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49 results on '"Lerousseau, Marvin"'

1. Giga-SSL: Self-Supervised Learning for Gigapixel Images

Author

Lazard, Tristan, Lerousseau, Marvin, Decencière, Etienne, and Walter, Thomas

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

Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. WSI are very large (gigapixel size) and complex (made of up to millions of cells). The current state-of-the-art (SoTA) approach to classify WSI subdivides them into tiles, encodes them by pre-trained networks and applies Multiple Instance Learning (MIL) to train for specific downstream tasks. However, annotated datasets are often small, typically a few hundred to a few thousand WSI, which may cause overfitting and underperforming models. Conversely, the number of unannotated WSI is ever increasing, with datasets of tens of thousands (soon to be millions) of images available. While it has been previously proposed to use these unannotated data to identify suitable tile representations by self-supervised learning (SSL), downstream classification tasks still require full supervision because parts of the MIL architecture is not trained during tile level SSL pre-training. Here, we propose a strategy of slide level SSL to leverage the large number of WSI without annotations to infer powerful slide representations. Applying our method to The Cancer-Genome Atlas, one of the most widely used data resources in cancer research (16 TB image data), we are able to downsize the dataset to 23 MB without any loss in predictive power: we show that a linear classifier trained on top of these embeddings maintains or improves previous SoTA performances on various benchmark WSI classification tasks. Finally, we observe that training a classifier on these representations with tiny datasets (e.g. 50 slides) improved performances over SoTA by an average of +6.3 AUC points over all downstream tasks.

Published
2022

2. MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network

Author

Estienne, Théo, Vakalopoulou, Maria, Battistella, Enzo, Henry, Theophraste, Lerousseau, Marvin, Leroy, Amaury, Paragios, Nikos, and Deutsch, Eric

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

Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made difficult by the high calculation time needed to solve the optimisation problem. Deep learning overtook this limitation by taking advantage of GPU calculation and the learning process. However, many deep learning methods do not take into account desirable properties respected by classical algorithms. In this paper, we present MICS, a novel deep learning algorithm for medical imaging registration. As registration is an ill-posed problem, we focused our algorithm on the respect of different properties: inverse consistency, symmetry and orientation conservation. We also combined our algorithm with a multi-step strategy to refine and improve the deformation grid. While many approaches applied registration to brain MRI, we explored a more challenging body localisation: abdominal CT. Finally, we evaluated our method on a dataset used during the Learn2Reg challenge, allowing a fair comparison with published methods., Comment: In submission

Published
2021

3. Exploring Deep Registration Latent Spaces

Author

Estienne, Théo, Vakalopoulou, Maria, Christodoulidis, Stergios, Battistella, Enzo, Henry, Théophraste, Lerousseau, Marvin, Leroy, Amaury, Chassagnon, Guillaume, Revel, Marie-Pierre, Paragios, Nikos, and Deutsch, Eric

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

Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretability of deep learning-based registration methods. In particular, with the appropriate model architecture and using a simple linear projection, we decompose the encoding space, generating a new basis, and we empirically show that this basis captures various decomposed anatomically aware geometrical transformations. We perform experiments using two different datasets focusing on lungs and hippocampus MRI. We show that such an approach can decompose the highly convoluted latent spaces of registration pipelines in an orthogonal space with several interesting properties. We hope that this work could shed some light on a better understanding of deep learning-based registration methods., Comment: 13 pages, 5 figures + 3 figures in supplementary materials Accepted to DART 2021 workshop

Published
2021

4. Weakly supervised pan-cancer segmentation tool

Author

Lerousseau, Marvin, Classe, Marion, Battistella, Enzo, Estienne, Théo, Henry, Théophraste, Leroy, Amaury, Sun, Roger, Vakalopoulou, Maria, Scoazec, Jean-Yves, Deutsch, Eric, and Paragios, Nikos

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

The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expert variability. To address these issues, recent approaches have leveraged categorical annotations at the slide-level, that in general suffer from robustness and generalization. In this paper, we propose a novel weakly supervised multi-instance learning approach that deciphers quantitative slide-level annotations which are fast to obtain and regularly present in clinical routine. The extreme potentials of the proposed approach are demonstrated for tumor segmentation of solid cancer subtypes. The proposed approach achieves superior performance in out-of-distribution, out-of-location, and out-of-domain testing sets.

Published
2021

5. SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification

Author

Lerousseau, Marvin, Vakalopoulou, Maria, Deutsch, Eric, and Paragios, Nikos

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

Multiple instance learning (MIL) is the preferred approach for whole slide image classification. However, most MIL approaches do not exploit the interdependencies of tiles extracted from a whole slide image, which could provide valuable cues for classification. This paper presents a novel MIL approach that exploits the spatial relationship of tiles for classifying whole slide images. To do so, a sparse map is built from tiles embeddings, and is then classified by a sparse-input CNN. It obtained state-of-the-art performance over popular MIL approaches on the classification of cancer subtype involving 10000 whole slide images. Our results suggest that the proposed approach might (i) improve the representation learning of instances and (ii) exploit the context of instance embeddings to enhance the classification performance. The code of this work is open-source at {github censored for review}.

Published
2021

6. Design and implementation of an environment for Learning to Run a Power Network (L2RPN)

Author

Lerousseau, Marvin

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract

This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics. The goal of the internship was to develop a software environment to simulate electricity transmission in a power grid and actions performed by operators to maintain this grid in security. Our environment lends itself to automate the control of the power grid with reinforcement learning agents, assisting human operators. It is amenable to organizing benchmarks, including a challenge in machine learning planned by INRIA and RTE for 2019. Our framework, built on top of open-source libraries, is available at https://github.com/MarvinLer/pypownet. In this report we present intermediary results and its usage in the context of a reinforcement learning game.

Published
2021

7. Cancer Gene Profiling through Unsupervised Discovery

Author

Battistella, Enzo, Vakalopoulou, Maria, Sun, Roger, Estienne, Théo, Lerousseau, Marvin, Nikolaev, Sergey, Andres, Emilie Alvarez, Carré, Alexandre, Niyoteka, Stéphane, Robert, Charlotte, Paragios, Nikos, and Deutsch, Eric

Subjects
Quantitative Biology - Genomics, Computer Science - Machine Learning
Abstract

Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm, that offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clustering methods and a referential gene signature used in the literature, confirm state of the art performance of our approach. In particular, our signature, that is based on 27 genes, reports at least $30$ times better mathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

Published
2021

8. Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution

Author

Henry, Theophraste, Carre, Alexandre, Lerousseau, Marvin, Estienne, Theo, Robert, Charlotte, Paragios, Nikos, and Deutsch, Eric

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

Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. Two independent ensembles of models from two different training pipelines were trained, and each produced a brain tumor segmentation map. These two labelmaps per patient were then merged, taking into account the performance of each ensemble for specific tumor subregions. Our performance on the online validation dataset with test time augmentation were as follows: Dice of 0.81, 0.91 and 0.85; Hausdorff (95%) of 20.6, 4,3, 5.7 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5mm on the final test dataset, ranking us among the top ten teams. More complicated training schemes and neural network architectures were investigated without significant performance gain at the cost of greatly increased training time. Overall, our approach yielded good and balanced performance for each tumor subregion. Our solution is open sourced at https://github.com/lescientifik/open_brats2020., Comment: BraTS 2020 proceedings (LNCS) paper

Published
2020

9. Deep learning based registration using spatial gradients and noisy segmentation labels

Author

Estienne, Théo, Vakalopoulou, Maria, Battistella, Enzo, Carré, Alexandre, Henry, Théophraste, Lerousseau, Marvin, Robert, Charlotte, Paragios, Nikos, and Deutsch, Eric

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

Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of $0.64$ for task 3 and $0.85$ for task 4 on the test sets, taking third place on the challenge. Our code and models are publicly available at https://github.com/TheoEst/abdominal_registration and \https://github.com/TheoEst/hippocampus_registration., Comment: 6 pages, 3 figures. Updated version after review modifications. Published to Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020. Lecture Notes in Computer Science, vol 12587

Published
2020
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10. Multimodal brain tumor classification

Author

Lerousseau, Marvin, Deutsh, Eric, and Paragios, Nikos

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

Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathological slides, radiology images should yield additional knowledge towards the efficacy of cancer diagnostics. This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. In particular, our solution comprises a powerful, generic and modular architecture for whole slide image classification. Experiments are prospectively conducted on the 2020 Computational Precision Medicine challenge, in a 3-classes unbalanced classification task. We report cross-validation (resp. validation) balanced-accuracy, kappa and f1 of 0.913, 0.897 and 0.951 (resp. 0.91, 0.90 and 0.94). For research purposes, including reproducibility and direct performance comparisons, our finale submitted models are usable off-the-shelf in a Docker image available at https://hub.docker.com/repository/docker/marvinler/cpm_2020_marvinler.

Published
2020

11. Weakly supervised multiple instance learning histopathological tumor segmentation

Author

Lerousseau, Marvin, Vakalopoulou, Maria, Classe, Marion, Adam, Julien, Battistella, Enzo, Carré, Alexandre, Estienne, Théo, Henry, Théophraste, Deutsch, Eric, and Paragios, Nikos

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

Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder clinical translation since histology suffers from significant variations between cancer phenotypes. In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems. In particular, we exploit a multiple instance learning scheme for training models. The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset. Promising results when compared with experts' annotations demonstrate the potentials of the presented approach. The complete framework, including $6481$ generated tumor maps and data processing, is available at https://github.com/marvinler/tcga_segmentation., Comment: Accepted MICCAI 2020; added code + results url; 10 pages, 3 figures

Published
2020

12. Learning to run a power network challenge for training topology controllers

Author

Marot, Antoine, Donnot, Benjamin, Romero, Camilo, Veyrin-Forrer, Luca, Lerousseau, Marvin, Donon, Balthazar, and Guyon, Isabelle

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However, a less costly and potentially more flexible option would be grid topology reconfiguration, as already partially exploited by Coreso (European RSC) and RTE (French TSO) operations. Beyond previous work on branch switching, bus reconfigurations are a broader class of action and could provide some substantial benefits to route electricity and optimize the grid capacity to keep it within safety margins. Because of its non-linear and combinatorial nature, no existing optimal power flow solver can yet tackle this problem. We here propose a new framework to learn topology controllers through imitation and reinforcement learning. We present the design and the results of the first "Learning to Run a Power Network" challenge released with this framework. We finally develop a method providing performance upper-bounds (oracle), which highlights remaining unsolved challenges and suggests future directions of improvement.

Published
2019

13. End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation

Author

Leroy, Amaury, Lerousseau, Marvin, Henry, Théophraste, Cafaro, Alexandre, Paragios, Nikos, Grégoire, Vincent, Deutsch, Eric, 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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14. Unsupervised Nuclei Segmentation Using Spatial Organization Priors

Author

Le Bescond, Loïc, Lerousseau, Marvin, Garberis, Ingrid, André, Fabrice, Christodoulidis, Stergios, Vakalopoulou, Maria, Talbot, Hugues, 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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15. Multimodal Brain Tumor Classification

Author

Lerousseau, Marvin, Deutsch, Eric, Paragios, Nikos, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
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16. Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution

Author

Henry, Théophraste, Carré, Alexandre, Lerousseau, Marvin, Estienne, Théo, Robert, Charlotte, Paragios, Nikos, Deutsch, Eric, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2021
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17. Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Author

Estienne, Théo, Vakalopoulou, Maria, Battistella, Enzo, Carré, Alexandre, Henry, Théophraste, Lerousseau, Marvin, Robert, Charlotte, Paragios, Nikos, Deutsch, Eric, 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, Shusharina, Nadya, editor, Heinrich, Mattias P., editor, and Huang, Ruobing, editor

Published
2021
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18. Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Author

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

Published
2024

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

Author

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

Abstract

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer.

Published
2024

21. Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning

Author

Alvarez Andres, Emilie, Fidon, Lucas, Vakalopoulou, Maria, Lerousseau, Marvin, Carré, Alexandre, Sun, Roger, Klausner, Guillaume, Ammari, Samy, Benzazon, Nathan, Reuzé, Sylvain, Estienne, Théo, Niyoteka, Stéphane, Battistella, Enzo, Rouyar, Angéla, Noël, Georges, Beaudre, Anne, Dhermain, Frédéric, Deutsch, Eric, Paragios, Nikos, and Robert, Charlotte

Published
2020
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22. U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets

Author

Estienne, Théo, Vakalopoulou, Maria, Christodoulidis, Stergios, Battistela, Enzo, Lerousseau, Marvin, Carre, Alexandre, Klausner, Guillaume, Sun, Roger, Robert, Charlotte, Mougiakakou, Stavroula, Paragios, Nikos, Deutsch, Eric, 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, Shen, Dinggang, editor, Liu, Tianming, editor, Peters, Terry M., editor, Staib, Lawrence H., editor, Essert, Caroline, editor, Zhou, Sean, editor, Yap, Pew-Thian, editor, and Khan, Ali, editor

Published
2019
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23. Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature

Author

Battistella, Enzo, Vakalopoulou, Maria, Estienne, Théo, Lerousseau, Marvin, Sun, Roger, Robert, Charlotte, Paragios, Nikos, Deutsch, Eric, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas, Fernando, editor, and Ortuño, Francisco, editor

Published
2019
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24. Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer

Author

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

Published
2024
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27. Exploring Deep Registration Latent Spaces

Author

Estienne, Théo, primary, Vakalopoulou, Maria, additional, Christodoulidis, Stergios, additional, Battistella, Enzo, additional, Henry, Théophraste, additional, Lerousseau, Marvin, additional, Leroy, Amaury, additional, Chassagnon, Guillaume, additional, Revel, Marie-Pierre, additional, Paragios, Nikos, additional, and Deutsch, Eric, additional

Published
2021
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29. Weakly Supervised Pan-Cancer Segmentation Tool

Author

Lerousseau, Marvin, primary, Classe, Marion, additional, Battistella, Enzo, additional, Estienne, Théo, additional, Henry, Théophraste, additional, Leroy, Amaury, additional, Sun, Roger, additional, Vakalopoulou, Maria, additional, Scoazec, Jean-Yves, additional, Deutsch, Eric, additional, and Paragios, Nikos, additional

Published
2021
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30. Spatial analyses of immune cell infiltration in cancer: current methods and future directions. A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer

Author

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

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

Author

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

Published
2023
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34. Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics

Author

Carré, Alexandre, Klausner, Guillaume, Edjlali, Myriam, Lerousseau, Marvin, Briend-Diop, Jade, Sun, Roger, Ammari, Samy, Reuzé, Sylvain, Alvarez Andres, Emilie, Estienne, Théo, Niyoteka, Stéphane, Battistella, Enzo, Vakalopoulou, Maria, Dhermain, Frédéric, Paragios, Nikos, Deutsch, Eric, Oppenheim, Catherine, Pallud, Johan, and Robert, Charlotte

Published
2020
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36. Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer:A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Author

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

Abstract

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer.

Published
2023

37. Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Author

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

Published
2023

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

Author

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

Published
2023

39. Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer:a report of the international immuno-oncology biomarker working group

Author

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

Abstract

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer.

Published
2023

40. Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

Author

Sun, Roger, primary, Lerousseau, Marvin, additional, Briend-Diop, Jade, additional, Routier, Emilie, additional, Roy, Severine, additional, Henry, Théophraste, additional, Ka, Kanta, additional, Jiang, Rui, additional, Temar, Nawal, additional, Carré, Alexandre, additional, Laville, Adrien, additional, Hamaoui, Anthony, additional, Laurent, Pierre-Antoine, additional, Rouyar, Angela, additional, Robert, Charlotte, additional, Robert, Caroline, additional, and Deutsch, Eric, additional

Published
2022
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42. MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network

Author

Estienne, Th��o, Vakalopoulou, Maria, Battistella, Enzo, Henry, Theophraste, Lerousseau, Marvin, Leroy, Amaury, Paragios, Nikos, and Deutsch, Eric

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)
Abstract

Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made difficult by the high calculation time needed to solve the optimisation problem. Deep learning overtook this limitation by taking advantage of GPU calculation and the learning process. However, many deep learning methods do not take into account desirable properties respected by classical algorithms. In this paper, we present MICS, a novel deep learning algorithm for medical imaging registration. As registration is an ill-posed problem, we focused our algorithm on the respect of different properties: inverse consistency, symmetry and orientation conservation. We also combined our algorithm with a multi-step strategy to refine and improve the deformation grid. While many approaches applied registration to brain MRI, we explored a more challenging body localisation: abdominal CT. Finally, we evaluated our method on a dataset used during the Learn2Reg challenge, allowing a fair comparison with published methods., In submission

Published
2021

43. COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

Author

Battistella, Enzo, Vakalopoulou, Maria, Sun, Roger, Estienne, Théo, Lerousseau, Marvin, Nikolaev, Sergey, Andres, Émilie, Carré, Alexandre, Niyoteka, Stéphane, Robert, Charlotte, Paragios, Nikos, Alvarez Andres, Emilie, Deutsch, Eric, Radiothérapie Moléculaire et Innovation Thérapeutique (RaMo-IT), Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay, Institut Gustave Roussy (IGR), CentraleSupélec, OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, Département d'anesthésie [Gustave Roussy], Hopital Saint-Louis [AP-HP] (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Physique médicale, Département de radiothérapie [Gustave Roussy], Institut Gustave Roussy (IGR)-Institut Gustave Roussy (IGR), Département d’Innovation Thérapeutique et essais précoces [Gustave Roussy] (DITEP), and Battistella, Enzo

Subjects
Computer science, Applied Mathematics, Genomics, Computational biology, Gene signature, Precision medicine, Bottleneck, Signature (logic), Clustering, Identification (information), Multi-tumor association, Genetics, Data analysis, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Cluster analysis, Predictive Signature, Biomarkers, Biotechnology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional centerbased unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitative metric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clustering methods and a referential gene signature used in the literature, confirm state of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times better mathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

Published
2021

44. COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

Author

Battistella, Enzo, primary, Vakalopoulou, Maria, additional, Sun, Roger, additional, Estienne, Theo, additional, Lerousseau, Marvin, additional, Nikolaev, Sergey, additional, Alvarez Andres, Emilie, additional, Carre, Alexandre, additional, Niyoteka, Stephane, additional, Robert, Charlotte, additional, Paragios, Nikos, additional, and Deutsch, Eric, additional

Published
2022
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46. Deep learning based registration using spatial gradients and noisy segmentation labels

Author

Estienne, Th��o, Vakalopoulou, Maria, Battistella, Enzo, Carr��, Alexandre, Henry, Th��ophraste, Lerousseau, Marvin, Robert, Charlotte, Paragios, Nikos, and Deutsch, Eric

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Machine Learning (cs.LG)
Abstract

Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of $0.64$ for task 3 and $0.85$ for task 4 on the test sets, taking third place on the challenge. Our code and models are publicly available at https://github.com/TheoEst/abdominal_registration and \https://github.com/TheoEst/hippocampus_registration., 6 pages, 3 figures. Updated version after review modifications. Published to Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020. Lecture Notes in Computer Science, vol 12587

Published
2020
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47. Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

Author

Estienne, Théo, primary, Lerousseau, Marvin, additional, Vakalopoulou, Maria, additional, Alvarez Andres, Emilie, additional, Battistella, Enzo, additional, Carré, Alexandre, additional, Chandra, Siddhartha, additional, Christodoulidis, Stergios, additional, Sahasrabudhe, Mihir, additional, Sun, Roger, additional, Robert, Charlotte, additional, Talbot, Hugues, additional, Paragios, Nikos, additional, and Deutsch, Eric, additional

Published
2020
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48. Models including pathological and radiomic features vs clinical models in predicting outcome of patients with metastatic non-small cell lung cancer treated with immunotherapy.

Author

Captier, Nicolas, Lerousseau, Marvin, Orlhac, Fanny, Hovhannisyan, Narinée, Luporsi, Marie, Lagha, Sarah, Tedesco, Anne-Sophie, Salamoun Feghali, Paulette, Lonjou, Christine, Ramtohul, Toulsie, Beaulaton, Clément, Brisse, Hervé, Vincent-Salomon, Anne, Walter, Thomas, Buvat, Irène, Girard, Nicolas, and Barillot, Emmanuel

Published
2023
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49. Trem2-expressing multinucleated giant macrophages are a biomarker of good prognosis in head and neck squamous cell carcinoma.

Author

Gessain G, Anzali AA, Lerousseau M, Mulder K, Bied M, Auperin A, Stockholm D, Signolle N, Sassi F, Marques Da Costa ME, Marchais A, Sayadi A, Weidner D, Uderhardt S, Blampey Q, Nakkireddy SR, Broutin S, Dutertre CA, Busson P, Walter T, Marhic A, Moya-Plana A, Guerlain J, Breuskin I, Casiraghi O, Gorphe P, Classe M, Scoazec JY, Bleriot C, and Ginhoux F

Abstract

Patients with head and neck squamous cell carcinomas (HNSCC) often have poor outcomes due to suboptimal risk-management and treatment strategies; yet integrating novel prognostic biomarkers into clinical practice is challenging. Here, we report the presence of multinucleated giant cells (MGC) - a type of macrophages - in tumors from patients with HNSCC, which are associated with a favorable prognosis in treatment-naive and preoperative-chemotherapy-treated patients. Importantly, MGC density increased in tumors following preoperative therapy, suggesting a role of these cells in the anti-tumoral response. To enable clinical translation of MGC density as a prognostic marker, we developed a deep-learning model to automate its quantification on routinely stained pathological whole slide images. Finally, we used spatial transcriptomic and proteomic approaches to describe the MGC-related tumor microenvironment and observed an increase in central memory CD4 T cells. We defined an MGC-specific signature resembling to TREM2-expressing mononuclear tumor associated macrophages, which co-localized in keratin tumor niches.

Published
2024
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