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551 results on '"Koelzer, Viktor H"'

1. SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction

Author

Schoenpflug, Lydia A. and Koelzer, Viktor H.

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

Detecting and classifying cells in histopathology H\&E stained whole-slide images is a core task in computational pathology, as it provides valuable insight into the tumor microenvironment. In this work we investigate the impact of ground truth formats on the models performance. Additionally, cell-tissue interactions are considered by providing tissue segmentation predictions as input to the cell detection model. We find that a "soft", probability-map instance segmentation ground truth leads to best model performance. Combined with cell-tissue interaction and test-time augmentation our Soft Cell-Tissue-Model (SoftCTM) achieves 0.7172 mean F1-Score on the Overlapped Cell On Tissue (OCELOT) test set, achieving the third best overall score in the OCELOT 2023 Challenge. The source code for our approach is made publicly available at https://github.com/lely475/ocelot23algo.

Published
2023

2. Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge

Author

Aubreville, Marc, Stathonikos, Nikolas, Donovan, Taryn A., Klopfleisch, Robert, Ganz, Jonathan, Ammeling, Jonas, Wilm, Frauke, Veta, Mitko, Jabari, Samir, Eckstein, Markus, Annuscheit, Jonas, Krumnow, Christian, Bozaba, Engin, Cayir, Sercan, Gu, Hongyan, Chen, Xiang 'Anthony', Jahanifar, Mostafa, Shephard, Adam, Kondo, Satoshi, Kasai, Satoshi, Kotte, Sujatha, Saipradeep, VG, Lafarge, Maxime W., Koelzer, Viktor H., Wang, Ziyue, Zhang, Yongbing, Yang, Sen, Wang, Xiyue, Breininger, Katharina, and Bertram, Christof A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert consensus and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an $F_1$ score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today's deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, but with only minor changes in the order of participants in the ranking.

Published
2023
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3. CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models

Author

Fan, Fan, Martinez, Georgia, Desilvio, Thomas, Shin, John, Chen, Yijiang, Wang, Bangchen, Ozeki, Takaya, Lafarge, Maxime W., Koelzer, Viktor H., Barisoni, Laura, Madabhushi, Anant, Viswanath, Satish E., and Janowczyk, Andrew

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder, an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com., Comment: 26 pages, 9 figures, 4 tables. Abstract was accepted by European Society of Digital and Integrative Pathology (ESDIP), Germany, 2022

Published
2023

4. Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia

Author

Wegmann, Rebekka, Bonilla, Ximena, Casanova, Ruben, Chevrier, Stéphane, Coelho, Ricardo, Esposito, Cinzia, Ficek-Pascual, Joanna, Goetze, Sandra, Gut, Gabriele, Jacob, Francis, Jacobs, Andrea, Kuipers, Jack, Lischetti, Ulrike, Mena, Julien, Milani, Emanuela S., Prummer, Michael, Del Castillo, Jacobo Sarabia, Singer, Franziska, Sivapatham, Sujana, Toussaint, Nora C., Vilinovszki, Oliver, Wildschut, Mattheus H. E., Thavayogarajah, Tharshika, Malani, Disha, Aebersold, Rudolf, Bacac, Marina, Beerenwinkel, Niko, Beisel, Christian, Bodenmiller, Bernd, Heinzelmann-Schwarz, Viola, Koelzer, Viktor H., Levesque, Mitchell P., Moch, Holger, Pelkmans, Lucas, Rätsch, Gunnar, Tolnay, Markus, Wicki, Andreas, Wollscheid, Bernd, Manz, Markus G., Snijder, Berend, and Theocharides, Alexandre P. A.

Published
2024
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6. 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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7. Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Author

Malla, Sudhir B., Byrne, Ryan M., Lafarge, Maxime W., Corry, Shania M., Fisher, Natalie C., Tsantoulis, Petros K., Mills, Megan L., Ridgway, Rachel A., Lannagan, Tamsin R. M., Najumudeen, Arafath K., Gilroy, Kathryn L., Amirkhah, Raheleh, Maguire, Sarah L., Mulholland, Eoghan J., Belnoue-Davis, Hayley L., Grassi, Elena, Viviani, Marco, Rogan, Emily, Redmond, Keara L., Sakhnevych, Svetlana, McCooey, Aoife J., Bull, Courtney, Hoey, Emily, Sinevici, Nicoleta, Hall, Holly, Ahmaderaghi, Baharak, Domingo, Enric, Blake, Andrew, Richman, Susan D., Isella, Claudio, Miller, Crispin, Bertotti, Andrea, Trusolino, Livio, Loughrey, Maurice B., Kerr, Emma M., Tejpar, Sabine, Maughan, Timothy S., Lawler, Mark, Campbell, Andrew D., Leedham, Simon J., Koelzer, Viktor H., Sansom, Owen J., and Dunne, Philip D.

Published
2024
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8. Multi-task learning for tissue segmentation and tumor detection in colorectal cancer histology slides

Author

Schoenpflug, Lydia A., Lafarge, Maxime W., Frei, Anja L., and Koelzer, Viktor H.

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

Automating tissue segmentation and tumor detection in histopathology images of colorectal cancer (CRC) is an enabler for faster diagnostic pathology workflows. At the same time it is a challenging task due to low availability of public annotated datasets and high variability of image appearance. The semi-supervised learning for CRC detection (SemiCOL) challenge 2023 provides partially annotated data to encourage the development of automated solutions for tissue segmentation and tumor detection. We propose a U-Net based multi-task model combined with channel-wise and image-statistics-based color augmentations, as well as test-time augmentation, as a candidate solution to the SemiCOL challenge. Our approach achieved a multi-task Dice score of .8655 (Arm 1) and .8515 (Arm 2) for tissue segmentation and AUROC of .9725 (Arm 1) and 0.9750 (Arm 2) for tumor detection on the challenge validation set. The source code for our approach is made publicly available at https://github.com/lely475/CTPLab_SemiCOL2023.

Published
2023

9. Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset

Author

Lafarge, Maxime W. and Koelzer, Viktor H.

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

Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) to address the problem of generalization for mitosis detection in images of hematoxylin-eosin-stained histology slides under high variability (scanner, tissue type and species variability). Our approach consists in training a rotation-invariant deep learning model using aggressive data augmentation with a training set enriched with hard negative examples and automatically selected negative examples from the unlabeled part of the challenge dataset. To optimize the performance of our models, we investigated a hard negative mining regime search procedure that lead us to train our best model using a subset of image patches representing 19.6% of our training partition of the challenge dataset. Our candidate model ensemble achieved a F1-score of .697 on the final test set after automated evaluation on the challenge platform, achieving the third best overall score in the MIDOG 2022 Challenge.

Published
2023

10. Swiss digital pathology recommendations: results from a Delphi process conducted by the Swiss Digital Pathology Consortium of the Swiss Society of Pathology

Author

Janowczyk, Andrew, Zlobec, Inti, Walker, Cedric, Berezowska, Sabina, Huschauer, Viola, Tinguely, Marianne, Kupferschmid, Joel, Mallet, Thomas, Merkler, Doron, Kreutzfeldt, Mario, Gasic, Radivoje, Rau, Tilman T., Mazzucchelli, Luca, Eyberg, Isgard, Cathomas, Gieri, Mertz, Kirsten D., Koelzer, Viktor H., Soldini, Davide, Jochum, Wolfram, Rössle, Matthias, Henkel, Maurice, and Grobholz, Rainer

Published
2023
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12. Mitosis domain generalization in histopathology images -- The MIDOG challenge

Author

Aubreville, Marc, Stathonikos, Nikolas, Bertram, Christof A., Klopleisch, Robert, ter Hoeve, Natalie, Ciompi, Francesco, Wilm, Frauke, Marzahl, Christian, Donovan, Taryn A., Maier, Andreas, Breen, Jack, Ravikumar, Nishant, Chung, Youjin, Park, Jinah, Nateghi, Ramin, Pourakpour, Fattaneh, Fick, Rutger H. J., Hadj, Saima Ben, Jahanifar, Mostafa, Rajpoot, Nasir, Dexl, Jakob, Wittenberg, Thomas, Kondo, Satoshi, Lafarge, Maxime W., Koelzer, Viktor H., Liang, Jingtang, Wang, Yubo, Long, Xi, Liu, Jingxin, Razavi, Salar, Khademi, April, Yang, Sen, Wang, Xiyue, Veta, Mitko, and Breininger, Katharina

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

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F_1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance., Comment: 19 pages, 9 figures, summary paper of the 2021 MICCAI MIDOG challenge

Published
2022
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13. Towards IID representation learning and its application on biomedical data

Author

Wu, Jiqing, Zlobec, Inti, Lafarge, Maxime, He, Yukun, and Koelzer, Viktor H.

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to the heterogeneity of real-world data, the widely accepted independent and identically distributed (IID) assumption has been criticized in recent studies on causality. In this paper, we argue that instead of being a questionable assumption, IID is a fundamental task-relevant property that needs to be learned. Consider $k$ independent random vectors $\mathsf{X}^{i = 1, \ldots, k}$, we elaborate on how a variety of different causal questions can be reformulated to learning a task-relevant function $\phi$ that induces IID among $\mathsf{Z}^i := \phi \circ \mathsf{X}^i$, which we term IID representation learning. For proof of concept, we examine the IID representation learning on Out-of-Distribution (OOD) generalization tasks. Concretely, by utilizing the representation obtained via the learned function that induces IID, we conduct prediction of molecular characteristics (molecular prediction) on two biomedical datasets with real-world distribution shifts introduced by a) preanalytical variation and b) sampling protocol. To enable reproducibility and for comparison to the state-of-the-art (SOTA) methods, this is done by following the OOD benchmarking guidelines recommended from WILDS. Compared to the SOTA baselines supported in WILDS, the results confirm the superior performance of IID representation learning on OOD tasks. The code is publicly accessible via https://github.com/CTPLab/IID_representation_learning., Comment: The paper is accepted by MIDL 2022--full paper track

Published
2022

14. Author Correction: Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Author

Malla, Sudhir B., Byrne, Ryan M., Lafarge, Maxime W., Corry, Shania M., Fisher, Natalie C., Tsantoulis, Petros K., Mills, Megan L., Ridgway, Rachel A., Lannagan, Tamsin R. M., Najumudeen, Arafath K., Gilroy, Kathryn L., Amirkhah, Raheleh, Maguire, Sarah L., Mulholland, Eoghan J., Belnoue-Davis, Hayley L., Grassi, Elena, Viviani, Marco, Rogan, Emily, Redmond, Keara L., Sakhnevych, Svetlana, McCooey, Aoife J., Bull, Courtney, Hoey, Emily, Sinevici, Nicoleta, Hall, Holly, Ahmaderaghi, Baharak, Domingo, Enric, Blake, Andrew, Richman, Susan D., Isella, Claudio, Miller, Crispin, Bertotti, Andrea, Trusolino, Livio, Loughrey, Maurice B., Kerr, Emma M., Tejpar, Sabine, Maughan, Timothy S., Lawler, Mark, Campbell, Andrew D., Leedham, Simon J., Koelzer, Viktor H., Sansom, Owen J., and Dunne, Philip D.

Published
2024
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16. Single-cell AI-based detection and prognostic and predictive value of DNA mismatch repair deficiency in colorectal cancer

Author

Glimelius, Bengt, Gogenur, Ismail, Jaeger, Emma, Morgan, Hannah, Orange, Clare, Palles, Claire, Roxburgh, Campbell, Nowak, Marta, Jabbar, Faiz, Rodewald, Ann-Katrin, Gneo, Luciana, Tomasevic, Tijana, Harkin, Andrea, Iveson, Tim, Saunders, Mark, Kerr, Rachel, Oein, Karin, Maka, Noori, Hay, Jennifer, Edwards, Joanne, Tomlinson, Ian, Sansom, Owen, Kelly, Caroline, Pezzella, Francesco, Kerr, David, Easton, Alistair, Domingo, Enric, Koelzer, Viktor H., and Church, David N.

Published
2024
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18. Automated causal inference in application to randomized controlled clinical trials

Author

Wu, Jiqing, Horeweg, Nanda, de Bruyn, Marco, Nout, Remi A., Jürgenliemk-Schulz, Ina M., Lutgens, Ludy C. H. W., Jobsen, Jan J., van der Steen-Banasik, Elzbieta M., Nijman, Hans W., Smit, Vincent T. H. B. M., Bosse, Tjalling, Creutzberg, Carien L., and Koelzer, Viktor H.

Subjects
Statistics - Methodology, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Randomized controlled trials (RCTs) are considered as the gold standard for testing causal hypotheses in the clinical domain. However, the investigation of prognostic variables of patient outcome in a hypothesized cause-effect route is not feasible using standard statistical methods. Here, we propose a new automated causal inference method (AutoCI) built upon the invariant causal prediction (ICP) framework for the causal re-interpretation of clinical trial data. Compared to existing methods, we show that the proposed AutoCI allows to efficiently determine the causal variables with a clear differentiation on two real-world RCTs of endometrial cancer patients with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remain consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis., Comment: Submitted to Nature Machine Intelligence. The code is publicly available via https://github.com/CTPLab/AutoCI

Published
2022

21. Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge

Author

Lafarge, Maxime W. and Koelzer, Viktor H.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen target scanners (hidden for the participants) under the constraint of using training data from a limited set of four independent source scanners. Given this goal and constraints, we joined the challenge by proposing a straight-forward solution based on a combination of state-of-the-art deep learning methods with the aim of yielding robustness to possible scanner-related distributional shifts at inference time. Our solution combines methods that were previously shown to be efficient for mitosis detection: hard negative mining, extensive data augmentation, rotation-invariant convolutional networks. We trained five models with different splits of the provided dataset. The subsequent classifiers produced F1-scores with a mean and standard deviation of 0.747+/-0.032 on the test splits. The resulting ensemble constitutes our candidate algorithm: its automated evaluation on the preliminary test set of the challenge returned a F1-score of 0.6828.

Published
2021

22. Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials

Author

Wakkerman, Famke C, Wu, Jiqing, Putter, Hein, Jürgenliemk-Schulz, Ina M, Jobsen, Jan J, Lutgens, Ludy C H W, Haverkort, Marie A D, de Jong, Marianne A, Mens, Jan Willem M, Wortman, Bastiaan G, Nout, Remi A, Léon-Castillo, Alicia, Powell, Melanie E, Mileshkin, Linda R, Katsaros, Dionyssios, Alfieri, Joanne, Leary, Alexandra, Singh, Naveena, de Boer, Stephanie M, Nijman, Hans W, Smit, Vincent T H B M, Bosse, Tjalling, Koelzer, Viktor H, Creutzberg, Carien L, and Horeweg, Nanda

Published
2024
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24. Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge

Author

Aubreville, Marc, Stathonikos, Nikolas, Donovan, Taryn A., Klopfleisch, Robert, Ammeling, Jonas, Ganz, Jonathan, Wilm, Frauke, Veta, Mitko, Jabari, Samir, Eckstein, Markus, Annuscheit, Jonas, Krumnow, Christian, Bozaba, Engin, Çayır, Sercan, Gu, Hongyan, Chen, Xiang ‘Anthony’, Jahanifar, Mostafa, Shephard, Adam, Kondo, Satoshi, Kasai, Satoshi, Kotte, Sujatha, Saipradeep, V.G., Lafarge, Maxime W., Koelzer, Viktor H., Wang, Ziyue, Zhang, Yongbing, Yang, Sen, Wang, Xiyue, Breininger, Katharina, and Bertram, Christof A.

Published
2024
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26. 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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27. Multiplex analysis of intratumoural immune infiltrate and prognosis in patients with stage II–III colorectal cancer from the SCOT and QUASAR 2 trials: a retrospective analysis

Author

Church, David N, Domingo, Enric, Edwards, Joanne, Glimelius, Bengt, Gonegur, Ismail, Harkin, Andrea, Hay, Jen, Iveson, Timothy, Jaeger, Emma, Kelly, Caroline, Kerr, Rachel, Maka, Noori, Morgan, Hannah, Oien, Karen, Orange, Clare, Palles, Claire, Roxburgh, Campbell, Sansom, Owen, Saunders, Mark P, Tomlinson, Ian, Frei, Anja L, McGuigan, Anthony, Sinha, Ritik R A K, Jabbar, Faiz, Gneo, Luciana, Tomasevic, Tijana, Iveson, Tim, Oien, Karin A, Pezzella, Francesco, Campo, Leticia, Browne, Molly, Glaire, Mark, Kildal, Wanja, Danielsen, Havard E, Hay, Jennifer, Kerr, David, and Koelzer, Viktor H

Published
2024
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28. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Author

Church, David, Domingo, Enric, Edwards, Joanne, Glimelius, Bengt, Gogenur, Ismail, Harkin, Andrea, Hay, Jen, Iveson, Timothy, Jaeger, Emma, Kelly, Caroline, Kerr, Rachel, Maka, Noori, Morgan, Hannah, Oien, Karin, Orange, Clare, Palles, Claire, Roxburgh, Campbell, Sansom, Owen, Saunders, Mark, Tomlinson, Ian, Wagner, Sophia J., Reisenbüchler, Daniel, West, Nicholas P., Niehues, Jan Moritz, Zhu, Jiefu, Foersch, Sebastian, Veldhuizen, Gregory Patrick, Quirke, Philip, Grabsch, Heike I., van den Brandt, Piet A., Hutchins, Gordon G.A., Richman, Susan D., Yuan, Tanwei, Langer, Rupert, Jenniskens, Josien C.A., Offermans, Kelly, Mueller, Wolfram, Gray, Richard, Gruber, Stephen B., Greenson, Joel K., Rennert, Gad, Bonner, Joseph D., Schmolze, Daniel, Jonnagaddala, Jitendra, Hawkins, Nicholas J., Ward, Robyn L., Morton, Dion, Seymour, Matthew, Magill, Laura, Nowak, Marta, Hay, Jennifer, Koelzer, Viktor H., Church, David N., Matek, Christian, Geppert, Carol, Peng, Chaolong, Zhi, Cheng, Ouyang, Xiaoming, James, Jacqueline A., Loughrey, Maurice B., Salto-Tellez, Manuel, Brenner, Hermann, Hoffmeister, Michael, Truhn, Daniel, Schnabel, Julia A., Boxberg, Melanie, Peng, Tingying, and Kather, Jakob Nikolas

Published
2023
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29. Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy

Author

Kleeman, Sam O., Thakir, Tuba Mansoor, Demestichas, Breanna, Mourikis, Nicholas, Loiero, Dominik, Ferrer, Miriam, Bankier, Sean, Riazat-Kesh, Yosef J.R.A., Lee, Hassal, Chantzichristos, Dimitrios, Regan, Claire, Preall, Jonathan, Sinha, Sarthak, Rosin, Nicole, Yipp, Bryan, de Almeida, Luiz G.N., Biernaskie, Jeff, Dufour, Antoine, Tober-Lau, Pinkus, Ruusalepp, Arno, Bjorkegren, Johan L.M., Ralser, Markus, Kurth, Florian, Demichev, Vadim, Heywood, Todd, Gao, Qing, Johannsson, Gudmundur, Koelzer, Viktor H., Walker, Brian R., Meyer, Hannah V., and Janowitz, Tobias

Published
2023
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31. 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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32. Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) Challenge

Author

Lafarge, Maxime W., Koelzer, Viktor H., 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, Aubreville, Marc, editor, Zimmerer, David, editor, and Heinrich, Mattias, editor

Published
2022
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33. Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts

Author

Fremond, Sarah, Andani, Sonali, Barkey Wolf, Jurriaan, Dijkstra, Jouke, Melsbach, Sinéad, Jobsen, Jan J, Brinkhuis, Mariel, Roothaan, Suzan, Jurgenliemk-Schulz, Ina, Lutgens, Ludy C H W, Nout, Remi A, van der Steen-Banasik, Elzbieta M, de Boer, Stephanie M, Powell, Melanie E, Singh, Naveena, Mileshkin, Linda R, Mackay, Helen J, Leary, Alexandra, Nijman, Hans W, Smit, Vincent T H B M, Creutzberg, Carien L, Horeweg, Nanda, Koelzer, Viktor H, and Bosse, Tjalling

Published
2023
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34. Mitosis domain generalization in histopathology images — The MIDOG challenge

Author

Aubreville, Marc, Stathonikos, Nikolas, Bertram, Christof A., Klopfleisch, Robert, ter Hoeve, Natalie, Ciompi, Francesco, Wilm, Frauke, Marzahl, Christian, Donovan, Taryn A., Maier, Andreas, Breen, Jack, Ravikumar, Nishant, Chung, Youjin, Park, Jinah, Nateghi, Ramin, Pourakpour, Fattaneh, Fick, Rutger H.J., Ben Hadj, Saima, Jahanifar, Mostafa, Shephard, Adam, Dexl, Jakob, Wittenberg, Thomas, Kondo, Satoshi, Lafarge, Maxime W., Koelzer, Viktor H., Liang, Jingtang, Wang, Yubo, Long, Xi, Liu, Jingxin, Razavi, Salar, Khademi, April, Yang, Sen, Wang, Xiyue, Erber, Ramona, Klang, Andrea, Lipnik, Karoline, Bolfa, Pompei, Dark, Michael J., Wasinger, Gabriel, Veta, Mitko, and Breininger, Katharina

Published
2023
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36. Tertiary lymphoid structures critical for prognosis in endometrial cancer patients

Author

Horeweg, Nanda, Workel, Hagma H., Loiero, Dominik, Church, David N., Vermij, Lisa, Léon-Castillo, Alicia, Krog, Ricki T., de Boer, Stephanie M., Nout, Remi A., Powell, Melanie E., Mileshkin, Linda R., MacKay, Helen, Leary, Alexandra, Singh, Naveena, Jürgenliemk-Schulz, Ina M., Smit, Vincent T. H. B. M., Creutzberg, Carien L., Koelzer, Viktor H., Nijman, Hans W., Bosse, Tjalling, and de Bruyn, Marco

Published
2022
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37. Prediction of recurrence risk in endometrial cancer with multimodal deep learning

Author

Volinsky-Fremond, Sarah, primary, Horeweg, Nanda, additional, Andani, Sonali, additional, Barkey Wolf, Jurriaan, additional, Lafarge, Maxime W., additional, de Kroon, Cor D., additional, Ørtoft, Gitte, additional, Høgdall, Estrid, additional, Dijkstra, Jouke, additional, Jobsen, Jan J., additional, Lutgens, Ludy C. H. W., additional, Powell, Melanie E., additional, Mileshkin, Linda R., additional, Mackay, Helen, additional, Leary, Alexandra, additional, Katsaros, Dionyssios, additional, Nijman, Hans W., additional, de Boer, Stephanie M., additional, Nout, Remi A., additional, de Bruyn, Marco, additional, Church, David, additional, Smit, Vincent T. H. B. M., additional, Creutzberg, Carien L., additional, Koelzer, Viktor H., additional, and Bosse, Tjalling, additional

Published
2024
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40. B cells critical for outcome in high grade serous ovarian carcinoma.

Author

Vledder, Annegé, Paijens, Sterre T., Loiero, Dominik, Maagdenberg, Alexis, Duiker, Evelien W., Bart, Joost, Hendriks, Anne M., Jalving, Mathilde, Werner, Naomi, van Rooij, Nienke, Plat, Annechien, Wisman, G. Bea A., Yigit, Refika, Roelofsen, Thijs, Kruse, Arnold J., de Lange, Nastascha M., Koelzer, Viktor H., de Bruyn, Marco, and Nijman, Hans W.

Subjects
TUMOR-infiltrating immune cells, B cells, PROGNOSIS, T cells, ADJUVANT chemotherapy, OVARIAN cancer
Abstract

Recent work has shown evidence for the prognostic significance of tumor infiltrating B cells (B‐TIL) in high grade serous ovarian carcinoma (HGSOC), the predominant histological subtype of ovarian cancer. However, it remains unknown how the favorable prognosis associated with B‐TIL relates to the current standard treatments of primary debulking surgery (PDS) followed by chemotherapy or (neo‐)adjuvant chemotherapy (NACT) combined with interval debulking surgery. To address this, we analyzed the prognostic impact of B‐TIL in relationship to primary treatment and tumor infiltrating T cell status in a highly homogenous cohort of HGSOC patients. This analysis involved a combined approach utilizing histological data and high‐dimensional flow cytometry analysis. Our findings indicate that while HGSOC tumors pre‐treated with NACT are infiltrated with tumor‐reactive CD8+ and CD4+ TIL subsets, only B‐TIL and IgA plasma blasts confer prognostic benefit in terms of overall survival. Importantly, the prognostic value of B‐TIL and IgA plasma blasts was not restricted to patients treated with NACT, but was also evident in patients treated with PDS. Together, our data point to a critical prognostic role for B‐TIL in HGSOC patients independent of T cell status, suggesting that alternative treatment approaches focused on the activation of B cells should be explored for HGSOC. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials

Author

MS Radiotherapie, Cancer, Wakkerman, Famke C., Wu, Jiqing, Putter, Hein, Jürgenliemk-Schulz, Ina M., Jobsen, Jan J., Lutgens, Ludy C.H.W., Haverkort, Marie A.D., de Jong, Marianne A., Mens, Jan Willem M., Wortman, Bastiaan G., Nout, Remi A., Léon-Castillo, Alicia, Powell, Melanie E., Mileshkin, Linda R., Katsaros, Dionyssios, Alfieri, Joanne, Leary, Alexandra, Singh, Naveena, de Boer, Stephanie M., Nijman, Hans W., Smit, Vincent T.H.B.M., Bosse, Tjalling, Koelzer, Viktor H., Creutzberg, Carien L., Horeweg, Nanda, MS Radiotherapie, Cancer, Wakkerman, Famke C., Wu, Jiqing, Putter, Hein, Jürgenliemk-Schulz, Ina M., Jobsen, Jan J., Lutgens, Ludy C.H.W., Haverkort, Marie A.D., de Jong, Marianne A., Mens, Jan Willem M., Wortman, Bastiaan G., Nout, Remi A., Léon-Castillo, Alicia, Powell, Melanie E., Mileshkin, Linda R., Katsaros, Dionyssios, Alfieri, Joanne, Leary, Alexandra, Singh, Naveena, de Boer, Stephanie M., Nijman, Hans W., Smit, Vincent T.H.B.M., Bosse, Tjalling, Koelzer, Viktor H., Creutzberg, Carien L., and Horeweg, Nanda

Published
2024

43. Prediction of recurrence risk in endometrial cancer with multimodal deep learning

Author

Volinsky-Fremond, Sarah, Horeweg, Nanda, Andani, Sonali, Barkey Wolf, Jurriaan, Lafarge, Maxime W., de Kroon, Cor D., Ørtoft, Gitte, Høgdall, Estrid, Dijkstra, Jouke, Jobsen, Jan J., Lutgens, Ludy C.H.W., Powell, Melanie E., Mileshkin, Linda R., Mackay, Helen, Leary, Alexandra, Katsaros, Dionyssios, Nijman, Hans W., de Boer, Stephanie M., Nout, Remi A., de Bruyn, Marco, Church, David, Smit, Vincent T.H.B.M., Creutzberg, Carien L., Koelzer, Viktor H., Bosse, Tjalling, Volinsky-Fremond, Sarah, Horeweg, Nanda, Andani, Sonali, Barkey Wolf, Jurriaan, Lafarge, Maxime W., de Kroon, Cor D., Ørtoft, Gitte, Høgdall, Estrid, Dijkstra, Jouke, Jobsen, Jan J., Lutgens, Ludy C.H.W., Powell, Melanie E., Mileshkin, Linda R., Mackay, Helen, Leary, Alexandra, Katsaros, Dionyssios, Nijman, Hans W., de Boer, Stephanie M., Nout, Remi A., de Bruyn, Marco, Church, David, Smit, Vincent T.H.B.M., Creutzberg, Carien L., Koelzer, Viktor H., and Bosse, Tjalling

Abstract

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan–Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC.

Published
2024

44. Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge

Author

Pathologie Support, UMC Utrecht Holding, Cancer, Aubreville, Marc, Stathonikos, Nikolas, Donovan, Taryn A., Klopfleisch, Robert, Ammeling, Jonas, Ganz, Jonathan, Wilm, Frauke, Veta, Mitko, Jabari, Samir, Eckstein, Markus, Annuscheit, Jonas, Krumnow, Christian, Bozaba, Engin, Çayır, Sercan, Gu, Hongyan, Chen, Xiang ‘Anthony’, Jahanifar, Mostafa, Shephard, Adam, Kondo, Satoshi, Kasai, Satoshi, Kotte, Sujatha, Saipradeep, V. G., Lafarge, Maxime W., Koelzer, Viktor H., Wang, Ziyue, Zhang, Yongbing, Yang, Sen, Wang, Xiyue, Breininger, Katharina, Bertram, Christof A., Pathologie Support, UMC Utrecht Holding, Cancer, Aubreville, Marc, Stathonikos, Nikolas, Donovan, Taryn A., Klopfleisch, Robert, Ammeling, Jonas, Ganz, Jonathan, Wilm, Frauke, Veta, Mitko, Jabari, Samir, Eckstein, Markus, Annuscheit, Jonas, Krumnow, Christian, Bozaba, Engin, Çayır, Sercan, Gu, Hongyan, Chen, Xiang ‘Anthony’, Jahanifar, Mostafa, Shephard, Adam, Kondo, Satoshi, Kasai, Satoshi, Kotte, Sujatha, Saipradeep, V. G., Lafarge, Maxime W., Koelzer, Viktor H., Wang, Ziyue, Zhang, Yongbing, Yang, Sen, Wang, Xiyue, Breininger, Katharina, and Bertram, Christof A.

Published
2024

45. Swiss digital pathology recommendations: results from a Delphi process conducted by the Swiss Digital Pathology Consortium of the Swiss Society of Pathology.

Author

Janowczyk, Andrew, Zlobec, Inti, Walker, Cedric, Berezowska, Sabina, Huschauer, Viola, Tinguely, Marianne, Kupferschmid, Joel, Mallet, Thomas, Merkler, Doron, Kreutzfeldt, Mario, Gasic, Radivoje, Rau, Tilman T., Mazzucchelli, Luca, Eyberg, Isgard, Cathomas, Gieri, Mertz, Kirsten D., Koelzer, Viktor H., Soldini, Davide, Jochum, Wolfram, and Rössle, Matthias

Abstract

Integration of digital pathology (DP) into clinical diagnostic workflows is increasingly receiving attention as new hardware and software become available. To facilitate the adoption of DP, the Swiss Digital Pathology Consortium (SDiPath) organized a Delphi process to produce a series of recommendations for DP integration within Swiss clinical environments. This process saw the creation of 4 working groups, focusing on the various components of a DP system (1) scanners, quality assurance and validation of scans, (2) integration of Whole Slide Image (WSI)-scanners and DP systems into the Pathology Laboratory Information System, (3) digital workflow—compliance with general quality guidelines, and (4) image analysis (IA)/artificial intelligence (AI), with topic experts for each recruited for discussion and statement generation. The work product of the Delphi process is 83 consensus statements presented here, forming the basis for "SDiPath Recommendations for Digital Pathology". They represent an up-to-date resource for national and international hospitals, researchers, device manufacturers, algorithm developers, and all supporting fields, with the intent of providing expectations and best practices to help ensure safe and efficient DP usage. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Pathway level subtyping identifies a slow-cycling and transcriptionally lethargic biological phenotype associated with poor clinical outcomes in colon cancer independent of genetics

Author

Malla, Sudhir B, primary, Byrne, Ryan M, additional, Lafarge, Maxime, additional, Corry, Shania M, additional, Fisher, Natalie C, additional, Tsantoulis, Petros, additional, Campbell, Andrew, additional, Lannagan, Tamsin, additional, Najumudeen, Arafath K, additional, Gilroy, Kathryn, additional, Amirkhah, Raheleh, additional, Maguire, Sarah, additional, Mulholland, Eoghan, additional, Belnoue-Davis, Hayley L, additional, Grassi, Elena, additional, Viviani, Marco, additional, Rogan, Emily, additional, Redmond, Keara, additional, Sakhnevych, Svetlana, additional, McCooey, Aoife, additional, Bull, Courtney, additional, Hoey, Emily, additional, Sinevici, Nicoleta, additional, Hall, Holly, additional, Ahmaderaghi, Baharak, additional, Domingo, Enric, additional, Blake, Andrew, additional, Richman, Susan, additional, Isella, Claudio, additional, Miller, Crispin, additional, Bertotti, Andrea, additional, Trusolino, Livio, additional, Loughrey, Maurice, additional, Kerr, Emma, additional, Tejpar, Sabine, additional, Maughan, Tim, additional, Lawler, Mark, additional, Leedham, Simon J, additional, Koelzer, Viktor H, additional, Sansom, Owen J, additional, and Dunne, Philip D, additional

Published
2024
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49. SST-editing: in silico spatial transcriptomic editing at single-cell resolution.

Author

Wu, Jiqing and Koelzer, Viktor H

Subjects
*CELL imaging, *TRANSCRIPTOMES, *GENE expression, *GENOME editing, *GENERATIVE adversarial networks
Abstract

Motivation Generative Adversarial Nets (GAN) achieve impressive performance for text-guided editing of natural images. However, a comparable utility of GAN remains understudied for spatial transcriptomics (ST) technologies with matched gene expression and biomedical image data. Results We propose In   S ilico   S patial T ranscriptomic e diting that enables gene expression-guided editing of immunofluorescence images. Using cell-level spatial transcriptomics data extracted from normal and tumor tissue slides, we train the approach under the framework of GAN (Inversion). To simulate cellular state transitions, we then feed edited gene expression levels to trained models. Compared to normal cellular images (ground truth), we successfully model the transition from tumor to normal tissue samples, as measured with quantifiable and interpretable cellular features. Availability and implementation https://github.com/CTPLab/SST-editing. [ABSTRACT FROM AUTHOR]

Published
2024
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50. HECTOR: multimodal deep learning predicts recurrence risk in endometrial cancer.

Author

Fremond-Volinsky, Sarah, primary, Horeweg, Nanda, additional, Andani, Sonali, additional, Barkey Wolf, Jurriaan, additional, Lafarge, Maxime W, additional, de Kroon, Cor, additional, Ortoft, Gitte, additional, Hogdall, Estrid, additional, Dijkstra, Jouke, additional, Jobsen, Jan J, additional, Lutgens, Ludy CHW, additional, Powell, Melanie E, additional, Mileshkin, Linda R, additional, Mackay, Helen, additional, Leary, Alexandra, additional, Katsaros, Dionyssios, additional, Nijman, Hans W, additional, de Boer, Stephanie M, additional, Nout, Remi A, additional, de Bruyn, Marco, additional, Church, David, additional, Smit, Vincent THBM, additional, Creutzberg, Carien L, additional, Koelzer, Viktor H, additional, and Bosse, Tjalling, additional

Published
2023
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