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Your search keyword '"Grabsch, Heike Irmgard"' showing total 8 results
Start Over Author "Grabsch, Heike Irmgard" Publication Type Academic Journals
8 results on '"Grabsch, Heike Irmgard"'

1. Clinical practice guidelines for esophagogastric junction cancer: Upper GI Oncology Summit 2023

Author

Kitagawa, Yuko, Matsuda, Satoru, Gotoda, Takuji, Kato, Ken, Wijnhoven, Bas, Lordick, Florian, Bhandari, Pradeep, Kawakubo, Hirofumi, Kodera, Yasuhiro, Terashima, Masanori, Muro, Kei, Takeuchi, Hiroya, Mansfield, Paul F., Kurokawa, Yukinori, So, Jimmy, Mönig, Stefan Paul, Shitara, Kohei, Rha, Sun Young, Janjigian, Yelena, Takahari, Daisuke, Chau, Ian, Sharma, Prateek, Ji, Jiafu, de Manzoni, Giovanni, Nilsson, Magnus, Kassab, Paulo, Hofstetter, Wayne L., Smyth, Elizabeth Catherine, Lorenzen, Sylvie, Doki, Yuichiro, Law, Simon, Oh, Do-Youn, Ho, Khek Yu, Koike, Tomoyuki, Shen, Lin, van Hillegersberg, Richard, Kawakami, Hisato, Xu, Rui-Hua, Wainberg, Zev, Yahagi, Naohisa, Lee, Yeong Yeh, Singh, Rajvinder, Ryu, Min-Hee, Ishihara, Ryu, Xiao, Zili, Kusano, Chika, Grabsch, Heike Irmgard, Hara, Hiroki, Mukaisho, Ken-ichi, Makino, Tomoki, Kanda, Mitsuro, Booka, Eisuke, Suzuki, Sho, Hatta, Waku, Kato, Motohiko, Maekawa, Akira, Kawazoe, Akihito, Yamamoto, Shun, Nakayama, Izuma, Narita, Yukiya, Yang, Han-Kwang, Yoshida, Masahiro, and Sano, Takeshi

Published
2024
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2. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

Author

Ghaffari Laleh, Narmin, Muti, Hannah Sophie, Loeffler, Chiara Maria Lavinia, Echle, Amelie, Saldanha, Oliver Lester, Mahmood, Faisal, Lu, Ming Y., Trautwein, Christian, Langer, Rupert, Dislich, Bastian, Buelow, Roman D., Grabsch, Heike Irmgard, Brenner, Hermann, Chang-Claude, Jenny, Alwers, Elizabeth, Brinker, Titus J., Khader, Firas, Truhn, Daniel, Gaisa, Nadine T., Boor, Peter, Hoffmeister, Michael, Schulz, Volkmar, and Kather, Jakob Nikolas

Published
2022
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3. Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Author

Echle, Amelie, Grabsch, Heike Irmgard, Quirke, Philip, van den Brandt, Piet A., West, Nicholas P., Hutchins, Gordon G.A., Heij, Lara R., Tan, Xiuxiang, Richman, Susan D., Krause, Jeremias, Alwers, Elizabeth, Jenniskens, Josien, Offermans, Kelly, Gray, Richard, Brenner, Hermann, Chang-Claude, Jenny, Trautwein, Christian, Pearson, Alexander T., Boor, Peter, Luedde, Tom, Gaisa, Nadine Therese, Hoffmeister, Michael, and Kather, Jakob Nikolas

Published
2020
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4. Genomic and epigenomic EBF1 alterations modulate TERT expression in gastric cancer

Author

Xing, Manjie, Ooi, Wen Fong, Tan, Jing, Qamra, Aditi, Lee, Po-Hsien, Li, Zhimei, Xu, Chang, Padmanabhan, Nisha, Lim, Jing Quan, Guo, Yu Amanda, Yao, Xiaosai, Amit, Mandoli, Ng, Ley Moy, Sheng, Taotao, Wang, Jing, Huang, Kie Kyon, Anene-Nzelu, Chukwuemeka George, Ho, Shamaine Wei Ting, Ray, Mohana, Ma, Lijia, Fazzi, Gregorio, Lim, Kevin Junliang, Wijaya, Giovani Claresta, Zhang, Shenli, Nandi, Tannistha, Yan, Tingdong, Chang, Mei Mei, Das, Kakoli, Isa, Zul Fazreen Adam, Wu, Jeanie, Poon, Polly Suk Yean, Lam, Yue Ning, Lin, Joyce Suling, Tay, Su Ting, Lee, Ming Hui, Tan, Angie Lay Keng, Ong, Xuewen, White, Kevin, Rozen, Steven George, Beer, Michael, Foo, Roger Sik Yin, Grabsch, Heike Irmgard, Skanderup, Anders Jacobsen, Li, Shang, Teh, Bin Tean, and Tan, Patrick

Subjects
Mortality -- Singapore, Stomach cancer -- Health aspects -- Analysis, Vorinostat -- Health aspects -- Analysis, Cancer genetics -- Health aspects -- Analysis, Cancer -- Analysis -- Health aspects, Nivolumab -- Analysis -- Health aspects, Transcription (Genetics) -- Analysis -- Health aspects, Telomerase -- Health aspects -- Analysis, Pembrolizumab -- Health aspects -- Analysis, Health care industry
Abstract

Transcriptional reactivation of telomerase catalytic subunit (TERT) is a frequent hallmark of cancer, occurring in 90% of human malignancies. However, specific mechanisms driving TERT reactivation remain obscure for many tumor types and in particular gastric cancer (GC), a leading cause of global cancer mortality. Here, through comprehensive genomic and epigenomic analysis of primary GCs and GC cell lines, we identified the transcription factor early B cell factor 1 (EBF1) as a TERT transcriptional repressor and inactivation of EBF1 function as a major cause of TERT upregulation. Abolishment of EBF1 function occurs through 3 distinct (epi)genomic mechanisms. First, EBF1 is epigenetically silenced via DNA methyltransferase, polycomb-repressive complex 2 (PRC2), and histone deacetylase activity in GCs. Second, recurrent, somatic, and heterozygous EBF1 DNA-binding domain mutations result in the production of dominant-negative EBF1 isoforms. Third, more rarely, genomic deletions and rearrangements proximal to the TERT promoter remobilize or abolish EBF7-binding sites, derepressing TERT and leading to high TERT expression. EBF1 is also functionally required for various malignant phenotypes in vitro and in vivo, highlighting its importance for GC development. These results indicate that multimodal genomic and epigenomic alterations underpin TERT reactivation in GC, converging on transcriptional repressors such as EBF1., Introduction Gastric cancer (GC) is a leading cause of global cancer mortality (1), with a particularly high prevalence in East Asia. With the exception of Japan and South Korea, where [...]

Published
2020
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5. Erratum to ‘Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology’ Medical Image Analysis, Volume 79, July 2022, 102474

Author

Ghaffari Laleh, Narmin, Muti, Hannah Sophie, Loeffler, Chiara Maria Lavinia, Echle, Amelie, Saldanha, Oliver Lester, Mahmood, Faisal, Lu, Ming Y., Trautwein, Christian, Langer, Rupert, Dislich, Bastian, Buelow, Roman D., Grabsch, Heike Irmgard, Brenner, Hermann, Chang-Claude, Jenny, Alwers, Elizabeth, Brinker, Titus J., Khader, Firas, Truhn, Daniel, Gaisa, Nadine T., Boor, Peter, Hoffmeister, Michael, Schulz, Volkmar, and Kather, Jakob Nikolas

Published
2022
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8. Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer.

Author

Brockmoeller, Scarlet, Echle, Amelie, Ghaffari Laleh, Narmin, Eiholm, Susanne, Malmstrøm, Marie Louise, Plato Kuhlmann, Tine, Levic, Katarina, Grabsch, Heike Irmgard, West, Nicholas P, Saldanha, Oliver Lester, Kouvidi, Katerina, Bono, Aurora, Heij, Lara R, Brinker, Titus J, Gögenür, Ismayil, Quirke, Philip, and Kather, Jakob Nikolas

Subjects
LYMPHATIC metastasis, DEEP learning, COLORECTAL cancer, MACHINE learning, ARTIFICIAL intelligence, AGE factors in cancer, SIGNAL convolution
Abstract

The spread of early‐stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end‐to‐end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population‐based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67–0.758) and patients with any LNM with an AUROC of 0.711 (0.597–0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644–0.778) and 0.567 (0.542–0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2022
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