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

Author

MS CGO, Cancer, 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, Sano, Takeshi, MS CGO, Cancer, 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

6. 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, Sano, Takeshi, 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

7. Clinicopathological and molecular factors related to chromosomal instability and its underlying genetic mechanisms in gastric cancer

Author

Grabsch, Heike Irmgard

Subjects
616.99433
Abstract

The majority of gastric cancer (GC) patients present at advanced stage and have a poor prognosis. Poor survival might be related to the unavailability of molecular markers to determine patient management. Most GC are considered genetically unstable (microsatellite unstable (MSI)) and/or chromosomal unstable (CIN). Recent studies suggest that CIN cancers are multi-drug resistant. First drugs targeting specific characteristics of CIN cancer have shown activity in vitro. Thus, there is an urgent need to fully understand the mechanisms leading to and maintaining CIN in GC as this might identify new treatment targets. It has been proposed that CIN might be caused by an impaired DNA damage response (DDR). The current study characterised the type of genetic instability present in GC measuring DNA ploidy (CIN surrogate marker) as well as MSI and investigated in parallel the expression of all key DDR proteins and their downstream signalling pathways regulating cell cycle progression in a retrospectively collected series of GC using immunohistochemistry and tissue microarray technology. GC were also screened for KRAS and BRAF mutations as the RAS signalling pathway had been implicated in generating genetic instability in colorectal cancer. This is the first study to identify a significant overlap between MSI and CIN in a subset of GC with poor prognosis. DNA double strand breaks were rare in GC making deregulated DNA damage response unlikely as the major mechanism leading to CIN in GC. The identification of GC with low proliferative index, low DNA repair activity and poor survival suggested that a subset of GC may switch from proliferation to invasion, identifying a potential additional cause for GC resistance against cytotoxic drugs warranting further studies. KRASIBRAF mutations were rare in GC, related to MSI but not to CIN. Results from this exploratory study require and warrant validation in a larger independent cohort.

Published
2011

8. Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study

Author

Muti, Hannah Sophie, primary, Röcken, Christoph, additional, Behrens, Hans-Michael, additional, Loeffler, Chiara Maria Lavinia, additional, Reitsam, Nic Gabriel, additional, Grosser, Bianca, additional, Märkl, Bruno, additional, Stange, Daniel E., additional, Jiang, Xiaofeng, additional, Veldhuizen, Gregory Patrick, additional, Truhn, Daniel, additional, Ebert, Matthias P., additional, Grabsch, Heike Irmgard, additional, and Kather, Jakob Nikolas, additional

Published
2023
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9. 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, primary, Muti, Hannah Sophie, additional, Loeffler, Chiara Maria Lavinia, additional, Echle, Amelie, additional, Saldanha, Oliver Lester, additional, Mahmood, Faisal, additional, Lu, Ming Y., additional, Trautwein, Christian, additional, Langer, Rupert, additional, Dislich, Bastian, additional, Buelow, Roman D., additional, Grabsch, Heike Irmgard, additional, Brenner, Hermann, additional, Chang-Claude, Jenny, additional, Alwers, Elizabeth, additional, Brinker, Titus J., additional, Khader, Firas, additional, Truhn, Daniel, additional, Gaisa, Nadine T., additional, Boor, Peter, additional, Hoffmeister, Michael, additional, Schulz, Volkmar, additional, and Kather, Jakob Nikolas, additional

Published
2022
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10. 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, Kather, Jakob Nikolas, 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

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.

Published
2022

11. 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

Subjects
610 Medizin und Gesundheit, 570 Biowissenschaften, Biologie
Published
2022
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12. Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

Author

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

Published
2021
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15. Benchmarking artificial intelligence methods for end-to-end computational pathology

Author

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

Published
2021
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16. Epstein-Barr virus and mismatch repair deficiency status differ between oesophageal and gastric cancer: A large multi-centre study

Author

Hewitt, Lindsay C., Inam, Imran Z., Saito, Yuichi, Yoshikawa, Takaki, Quaas, Alexander, Hölscher, Arnulf H., Bollschweiler, Elfriede, Fazzi, Gregorio E., Melotte, Veerle, Langley, Ruth Elizabeth, Nankivell, Matthew, Cunningham, David, Allum, William, Hutchins, Gordon G., Grabsch, Heike Irmgard, Clinical Genetics, Promovendi ODB, Pathologie, and RS: GROW - R2 - Basic and Translational Cancer Biology

Subjects
HIGH-INCIDENCE AREA, Epsteine-Barr virus, Oesophageal cancer, DNA mismatch repair, COLON-CANCER, NONPOLYPOSIS COLORECTAL-CANCER, RANDOMIZED PHASE-3 TRIAL, OPEN-LABEL, PD-1 BLOCKADE, DOUBLE-BLIND, MICROSATELLITE-INSTABILITY, SDG 3 - Good Health and Well-being, hemic and lymphatic diseases, Microsatellite instability, ddc:610, SQUAMOUS-CELL CARCINOMA, CLINICAL-PRACTICE GUIDELINES, Gastric cancer
Abstract

Background: Oesophageal (OeC) and gastric (GC) cancer patients are treated with similar multimodal therapy and have poor survival. There remains an urgent clinical need to identify biomarkers to individualise patient management and improve outcomes. Therapy with immune checkpoint inhibitors has shown promising results in other cancers. Proposed biomarkers to predict potential response to immune checkpoint inhibitors include DNA mismatch repair (MMR) and/ or EpsteineBarr virus (EBV) status. The aim of this study was to establish and compare EBV status and MMR status in large multi-centre series of OeC and GC. Methods: EBV was assessed by EBV-encoded RNA (EBER) in situ hybridisation and MMR protein expression by immunohistochemistry (IHC) in 988 OeC and 1213 GC from multiple centres. In a subset of OeC, microsatellite instability (MSI) was tested in parallel with MMR IHC. Results: Frequency of MMR deficiency (MMRdef) and MSI was low in OeC (0.8% and 0.6%, respectively) compared with GC (10.3%). None of the OeCs were EBER positive in contrast to 4.8% EBER positive GC. EBV positive GC patients were younger (p = 0.01), more often male (p = 0.001) and had a better overall survival (p = 0.012). MMRdef GC patients were older (p = 0.001) and showed more often intestinal-type histology (p = 0.022). Conclusions: This is the largest study to date indicating that EBV and MMRdef do not play a role in OeC carcinogenesis in contrast to GC. The potential clinical usefulness of determining MMRdef/EBV status to screen patients for eligibility for immune-targeting therapy differs between OeC and GC patients. (C) 2018 The Authors. Published by Elsevier Ltd.

Published
2018

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

Author

Brockmoeller S, Echle A, Ghaffari Laleh N, Eiholm S, Malmstrøm ML, Plato Kuhlmann T, Levic K, Grabsch HI, West NP, Saldanha OL, Kouvidi K, Bono A, Heij LR, Brinker TJ, Gögenür I, Quirke P, and Kather JN

Subjects
Biopsy, Humans, Lymphatic Metastasis, Neoplasm Staging, Predictive Value of Tests, Proof of Concept Study, Reproducibility of Results, Retrospective Studies, Risk Assessment, Risk Factors, Adipose Tissue pathology, Colorectal Neoplasms pathology, Deep Learning, Diagnosis, Computer-Assisted, Early Detection of Cancer, Image Interpretation, Computer-Assisted, Lymph Nodes pathology, Microscopy
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., (© 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.)

Published
2022
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