@article{Hoyt2021,
 
author={Hoyt, Clifford C. },

title={Multiplex Immunofluorescence and Multispectral Imaging: Forming the Basis of a Clinical Test Platform for Immuno-Oncology},

journal={Frontiers in Molecular Biosciences},

volume={8},

year={2021},

url={https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2021.674747},

doi={10.3389/fmolb.2021.674747},

isn={2296-889X},
}

@article{ruifrok2001quantification,
  title={Quantification of histochemical staining by color deconvolution},
  author={Ruifrok, A.C. and Johnston, D.A.},
  journal={Analytical and quantitative cytology and histology},
  volume={23},
  number={4},
  pages={291--299},
  year={2001}
}

@book{huss2022artificial,
  title={Artificial Intelligence Applications in Human Pathology},
  author={Huss, R. and Grunkin, M.},
  year={2022},
  publisher={World Scientific},
  address={London}
}

@misc{visiopharm2023alignment,
  title={High-quality alignment of serial sections},
  author={{Visiopharm}},
  year={2023},
  howpublished={\url{https://visiopharm.com/visiopharm-digital-image-analysis-software-features/tissuealign/}},
  note={Last accessed 2024/09/08}
}

@inproceedings{bloom2022generating,
  title={Generating Virtual Multiplex Images from Sequential Immunohistochemistry ({IHC}) Slides using Deep Learning},
  author={Bloom, K. and Soong, D. and Higgs, B. and Arbiv, B. and Markovits, E. and Gluskin, R. and Groisman, A. and Lipinsky, M. and Gabay, Y. and Dagan, S. and Blumberg, M. and Baum, I. and Golan, G. and Zelichov, O. and Couto, S.},
  booktitle={Pathology Vision},
  year={2022},
  address={Las Vegas}
}

@misc{owkin2023staining,
  title={Developing virtual staining at {Owkin} to enrich digital pathology},
  author={{Owkin}},
  year={2020},
  howpublished={\url{https://www.owkin.com/blogs-case-studies/developing-virtual-staining-at-owkin-to-enrich-digital-pathology}},
  note={Last accessed 2024/09/08}
}

@article{dehaan2021deep,
  title={Deep learning-based transformation of H\&E stained tissues into special stains},
  author={de Haan, K. and Zhang, Y. and Zuckerman, J.E. and Liu, T. and Sisk, A.E. and Diaz, M.F. and Jen, K.Y. and Nobori, A. and Liou, S. and Zhang, S. and Riahi, R.},
  journal={Nature communications},
  volume={12},
  number={1},
  pages={4884},
  year={2021},
  publisher={Nature Publishing Group}
}

@article{rivenson2019virtual,
  title={Virtual histological staining of unlabeled tissue-autofluorescence images via deep learning},
  author={Rivenson, Y. and Wang, H. and Wei, Z. and de Haan, K. and Zhang, Y. and Wu, Y. and Günaydın, H. and Zuckerman, J.E. and Chong, T. and Sisk, A.E. and Westbrook, L.M.},
  journal={Nature Biomedical Engineering},
  volume={3},
  number={6},
  pages={466--477},
  year={2019},
  publisher={Nature Publishing Group}
}

@article{zhang2020digital,
  title={Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue},
  author={Zhang, Y. and de Haan, K. and Rivenson, Y. and Li, J. and Delis, A. and Ozcan, A.},
  journal={Light: Science \& Applications},
  volume={9},
  number={1},
  pages={78},
  year={2020},
  publisher={Nature Publishing Group}
}

@article{pradhan2021computational,
  title={Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning},
  author={Pradhan, P. and Meyer, T. and Vieth, M. and Stallmach, A. and Waldner, M. and Schmitt, M. and Popp, J. and Bocklitz, T.},
  journal={Biomedical Optics Express},
  volume={12},
  number={4},
  pages={2280--2298},
  year={2021},
  publisher={Optica Publishing Group}
}

@article{bai2022label,
  title={Label-free virtual {HER2} immunohistochemical staining of breast tissue using deep learning},
  author={Bai, B. and Wang, H. and Li, Y. and de Haan, K. and Colonnese, F. and Wan, Y. and Zuo, J. and Doan, N.B. and Zhang, X. and Zhang, Y. and Li, J.},
  journal={BME Frontiers},
  year={2022},
  publisher={BMJ Publishing Group Ltd}
}

@article{herbst2016pembrolizumab,
  title={Pembrolizumab versus docetaxel for previously treated, {PD-L1}-positive, advanced non-small-cell lung cancer ({KEYNOTE}-010): a randomised controlled trial},
  author={Herbst, R.S. and Baas, P. and Kim, D.W. and Felip, E. and Pérez-Gracia, J.L. and Han, J.Y. and Molina, J. and Kim, J.H. and Arvis, C.D. and Ahn, M.J. and Majem, M.},
  journal={The Lancet},
  volume={387},
  number={10027},
  pages={1540--1550},
  year={2016},
  publisher={Elsevier}
}

@misc{agilent2023pdl1,
  title={{PD-L1 IHC 22C3} pharm{D}x Interpretation Manual, {NSCLC}},
  author={{Agilent Technologies}},
  year={2021},
  howpublished={\url{https://www.agilent.com/cs/library/usermanuals/public/29158_pd-l1-ihc-22C3-pharmdx-nsclc-interpretation-manual.pdf}},
  note={Last accessed 2024/09/08}
}

@article{paces2022digital,
  title={A digital assay for programmed death-ligand 1 ({22C3}) quantification combined with immune cell recognition algorithms in non-small cell lung cancer},
  author={Paces, W. and Ergon, E. and Bueche, E. and Young, G.D. and Adisetiyo, V. and Luengo, C. and James, M. and Caldwell, C. and Miller, D. and Wambaugh, M. and Metcalf, G.},
  journal={Scientific Reports},
  volume={12},
  number={1},
  pages={9745},
  year={2022},
  publisher={Nature Publishing Group}
}

@inproceedings{beck2019p730,
  title={P730 An Empirical Framework for Validating Artificial Intelligence--Derived {PD-L1} Positivity Predictions Applied to Urothelial Carcinoma},
  author={Beck, A. and Glass, B. and Elliott, H. and Kerner, J.K. and Khosla, A. and Lahiri, A. and Pokkalla, H. and Wang, D. and Wapinski, I. and Lee, G. and Baxi, V.},
  booktitle={Cancer cells},
  volume={3728},
  pages={18--056},
  year={2019},
  address={Unknown}
}

@book{wang2012biomedical,
  title={Biomedical optics: principles and imaging},
  author={Wang, L.V. and Wu, H.I.},
  year={2012},
  publisher={John Wiley \& Sons},
  address={New Jersey}
}

@inproceedings{ronneberger2015unet,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, O. and Fischer, P. and Brox, T.},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III},
  pages={234--241},
  year={2015},
  organization={Springer International Publishing}
}

@article{mcnemar1947note,
  title={Note on the sampling error of the difference between correlated proportions or percentages},
  author={McNemar, Q.},
  journal={Psychometrika},
  volume={12},
  number={2},
  pages={153--157},
  year={1947},
  publisher={Springer}
}

@article{keren2019mibi,
  title={MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure},
  author={Keren, Leeat and Bosse, Marc and Thompson, Steve and Risom, Tyler and Vijayaragavan, Kausalia and McCaffrey, Erin and Marquez, Diana and Angoshtari, Roshan and Greenwald, Noah F and Fienberg, Harris and others},
  journal={Science advances},
  volume={5},
  number={10},
  pages={eaax5851},
  year={2019},
  publisher={American Association for the Advancement of Science}
}

@article{imran2024virtual,
  title={Virtual histopathology methods in medical imaging-a systematic review},
  author={Imran, Muhammad Talha and Shafi, Imran and Ahmad, Jamil and Butt, Muhammad Fasih Uddin and Villar, Santos Gracia and Villena, Eduardo Garcia and Khurshaid, Tahir and Ashraf, Imran},
  journal={BMC medical imaging},
  volume={24},
  number={1},
  pages={318},
  year={2024},
  publisher={Springer}
}

@article{robbins2023multi,
  title={Multi-institutional assessment of pathologist scoring HER2 immunohistochemistry},
  author={Robbins, Charles J and Fernandez, Aileen I and Han, Gang and Wong, Serena and Harigopal, Malini and Podoll, Mirna and Singh, Kamaljeet and Ly, Amy and Kuba, M Gabriela and Wen, Hannah and others},
  journal={Modern Pathology},
  volume={36},
  number={1},
  pages={100032},
  year={2023},
  publisher={Elsevier}
}

@inproceedings{ben2024deep,
  title={Deep-Learning Based Virtual Stain Multiplexing Immunohistochemistry Slides--a Pilot Study},
  author={Ben-David, Oded and Arbel, Elad and Aviel-Ronen, Sarit and Aidt, Frederik Heurlin and Kersch, Kristopher and Olsen, Tine Hagedorn and Rabkin, Daniela and Remer, Itay and Ben-Dor, Amir and Jacobsen, Lars and others},
  booktitle={MICCAI Workshop on Computational Pathology with Multimodal Data (COMPAYL)},
  year={2024}
}

@article{fleiss1973equivalence,
  title={The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability},
  author={Fleiss, Joseph L and Cohen, Jacob},
  journal={Educational and psychological measurement},
  volume={33},
  number={3},
  pages={613--619},
  year={1973},
  publisher={Sage Publications Sage CA: Thousand Oaks, CA}
}

@article{hirsch2010tissue,
  title={The tissue is the issue: personalized medicine for non-small cell lung cancer},
  author={Hirsch, Fred R and Wynes, Murry W and Gandara, David R and Bunn Jr, Paul A},
  journal={Clinical Cancer Research},
  volume={16},
  number={20},
  pages={4909--4911},
  year={2010},
  publisher={AACR}
}

@article{li2024correlation,
  title={Correlation of HER2 Protein Level With mRNA Level Quantified by RNAscope in Breast Cancer},
  author={Li, Xiaoxian and Lee, Ji-Hoon and Gao, Yuan and Zhang, Jilun and Bates, Katherine M and Rimm, David L and Zhang, Huina and Smith, Geoffrey Hughes and Lawson, Diane and Meisel, Jane and others},
  journal={Modern Pathology},
  volume={37},
  number={2},
  pages={100408},
  year={2024},
  publisher={Elsevier}
}

@article{baxi2022association,
  title={Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab$\pm$ipilimumab},
  author={Baxi, Vipul and Lee, George and Duan, Chunzhe and Pandya, Dimple and Cohen, Daniel N and Edwards, Robin and Chang, Han and Li, Jun and Elliott, Hunter and Pokkalla, Harsha and others},
  journal={Modern Pathology},
  volume={35},
  number={11},
  pages={1529--1539},
  year={2022},
  publisher={Nature Publishing Group US New York}
}

@article{iyer2024ai,
  title={AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases},
  author={Iyer, Janani S and Juyal, Dinkar and Le, Quang and Shanis, Zahil and Pokkalla, Harsha and Pouryahya, Maryam and Pedawi, Aryan and Stanford-Moore, S Adam and Biddle-Snead, Charles and Carrasco-Zevallos, Oscar and others},
  journal={Nature Medicine},
  volume={30},
  number={10},
  pages={2914--2923},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

@article{pulaski2024clinical,
  title={Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis},
  author={Pulaski, Hanna and Harrison, Stephen A and Mehta, Shraddha S and Sanyal, Arun J and Vitali, Marlena C and Manigat, Laryssa C and Hou, Hypatia and Madasu Christudoss, Susan P and Hoffman, Sara M and Stanford-Moore, Adam and others},
  journal={Nature Medicine},
  pages={1--8},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

@article{kapil2021VSdomain,
  title={Domain adaptation-based deep learning for automated tumor cell (TC) scoring and survival analysis on PD-L1 stained tissue images},
  author={Kapil, Ansh and Meier, Armin and Steele, Keith and Rebelatto, Marlon and Nekolla, Katharina and Haragan, Alexander and Silva, Abraham and Zuraw, Aleksandra and Barker, Craig and Scott, Marietta L and others},
  journal={IEEE Transactions on Medical Imaging},
  volume={40},
  number={9},
  pages={2513--2523},
  year={2021},
  publisher={IEEE}
}

@Conference{McKelvey2024sabcs24,
  title={Agreement Across 10 Artificial Intelligence Models in Assessing HER2 in Breast Cancer Whole Slide Images:Findings from the Friends of Cancer Research Digital PATH Project},
  author={McKelvey, Brittany and Torres-Saavedra, Pedro A. and Li, Jessica and Broeckx, Glenn and Deman, Frederik and Ali, Siraj and Andrews, Hillary and Arslan, Salim and Balasubramanian, Santhosh and Barrett, J. Carl and Caie, Peter and Chen, Ming and Cohen, Daniel and Dasgupta, Tathagata and Gallas, Brandon and Green, George and Gustavson, Mark and Hersey, Sarah and Hidalgo-Sastre, Ana and Jiwani, Shahanawaz and Jung, Wonkyung and Kulig, Kimary and Kushnarev, Vladimir and Li, Xiaoxian and Lodge, Meredith and Mancuso, Joan and Montalto, Mike and Mukhopadhyay, Satabhisa and Oberley, Matthew and Pandya, Pahini and Puig, Oscar and Richardson, Edward and Sarachakov, Alexander and Shaked, Or and Stewart, Mark and McShane, Lisa M. and Salgado, Roberto and Allen, Jeff},
  booktitle={San Antonio Breast Cancer Symposium},
  address={San Antonio, Texas},
  year={2024},
  month={Dec.~10-13},
  url={https://friendsofcancerresearch.org/wp-content/uploads/SABCS-Poster_Digital-PATH-Project-Findings.pdf}
}

@article{robert2023high,
  title={High interobserver variability among pathologists using combined positive score to evaluate PD-L1 expression in gastric, gastroesophageal junction, and esophageal adenocarcinoma},
  author={Robert, Marie E and R{\"u}schoff, Josef and Jasani, Bharat and Graham, Rondell P and Badve, Sunil S and Rodriguez-Justo, Manuel and Kodach, Liudmila L and Srivastava, Amitabh and Wang, Hanlin L and Tang, Laura H and others},
  journal={Modern Pathology},
  volume={36},
  number={5},
  pages={100154},
  year={2023},
  publisher={Elsevier}
}

@article{tsao2018pd,
  title={PD-L1 immunohistochemistry comparability study in real-life clinical samples: results of blueprint phase 2 project},
  author={Tsao, Ming Sound and Kerr, Keith M and Kockx, Mark and Beasley, Mary-Beth and Borczuk, Alain C and Botling, Johan and Bubendorf, Lukas and Chirieac, Lucian and Chen, Gang and Chou, Teh-Ying and others},
  journal={Journal of Thoracic Oncology},
  volume={13},
  number={9},
  pages={1302--1311},
  year={2018},
  publisher={Elsevier}
}

@article{han2022determination,
  title={Determination of the number of observers needed to evaluate a subjective test and its application in two PD-L1 studies},
  author={Han, Gang and Schell, Michael J and Reisenbichler, Emily S and Guo, Bohong and Rimm, David L},
  journal={Statistics in medicine},
  volume={41},
  number={8},
  pages={1361--1375},
  year={2022},
  publisher={Wiley Online Library}
}

@article{mercier2023reproducibility,
  title={Reproducibility in PD-L1 Immunohistochemistry Quantification through the Tumor Proportion Score and the Combined Positive Score: Could Dual Immunostaining Help Pathologists?},
  author={Mercier, Ana{\"\i}s and Conan-Charlet, Virginie and Quintin-Rou{\'e}, Isabelle and Doucet, Laurent and Marcorelles, Pascale and Uguen, Arnaud},
  journal={Cancers},
  volume={15},
  number={10},
  pages={2768},
  year={2023},
  publisher={MDPI}
}

@article{latonen2024virtual,
  title={Virtual staining for histology by deep learning},
  author={Latonen, Leena and Koivukoski, Sonja and Khan, Umair and Ruusuvuori, Pekka},
  journal={Trends in Biotechnology},
  year={2024},
  publisher={Elsevier}
}

@article{pati2024generativeAI,
  title={Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling},
  author={Pati, Pushpak and Karkampouna, Sofia and Bonollo, Francesco and Comp{\'e}rat, Eva and Radi{\'c}, Martina and Spahn, Martin and Martinelli, Adriano and Wartenberg, Martin and Kruithof-de Julio, Marianna and Rapsomaniki, Marianna},
  journal={Nature Machine Intelligence},
  volume={6},
  number={9},
  pages={1077--1093},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@misc {aidt2025qIHC,
	author = {Aidt, Frederik and Arbel, Elad and Remer, Itay and Ben-David, Oded and Ben-Dor, Amir and Rabkin, Daniela and Hoff, Kirsten and Salomon, Karin and Aviel-Ronen, Sarit and Nielsen, Gitte and Mollerup, Jens and Jacobsen, Lars and Tsalenko, Anya},
	title = {Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence},
	elocation-id = {2025.01.22.634230},
	year = {2025},
	doi = {10.1101/2025.01.22.634230},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2025/01/25/2025.01.22.634230},
	eprint = {https://www.biorxiv.org/content/early/2025/01/25/2025.01.22.634230.full.pdf},
	journal = {bioRxiv}
}

@article{frafjord2020cd68,
author = {Frafjord, Astri and Skarshaug, Renate and Hammarström, Clara and Stankovic, Branislava and Dorg, Linda T. and Aamodt, Henrik and Woldbæk, Per Reidar and Helland, Åslaug and Brustugun, Odd Terje and Øynebråten, Inger and Corthay, Alexandre},
title = {Antibody combinations for optimized staining of macrophages in human lung tumours},
journal = {Scandinavian Journal of Immunology},
volume = {92},
number = {1},
pages = {e12889},
keywords = {flow cytometry, human, immunohistochemistry, lung cancer, macrophages, tumour},
doi = {https://doi.org/10.1111/sji.12889},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/sji.12889},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/sji.12889},
abstract = {Abstract The analysis of tumour-associated macrophages (TAMs) has a high potential to predict cancer recurrence and response to immunotherapy. However, the heterogeneity of TAMs poses a challenge for quantitative and qualitative measurements. Here, we critically evaluated by immunohistochemistry and flow cytometry two commonly used pan-macrophage markers (CD14 and CD68) as well as some suggested markers for tumour-promoting M2 macrophages (CD163, CD204, CD206 and CD209) in human non–small cell lung cancer (NSCLC). Tumour, non-cancerous lung tissue and blood were investigated. For immunohistochemistry, CD68 was confirmed to be a useful pan-macrophage marker although careful selection of antibody was found to be critical. The widely used anti-CD68 antibody clone KP-1 stains both macrophages and neutrophils, which is problematic for TAM quantification because lung tumours contain many neutrophils. For TAM counting in tumour sections, we recommend combined labelling of CD68 with a cell membrane marker such as CD14, CD163 or CD206. In flow cytometry, the commonly used combination of CD14 and HLA-DR was found to not be optimal because some TAMs do not express CD14. Instead, combined staining of CD68 and HLA-DR is preferable to gate all TAMs. Concerning macrophage phenotypic markers, the scavenger receptor CD163 was found to be expressed by a substantial fraction (50\%-86\%) of TAMs with a large patient-to-patient variation. Approximately 50\% of TAMs were positive for CD206. Surprisingly, there was no clear overlap between CD163 and CD206 positivity, and three distinct TAM sub-populations were identified in NSCLC tumours: CD163+CD206+, CD163+CD206− and CD163−CD206−. This work should help develop macrophage-based prognostic tools for cancer.},
year = {2020}
}
@article{vanderWalt2014skimage,
  title = {scikit-image: image processing in Python},
  volume = {2},
  ISSN = {2167-8359},
  url = {http://dx.doi.org/10.7717/peerj.453},
  DOI = {10.7717/peerj.453},
  journal = {PeerJ},
  publisher = {PeerJ},
  author = {van der Walt,  Stéfan and Sch\"{o}nberger,  Johannes L. and Nunez-Iglesias,  Juan and Boulogne,  Fran\c{c}ois and Warner,  Joshua D. and Yager,  Neil and Gouillart,  Emmanuelle and Yu,  Tony},
  year = {2014},
  month = jun,
  pages = {e453}
}

@inproceedings{rublee2011orb,
  author={Rublee, Ethan and Rabaud, Vincent and Konolige, Kurt and Bradski, Gary},
  booktitle={2011 International Conference on Computer Vision}, 
  title={ORB: An efficient alternative to SIFT or SURF}, 
  year={2011},
  volume={},
  number={},
  pages={2564-2571},
  keywords={Boats},
  doi={10.1109/ICCV.2011.6126544}}
  
 @article{kroeger2016dis,
  title={Fast Optical Flow Using Dense Inverse Search},
  author={Till Kroeger and Radu Timofte and Dengxin Dai and Luc Van Gool},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.03590},
  url={https://api.semanticscholar.org/CorpusID:14824916}
}