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209 results on '"Sirinukunwattana, Korsuk"'

1. Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy

Author

Lafarge, Maxime W., Domingo, Enric, Sirinukunwattana, Korsuk, Wood, Ruby, Samuel, Leslie, Murray, Graeme, Richman, Susan D., Blake, Andrew, Sebag-Montefiore, David, Gollins, Simon, Klieser, Eckhard, Neureiter, Daniel, Huemer, Florian, Greil, Richard, Dunne, Philip, Quirke, Philip, Weiss, Lukas, Rittscher, Jens, Maughan, Tim, and Koelzer, Viktor H.

Published
2024
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2. A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples

Author

Martin, Natalia Garcia, Malacrino, Stefano, Wojciechowska, Marta, Campo, Leticia, Jones, Helen, Wedge, David C., Holmes, Chris, Sirinukunwattana, Korsuk, Sailem, Heba, Verrill, Clare, and Rittscher, Jens

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

Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.

Published
2022
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3. Joint Prediction of Response to Therapy, Molecular Traits, and Spatial Organisation in Colorectal Cancer Biopsies

Author

Wood, Ruby, Domingo, Enric, Sirinukunwattana, Korsuk, Lafarge, Maxime W., Koelzer, Viktor H., Maughan, Timothy S., Rittscher, Jens, 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, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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5. Enhancing Local Context of Histology Features in Vision Transformers

Author

Wood, Ruby, Sirinukunwattana, Korsuk, Domingo, Enric, Sauer, Alexander, Lafarge, Maxime W., Koelzer, Viktor H., Maughan, Timothy S., Rittscher, Jens, 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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6. Predicting Molecular Traits from Tissue Morphology Through Self-interactive Multi-instance Learning

Author

Hu, Yang, Sirinukunwattana, Korsuk, Gaitskell, Kezia, Wood, Ruby, Verrill, Clare, Rittscher, Jens, 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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7. Improving Whole Slide Segmentation Through Visual Context - A Systematic Study

Author

Sirinukunwattana, Korsuk, Alham, Nasullah Khalid, Verrill, Clare, and Rittscher, Jens

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems.

Published
2018

8. Why rankings of biomedical image analysis competitions should be interpreted with care

Author

Maier-Hein, Lena, Eisenmann, Matthias, Reinke, Annika, Onogur, Sinan, Stankovic, Marko, Scholz, Patrick, Arbel, Tal, Bogunovic, Hrvoje, Bradley, Andrew P., Carass, Aaron, Feldmann, Carolin, Frangi, Alejandro F., Full, Peter M., van Ginneken, Bram, Hanbury, Allan, Honauer, Katrin, Kozubek, Michal, Landman, Bennett A., März, Keno, Maier, Oskar, Maier-Hein, Klaus, Menze, Bjoern H., Müller, Henning, Neher, Peter F., Niessen, Wiro, Rajpoot, Nasir, Sharp, Gregory C., Sirinukunwattana, Korsuk, Speidel, Stefanie, Stock, Christian, Stoyanov, Danail, Taha, Abdel Aziz, van der Sommen, Fons, Wang, Ching-Wei, Weber, Marc-André, Zheng, Guoyan, Jannin, Pierre, and Kopp-Schneider, Annette

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future., Comment: Article published in Nature Communications: https://rdcu.be/bRmNr

Published
2018
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9. Novel digital tissue phenotypic signatures of distant metastasis in colorectal cancer

Author

Sirinukunwattana, Korsuk, Snead, David, Epstein, David, Aftab, Zia, Mujeeb, Imaad, Tsang, Yee Wah, Cree, Ian, and Rajpoot, Nasir

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

Distant metastasis is the major cause of death in colorectal cancer (CRC). Patients at high risk of developing distant metastasis could benefit from appropriate adjuvant and follow-up treatments if stratified accurately at an early stage of the disease. Studies have increasingly recognized the role of diverse cellular components within the tumor microenvironment in the development and progression of CRC tumors. In this paper, we show that a new method of automated analysis of digitized images from colorectal cancer tissue slides can provide important estimates of distant metastasis-free survival (DMFS, the time before metastasis is first observed) on the basis of details of the microenvironment. Specifically, we determine what cell types are found in the vicinity of other cell types, and in what numbers, rather than concentrating exclusively on the cancerous cells. We then extract novel tissue phenotypic signatures using statistical measurements about tissue composition. Such signatures can underpin clinical decisions about the advisability of various types of adjuvant therapy.

Published
2018

12. Improving Pathological Distribution Measurements with Bayesian Uncertainty

Author

Tam, Ka Ho, Sirinukunwattana, Korsuk, Soares, Maria F., Kaisar, Maria, Ploeg, Rutger, Rittscher, Jens, 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, Sudre, Carole H., editor, Fehri, Hamid, editor, Arbel, Tal, editor, Baumgartner, Christian F., editor, Dalca, Adrian, editor, Tanno, Ryutaro, editor, Van Leemput, Koen, editor, Wells, William M., editor, Sotiras, Aristeidis, editor, Papiez, Bartlomiej, editor, Ferrante, Enzo, editor, and Parisot, Sarah, editor

Published
2020
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14. Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Author

Sirinukunwattana, Korsuk, Pluim, Josien P. W., Chen, Hao, Qi, Xiaojuan, Heng, Pheng-Ann, Guo, Yun Bo, Wang, Li Yang, Matuszewski, Bogdan J., Bruni, Elia, Sanchez, Urko, Böhm, Anton, Ronneberger, Olaf, Cheikh, Bassem Ben, Racoceanu, Daniel, Kainz, Philipp, Pfeiffer, Michael, Urschler, Martin, Snead, David R. J., and Rajpoot, Nasir M.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

Published
2016

16. Towards a quantitative analysis of cancer tissue morphology

Author

Sirinukunwattana, Korsuk

Subjects
616.99, RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Abstract

Morphological analysis of the appearance and quantity of cells and tissue architecture has been routinely used by pathologists to determine cancer’s histologic grade. In standard practice, however, the analysis is based on only a few morphological features, and is mainly done in a qualitative or semi-quantitative fashion. This makes the analysis subjective and less reproducible. In fact, a single piece of cancer tissue can contain a high level of heterogeneity – two cells of the same type can behave differently depending on their genetic makeup and their microenvironment. This simply means that there is still a vast amount of information hidden in cancer tissues, whose clinical value is not fully realised. With digital slide scanning technology becoming more available, it is now possible to obtain a whole-slide image of a histological section of high-quality and with high-resolution (multi-gigapixel images) in a digital format. Moreover, the volume of data is rapidly growing daily as slides are routinely scanned. This presents an opportunity to advance image analytical techniques and computational algorithms for quantitative analysis of tissue morphology (morphometrics). Morphometrics provides an accurate and reproducible means for the diagnosis and prognostication of cancers. This is the very first step towards effective treatment decision making and personalised medication. In the first part of this thesis, we focus on the development of automated algorithms for quantifying morphological features from histological sections. All the algorithms are specifically designed for histological sections stained by a standard haematoxylin and eosin (H&E) stain. Firstly, we proposed a discriminative dictionary learning based algorithm for learning the appearance of cells. The approach is applied to distinguish mitotic cells from non-mitotic cells in breast cancer histological images. Secondly, we develop a deep learning based framework that utilises a local context information for nuclear detection and classification. We demonstrate the effectiveness of this framework for detecting and classifying various types of cells in colorectal cancer. Lastly, we consider the intestinal gland segmentation problem in histological images of colorectal cancer. We formulate a segmentation algorithm that finds the best polygon matching the shape of each gland in a Bayesian framework. The method is capable of segmenting glands in both benign and malignant cases. In the second part of this thesis, we turn toward a quantitive analysis of tissue morphology of colorectal adenocarcinomas. This is to find potential morphological features that are clinically relevant. We extract morphological features, using the algorithms developed in the first part of this thesis. The results from statistical analyses show that a quantitative measure for aberrance of glandular shape is strongly associated with the histologic grade, and a quantitative measure related to the desmoplasia is associated with the tendency to develop metastasis.

Published
2016

19. Image-Based Consensus Molecular Subtyping in Rectal Cancer Biopsies and Response to Neoadjuvant Chemoradiotherapy

Author

Lafarge, Maxime W, primary, Domingo, Enric, additional, Sirinukunwattana, Korsuk, additional, Wood, Ruby, additional, Samuel, Leslie, additional, Murray, Graeme, additional, Richman, Susan D, additional, Blake, Andrew, additional, Sebag-Montefiore, David, additional, Gollins, Simon, additional, Klieser, Eckhard, additional, Neureiter, Daniel, additional, Huemer, Florian, additional, Greil, Richard, additional, Dunne, Philip, additional, Quirke, Philip, additional, Weiss, Lukas, additional, Rittscher, Jens, additional, Maughan, Tim, additional, and Koelzer, Viktor H, additional

Published
2023
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22. How to Exploit Weaknesses in Biomedical Challenge Design and Organization

Author

Reinke, Annika, Eisenmann, Matthias, Onogur, Sinan, Stankovic, Marko, Scholz, Patrick, Full, Peter M., Bogunovic, Hrvoje, Landman, Bennett A., Maier, Oskar, Menze, Bjoern, Sharp, Gregory C., Sirinukunwattana, Korsuk, Speidel, Stefanie, van der Sommen, Fons, Zheng, Guoyan, Müller, Henning, Kozubek, Michal, Arbel, Tal, Bradley, Andrew P., Jannin, Pierre, Kopp-Schneider, Annette, Maier-Hein, Lena, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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23. A pro-inflammatory stem cell niche drives myelofibrosis through a targetable galectin 1 axis

Author

Li, Rong, primary, Colombo, Michela, additional, Wang, Guanlin, additional, Rodriguez-Romera, Antonio, additional, O'Sullivan, Jennifer, additional, Clark, Sally-Ann, additional, Pérez Sáez, Juan, additional, Meng, Yiran, additional, Khan, Abdullah, additional, Wen, Sean, additional, Dong, Pengwei, additional, Zhou, Wenjiang, additional, Sousos, Nikolaos, additional, Murphy, Lauren, additional, Clarke, Matthew, additional, Jooss, Natalie, additional, Olijnik, Aude-Anais, additional, Wong, Zoe, additional, Sirinukunwattana, Korsuk, additional, Ryou, Hosuk, additional, Norfo, Ruggiero, additional, Cheng, Qian, additional, Brierley, Charlotte, additional, Carrelha, Joana, additional, Ren, Zemin, additional, Thongjuea, Supat, additional, Rathinam, Vijay, additional, Krishnan, Anandi Krishnan, additional, Royston, Daniel, additional, Rabinovich, Gabriel, additional, Mead, Adam, additional, and Psaila, Bethan, additional

Published
2023
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24. Flexible and Highly-Efficient Feature Perception for Molecular Traits Prediction via Self-interactive Deep Learning

Author

Hu, Yang, primary, Sirinukunwattana, Korsuk, additional, Li, Bin, additional, Gaitskell, Kezia, additional, Bonnaffe, Willem, additional, Wojciechowska, Marta, additional, Wood, Ruby, additional, Alham, Nasullah Khalid, additional, Malacrino, Stefano, additional, Woodcock, Dan, additional, Verrill, Clare, additional, Ahmed, Ahmed, additional, and Rittscher, Jens, additional

Published
2023
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27. Gland segmentation in colon histology images: The glas challenge contest

Author

Sirinukunwattana, Korsuk, Pluim, Josien P.W., Chen, Hao, Qi, Xiaojuan, Heng, Pheng-Ann, Guo, Yun Bo, Wang, Li Yang, Matuszewski, Bogdan J., Bruni, Elia, Sanchez, Urko, Böhm, Anton, Ronneberger, Olaf, Cheikh, Bassem Ben, Racoceanu, Daniel, Kainz, Philipp, Pfeiffer, Michael, Urschler, Martin, Snead, David R.J., and Rajpoot, Nasir M.

Published
2017
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28. A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images

Author

Sirinukunwattana, Korsuk, Ahmed Raza, Shan E., Tsang, Yee-Wah, Snead, David, Cree, Ian, Rajpoot, Nasir, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wu, Guorong, editor, Coupé, Pierrick, editor, Zhan, Yiqiang, editor, Munsell, Brent, editor, and Rueckert, Daniel, editor

Published
2015
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29. Geodesic Geometric Mean of Regional Covariance Descriptors as an Image-Level Descriptor for Nuclear Atypia Grading in Breast Histology Images

Author

Khan, Adnan Mujahid, Sirinukunwattana, Korsuk, Rajpoot, Nasir, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wu, Guorong, editor, Zhang, Daoqiang, editor, and Zhou, Luping, editor

Published
2014
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31. How to Exploit Weaknesses in Biomedical Challenge Design and Organization

Author

Reinke, Annika, primary, Eisenmann, Matthias, additional, Onogur, Sinan, additional, Stankovic, Marko, additional, Scholz, Patrick, additional, Full, Peter M., additional, Bogunovic, Hrvoje, additional, Landman, Bennett A., additional, Maier, Oskar, additional, Menze, Bjoern, additional, Sharp, Gregory C., additional, Sirinukunwattana, Korsuk, additional, Speidel, Stefanie, additional, van der Sommen, Fons, additional, Zheng, Guoyan, additional, Müller, Henning, additional, Kozubek, Michal, additional, Arbel, Tal, additional, Bradley, Andrew P., additional, Jannin, Pierre, additional, Kopp-Schneider, Annette, additional, and Maier-Hein, Lena, additional

Published
2018
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33. Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients

Author

Ryou, Hosuk, primary, Sirinukunwattana, Korsuk, additional, Aberdeen, Alan, additional, Grindstaff, Gillian, additional, Stolz, Bernadette J., additional, Byrne, Helen, additional, Harrington, Heather A., additional, Sousos, Nikolaos, additional, Godfrey, Anna L., additional, Harrison, Claire N., additional, Psaila, Bethan, additional, Mead, Adam J., additional, Rees, Gabrielle, additional, Turner, Gareth D. H., additional, Rittscher, Jens, additional, and Royston, Daniel, additional

Published
2022
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34. Cross-Talk between Hematopoietic Cells and Fibroblast Subsets Drives Inflammation and Remodelling of the Bone Marrow Microenvironment in Myeloproliferative Neoplasms

Author

Li, Rong, primary, Colombo, Michela, additional, Wang, Guanlin, additional, Clark, Sally-Ann, additional, Rodriguez-Romera, Antonio, additional, Meng, Yiran, additional, Khan, Abdullah, additional, Wen, Wei Xiong, additional, Sousos, Nikolaos, additional, Murphy, Lauren, additional, Simoglou Karali, Christina, additional, Sirinukunwattana, Korsuk, additional, O'Sullivan, Jennifer, additional, Norfo, Ruggiero, additional, Cheng, Qian, additional, Brierley, Charlotte, additional, Carrelha, Joana, additional, Ren, Zemin, additional, Rabinovich, Gabriel A., additional, Rathinam, Vijay A, additional, Thongjuea, Supat, additional, Royston, Daniel, additional, Mead, Adam J, additional, and Psaila, Bethan, additional

Published
2022
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36. Loss of COP9 signalosome genes at 2q37 is associated with IMiD resistance in multiple myeloma

Author

Gooding, Sarah, primary, Ansari-Pour, Naser, additional, Kazeroun, Mohammad, additional, Karagoz, Kubra, additional, Polonskaia, Ann, additional, Salazar, Mirian, additional, Fitzsimons, Evie, additional, Sirinukunwattana, Korsuk, additional, Chavda, Selina, additional, Ortiz Estevez, Maria, additional, Towfic, Fadi, additional, Flynt, Erin, additional, Pierceall, William, additional, Royston, Daniel, additional, Yong, Kwee, additional, Ramasamy, Karthik, additional, Vyas, Paresh, additional, and Thakurta, Anjan, additional

Published
2022
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40. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

Author

Ehteshami Bejnordi, Babak, Veta, Mitko, Johannes van Diest, Paul, van Ginneken, Bram, Karssemeijer, Nico, Litjens, Geert, van der Laak, Jeroen A. W. M., Hermsen, Meyke, Manson, Quirine F, Balkenhol, Maschenka, Geessink, Oscar, Stathonikos, Nikolaos, van Dijk, Marcory CRF, Bult, Peter, Beca, Francisco, Beck, Andrew H, Wang, Dayong, Khosla, Aditya, Gargeya, Rishab, Irshad, Humayun, Zhong, Aoxiao, Dou, Qi, Li, Quanzheng, Chen, Hao, Lin, Huang-Jing, Heng, Pheng-Ann, Haß, Christian, Bruni, Elia, Wong, Quincy, Halici, Ugur, Öner, Mustafa Ümit, Cetin-Atalay, Rengul, Berseth, Matt, Khvatkov, Vitali, Vylegzhanin, Alexei, Kraus, Oren, Shaban, Muhammad, Rajpoot, Nasir, Awan, Ruqayya, Sirinukunwattana, Korsuk, Qaiser, Talha, Tsang, Yee-Wah, Tellez, David, Annuscheit, Jonas, Hufnagl, Peter, Valkonen, Mira, Kartasalo, Kimmo, Latonen, Leena, Ruusuvuori, Pekka, Liimatainen, Kaisa, Albarqouni, Shadi, Mungal, Bharti, George, Ami, Demirci, Stefanie, Navab, Nassir, Watanabe, Seiryo, Seno, Shigeto, Takenaka, Yoichi, Matsuda, Hideo, Ahmady Phoulady, Hady, Kovalev, Vassili, Kalinovsky, Alexander, Liauchuk, Vitali, Bueno, Gloria, Fernandez-Carrobles, M. Milagro, Serrano, Ismael, Deniz, Oscar, Racoceanu, Daniel, and Venâncio, Rui

Published
2017
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45. PathProfiler: Automated Quality Assessment of Retrospective Histopathology Whole-Slide Image Cohorts by Artificial Intelligence – A Case Study for Prostate Cancer Research

Author

Haghighat, Maryam, primary, Browning, Lisa, additional, Sirinukunwattana, Korsuk, additional, Malacrino, Stefano, additional, Alham, Nasullah Khalid, additional, Colling, Richard, additional, Cui, Ying, additional, Rakha, Emad, additional, Hamdy, Freddie C., additional, Verrill, Clare, additional, and Rittscher, Jens, additional

Published
2021
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46. Additional file 1 of Associations of reproductive breast cancer risk factors with breast tissue composition

Author

Yaghjyan, Lusine, Austin-Datta, Rebecca J., Oh, Hannah, Heng, Yujing J., Vellal, Adithya D., Sirinukunwattana, Korsuk, Baker, Gabrielle M., Collins, Laura C., Murthy, Divya, Rosner, Bernard, and Tamimi, Rulla M.

Subjects
skin and connective tissue diseases
Abstract

Additional file 1: Table S1. Distribution of various tissue elements by the type of benign breast disease. Table S2. Associations of reproductive factors with tissue types in benign breast biopsy samples using proc glimmix procedure (Odds Ratios and 95% Confidence Intervals). Table S3. Associations of reproductive variables with tissue types in benign breast biopsy samples of premenopausal women using proc glimmix procedure (Odds Ratios and 95% Confidence Intervals). Table S4. Associations of reproductive variables with percentage of different tissue types (log-transformed) in benign breast biopsy samples (β coefficients and 95% Confidence Intervals), additionally adjusted for BBD subtype. Table S5. Associations of reproductive variables with percentage of different tissue types (log-transformed) in benign breast biopsy samples of premenopausal women (β coefficients and 95% Confidence Intervals), additionally adjusted for BBD subtype.

Published
2021
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47. Additional file 1 of Transcriptome and genome evolution during HER2-amplified breast neoplasia

Author

Lu, Peipei, Foley, Joseph, Zhu, Chunfang, McNamara, Katherine, Sirinukunwattana, Korsuk, Vennam, Sujay, Varma, Sushama, Fehri, Hamid, Srivastava, Arunima, Zhu, Shirley, Rittscher, Jens, Mallick, Parag, Curtis, Christina, and West, Robert

Subjects
skin and connective tissue diseases, neoplasms
Abstract

Additional file 1: Supplementary Figure 1. Pipeline for automatic counting of FISH signals in DCIS epithelial cells. Supplementary Figure 2. The probability density of HER2-FISH signal/cell in DCIS_noamp and DCIS_amp references. The reference samples (18 non-amplified DCISs and 29 amplified DCISs) were handpicked from HER2-positive patients and the cells within each sample were combined into one reference for each group. The HER2-FISH signal/cell represents the absolute number of HER2-FISH signals in an epithelial cell in DCIS. The distribution of HER2-FISH signal/cell for all detected epithelial cells in a DCIS was represented by the probability density using kernel density estimation. Supplementary Figure 3. The probability distribution of HER2-FISH signal/cell in DCISs for each HER2-positive patient. The HER2-FISH signal/cell represents the absolute number of HER2-FISH signals in an epithelial cell in DCIS. The distribution of HER2-FISH signal/cell for all detected epithelial cells in a DCIS was represented by the probability density using kernel density estimation. For each patient, the density plots of HER2-FISH signal for each individual DCIS were overlayed into one plot. Supplementary Figure 4. Principal Component Analysis (PCA) on the distribution of HER2-FISH signal/cell in DCISs for each HER2-positive patient. The HER2-FISH signal/cell represents the absolute number of HER2-FISH signals in an epithelial cell in DCIS. The distribution of HER2-FISH signal/cell in a DCIS was represented by the probability density. Supplementary Figure 5. Pipeline of inferring genome-wide copy number variations (CNVs) from Smart-3SEQ and WGS data. Supplementary Figure 6. Principal Component Analysis (PCA) from gene expression data on combined DCISs of all HER2-positive patients showing that ‘patient’ factor explains the most variability. Supplementary Figure 7. (a) Heatmap of the mean-centered expressions of the 43 interferon-stimulated genes (ISGs) in DCIS_amp/DCIS_noamp and IDC_amp. These 43 ISGs were identified by gene set enrichment analysis on the up-regulated genes in comparison between DCIS_amp and DCIS_noamp. Patient IDs and sample types were denoted as the top bar. (b) Mean ISG signature scores in 5 patients with paired DCIS_amp and IDC_amp samples. The mean ISG score was calculated per case for DCIS_amp and IDC_amp separately if there are >1 samples in each group. Paired T-test was performed to determine the statistical significance between DCIS_amp and IDC_amp. p

Published
2021
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50. Artificial Intelligence for Advance Requesting of Immunohistochemistry in Diagnostically Uncertain Prostate Biopsies

Author

Chatrian, Andrea, primary, Colling, Richard T, additional, Browning, Lisa, additional, Alham, Nasullah Khalid, additional, Sirinukunwattana, Korsuk, additional, Malacrino, Stefano, additional, Haghighat, Maryam, additional, Aberdeen, Alan, additional, Monks, Amelia, additional, Moxley-Wyles, Benjamin, additional, Rakha, Emad, additional, Snead, David R J, additional, Rittscher, Jens, additional, and Verrill, Clare, additional

Published
2021
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