Maher A, additional, Sur, Daniel, additional, Symmans, Fraser, additional, Tanaka, Sunao, additional, Taxter, Timothy, additional, Tejpar, Sabine, additional, Teuwen, Jonas, additional, Thompson, E Aubrey, additional, Tramm, Trine, additional, Tran, William T, additional, van der Laak, Jeroen, additional, van Diest, Paul J, additional, Verghese, Gregory E, additional, Viale, Giuseppe, additional, Vieth, Michael, additional, Wahab, Noorul, additional, Walter, Thomas, additional, Waumans, Yannick, additional, Wen, Hannah Y, additional, Yang, Wentao, additional, Yuan, Yinyin, additional, Adams, Sylvia, additional, Bartlett, John Mark Seaverns, additional, Loibl, Sibylle, additional, Denkert, Carsten, additional, Savas, Peter, additional, Loi, Sherene, additional, Salgado, Roberto, additional, Specht Stovgaard, Elisabeth, additional, Akturk, Guray, additional, and Bouchmaa, Najat, additional

Published
2023
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45. Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

Author

Thagaard, Jeppe, primary, Broeckx, Glenn, additional, Page, David B, additional, Jahangir, Chowdhury Arif, additional, Verbandt, Sara, additional, Kos, Zuzana, additional, Gupta, Rajarsi, additional, Khiroya, Reena, additional, Abduljabbar, Khalid, additional, Acosta Haab, Gabriela, additional, Acs, Balazs, additional, Akturk, Guray, additional, Almeida, Jonas S, additional, Alvarado‐Cabrero, Isabel, additional, Amgad, Mohamed, additional, Azmoudeh‐Ardalan, Farid, additional, Badve, Sunil, additional, Baharun, Nurkhairul Bariyah, additional, Balslev, Eva, additional, Bellolio, Enrique R, additional, Bheemaraju, Vydehi, additional, Blenman, Kim RM, additional, Botinelly Mendonça Fujimoto, Luciana, additional, Bouchmaa, Najat, additional, Burgues, Octavio, additional, Chardas, Alexandros, additional, Chon U Cheang, Maggie, additional, Ciompi, Francesco, additional, Cooper, Lee AD, additional, Coosemans, An, additional, Corredor, Germán, additional, Dahl, Anders B, additional, Dantas Portela, Flavio Luis, additional, Deman, Frederik, additional, Demaria, Sandra, additional, Doré Hansen, Johan, additional, Dudgeon, Sarah N, additional, Ebstrup, Thomas, additional, Elghazawy, Mahmoud, additional, Fernandez‐Martín, Claudio, additional, Fox, Stephen B, additional, Gallagher, William M, additional, Giltnane, Jennifer M, additional, Gnjatic, Sacha, additional, Gonzalez‐Ericsson, Paula I, additional, Grigoriadis, Anita, additional, Halama, Niels, additional, Hanna, Matthew G, additional, Harbhajanka, Aparna, additional, Hart, Steven N, additional, Hartman, Johan, additional, Hauberg, Søren, additional, Hewitt, Stephen, additional, Hida, Akira I, additional, Horlings, Hugo M, additional, Husain, Zaheed, additional, Hytopoulos, Evangelos, additional, Irshad, Sheeba, additional, Janssen, Emiel AM, additional, Kahila, Mohamed, additional, Kataoka, Tatsuki R, additional, Kawaguchi, Kosuke, additional, Kharidehal, Durga, additional, Khramtsov, Andrey I, additional, Kiraz, Umay, additional, Kirtani, Pawan, additional, Kodach, Liudmila L, additional, Korski, Konstanty, additional, Kovács, Anikó, additional, Laenkholm, Anne‐Vibeke, additional, Lang‐Schwarz, Corinna, additional, Larsimont, Denis, additional, Lennerz, Jochen K, additional, Lerousseau, Marvin, additional, Li, Xiaoxian, additional, Ly, Amy, additional, Madabhushi, Anant, additional, Maley, Sai K, additional, Manur Narasimhamurthy, Vidya, additional, Marks, Douglas K, additional, McDonald, Elizabeth S, additional, Mehrotra, Ravi, additional, Michiels, Stefan, additional, Minhas, Fayyaz ul Amir Afsar, additional, Mittal, Shachi, additional, Moore, David A, additional, Mushtaq, Shamim, additional, Nighat, Hussain, additional, Papathomas, Thomas, additional, Penault‐Llorca, Frederique, additional, Perera, Rashindrie D, additional, Pinard, Christopher J, additional, Pinto‐Cardenas, Juan Carlos, additional, Pruneri, Giancarlo, additional, Pusztai, Lajos, additional, Rahman, Arman, additional, Rajpoot, Nasir Mahmood, additional, Rapoport, Bernardo Leon, additional, Rau, Tilman T, additional, Reis‐Filho, Jorge S, additional, Ribeiro, Joana M, additional, Rimm, David, additional, Roslind, Anne, additional, Vincent‐Salomon, Anne, additional, Salto‐Tellez, Manuel, additional, Saltz, Joel, additional, Sayed, Shahin, additional, Scott, Ely, additional, Siziopikou, Kalliopi P, additional, Sotiriou, Christos, additional, Stenzinger, Albrecht, additional, Sughayer, Maher A, additional, Sur, Daniel, additional, Fineberg, Susan, additional, Symmans, Fraser, additional, Tanaka, Sunao, additional, Taxter, Timothy, additional, Tejpar, Sabine, additional, Teuwen, Jonas, additional, Thompson, E Aubrey, additional, Tramm, Trine, additional, Tran, William T, additional, van der Laak, Jeroen, additional, van Diest, Paul J, additional, Verghese, Gregory E, additional, Viale, Giuseppe, additional, Vieth, Michael, additional, Wahab, Noorul, additional, Walter, Thomas, additional, Waumans, Yannick, additional, Wen, Hannah Y, additional, Yang, Wentao, additional, Yuan, Yinyin, additional, Zin, Reena Md, additional, Adams, Sylvia, additional, Bartlett, John, additional, Loibl, Sibylle, additional, Denkert, Carsten, additional, Savas, Peter, additional, Loi, Sherene, additional, Salgado, Roberto, additional, and Specht Stovgaard, Elisabeth, additional

Published
2023
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47. Statistical inference from large-scale genomic data

Author

Yuan, Yinyin

Subjects
576.5, QA76 Electronic computers. Computer science. Computer software
Abstract

This thesis explores the potential of statistical inference methodologies in their applications in functional genomics. In essence, it summarises algorithmic findings in this field, providing step-by-step analytical methodologies for deciphering biological knowledge from large-scale genomic data, mainly microarray gene expression time series. This thesis covers a range of topics in the investigation of complex multivariate genomic data. One focus involves using clustering as a method of inference and another is cluster validation to extract meaningful biological information from the data. Information gained from the application of these various techniques can then be used conjointly in the elucidation of gene regulatory networks, the ultimate goal of this type of analysis. First, a new tight clustering method for gene expression data is proposed to obtain tighter and potentially more informative gene clusters. Next, to fully utilise biological knowledge in clustering validation, a validity index is defined based on one of the most important ontologies within the Bioinformatics community, Gene Ontology. The method bridges a gap in current literature, in the sense that it takes into account not only the variations of Gene Ontology categories in biological specificities and their significance to the gene clusters, but also the complex structure of the Gene Ontology. Finally, Bayesian probability is applied to making inference from heterogeneous genomic data, integrated with previous efforts in this thesis, for the aim of large-scale gene network inference. The proposed system comes with a stochastic process to achieve robustness to noise, yet remains efficient enough for large-scale analysis. Ultimately, the solutions presented in this thesis serve as building blocks of an intelligent system for interpreting large-scale genomic data and understanding the functional organisation of the genome.

Published
2009

48. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Author

Amgad, Mohamed, Stovgaard, Elisabeth Specht, Balslev, Eva, Thagaard, Jeppe, Chen, Weijie, Dudgeon, Sarah, Sharma, Ashish, Kerner, Jennifer K., Denkert, Carsten, Yuan, Yinyin, AbdulJabbar, Khalid, Wienert, Stephan, Savas, Peter, Voorwerk, Leonie, Beck, Andrew H., Madabhushi, Anant, Hartman, Johan, Sebastian, Manu M., Horlings, Hugo M., Hudeček, Jan, Ciompi, Francesco, Moore, David A., Singh, Rajendra, Roblin, Elvire, Balancin, Marcelo Luiz, Mathieu, Marie-Christine, Lennerz, Jochen K., Kirtani, Pawan, Chen, I-Chun, Braybrooke, Jeremy P., Pruneri, Giancarlo, Demaria, Sandra, Adams, Sylvia, Schnitt, Stuart J., Lakhani, Sunil R., Rojo, Federico, Comerma, Laura, Badve, Sunil S., Khojasteh, Mehrnoush, Symmans, W. Fraser, Sotiriou, Christos, Gonzalez-Ericsson, Paula, Pogue-Geile, Katherine L., Kim, Rim S., Rimm, David L., Viale, Giuseppe, Hewitt, Stephen M., Bartlett, John M. S., Penault-Llorca, Frédérique, Goel, Shom, Lien, Huang-Chun, Loibl, Sibylle, Kos, Zuzana, Loi, Sherene, Hanna, Matthew G., Michiels, Stefan, Kok, Marleen, Nielsen, Torsten O., Lazar, Alexander J., Bago-Horvath, Zsuzsanna, Kooreman, Loes F. S., van der Laak, Jeroen A. W. M., Saltz, Joel, Gallas, Brandon D., Kurkure, Uday, Barnes, Michael, Salgado, Roberto, and Cooper, Lee A. D.

Published
2020
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49. Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Author

Kos, Zuzana, Roblin, Elvire, Kim, Rim S., Michiels, Stefan, Gallas, Brandon D., Chen, Weijie, van de Vijver, Koen K., Goel, Shom, Adams, Sylvia, Demaria, Sandra, Viale, Giuseppe, Nielsen, Torsten O., Badve, Sunil S., Symmans, W. Fraser, Sotiriou, Christos, Rimm, David L., Hewitt, Stephen, Denkert, Carsten, Loibl, Sibylle, Luen, Stephen J., Bartlett, John M. S., Savas, Peter, Pruneri, Giancarlo, Dillon, Deborah A., Cheang, Maggie Chon U., Tutt, Andrew, Hall, Jacqueline A., Kok, Marleen, Horlings, Hugo M., Madabhushi, Anant, van der Laak, Jeroen, Ciompi, Francesco, Laenkholm, Anne-Vibeke, Bellolio, Enrique, Gruosso, Tina, Fox, Stephen B., Araya, Juan Carlos, Floris, Giuseppe, Hudeček, Jan, Voorwerk, Leonie, Beck, Andrew H., Kerner, Jen, Larsimont, Denis, Declercq, Sabine, Van den Eynden, Gert, Pusztai, Lajos, Ehinger, Anna, Yang, Wentao, AbdulJabbar, Khalid, Yuan, Yinyin, Singh, Rajendra, Hiley, Crispin, Bakir, Maise al, Lazar, Alexander J., Naber, Stephen, Wienert, Stephan, Castillo, Miluska, Curigliano, Giuseppe, Dieci, Maria-Vittoria, André, Fabrice, Swanton, Charles, Reis-Filho, Jorge, Sparano, Joseph, Balslev, Eva, Chen, I-Chun, Stovgaard, Elisabeth Ida Specht, Pogue-Geile, Katherine, Blenman, Kim R. M., Penault-Llorca, Frédérique, Schnitt, Stuart, Lakhani, Sunil R., Vincent-Salomon, Anne, Rojo, Federico, Braybrooke, Jeremy P., Hanna, Matthew G., Soler-Monsó, M. Teresa, Bethmann, Daniel, Castaneda, Carlos A., Willard-Gallo, Karen, Sharma, Ashish, Lien, Huang-Chun, Fineberg, Susan, Thagaard, Jeppe, Comerma, Laura, Gonzalez-Ericsson, Paula, Brogi, Edi, Loi, Sherene, Saltz, Joel, Klaushen, Frederick, Cooper, Lee, Amgad, Mohamed, Moore, David A., and Salgado, Roberto

Published
2020
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50. Figure 5 from Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma

Author

Zhang, Hanyun, primary, AbdulJabbar, Khalid, primary, Moore, David A., primary, Akarca, Ayse, primary, Enfield, Katey S.S., primary, Jamal-Hanjani, Mariam, primary, Raza, Shan E. Ahmed, primary, Veeriah, Selvaraju, primary, Salgado, Roberto, primary, McGranahan, Nicholas, primary, Le Quesne, John, primary, Swanton, Charles, primary, Marafioti, Teresa, primary, and Yuan, Yinyin, primary

Published
2023
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