The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
QuPath: Open source software for digital pathology image analysis
Open accessPublished version of a preprint
Abstract
QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath's flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures


Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“On the software side, frameworks such as TensorFlow and PyTorch support AI model development, while tools like QuPath enable image annotation and analysis [44].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [44]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Emerging solutions include standardized open-source tools (e.g., QuPath) [44], collaborative multi-institutional annotation efforts, and AI-assisted annotation workflows that reduce manual burden [45].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [44]
The cited source says what the claim attributes to it.
In the citing paper
“The microglial ultrastructural analyses was performed using QuPath (v.0.4.4) [71] on images blinded to both animal and group, following an adaptation from our previous work [56, 64].”
From Sex chromosomes and sex hormones differently shape microglial properties during normal physiological conditions in the adult mouse hippocampus (2025) · cites this paper as [71]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
NIH Image to ImageJ: 25 years of image analysis
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012) · Nature Methods · doi:10.1038/nmeth.2089 · Cited by 64,638
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay‐Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,320
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Susstrunk, S. E. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.120 · Cited by 9,451
Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome
Galon, J., Costes, A., Sanchez-Cabo, F., Kirilovsky, A., Mlecnik, B., Lagorce-Pagès, C., Tosolini, M., Camus, M., Berger, A., Wind, P., et al. (2006) · Science · doi:10.1126/science.1129139 · Cited by 6,516
Pembrolizumab for the Treatment of Non–Small-Cell Lung Cancer
Garon, E. B., Rizvi, N. A., Hui, R., Leighl, N., Balmanoukian, A. S., Eder, J. P., Patnaik, A., Aggarwal, C., Gubens, M., Horn, L., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1501824 · Cited by 6,198
Quantification of histochemical staining by color deconvolution.
Ruifrok, A. C. C., & Johnston, D. A. (2001) · PubMed · Cited by 2,029
Cited by
Sorted by citations
Items 501–600 of 1,045
Timing and location dictate monocyte fate and their transition to tumor-associated macrophages
Dunsmore, G., Guo, W., Li, Z., Bejarano, D. A., Pai, R., Yang, K. L., Kwok, I., Tan, L., Ng, M. S. F., de la Calle‐Fabregat, C., et al. (2024) · Science Immunology · doi:10.1126/sciimmunol.adk3981 · Cited by 74
Chhabra, Y., Fane, M. E., Pramod, S., Hüser, L., Zabransky, D. J., Wang, V., Dixit, A., Zhao, R., Kumah, E., Brezka, M. L., et al. (2024) · Cell · doi:10.1016/j.cell.2024.08.013 · Cited by 74
Qiu, X., Zhou, T., Li, S., Wu, J., Tang, J., Ma, G., Yang, S., Hu, J., Wang, K., Shen, S., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00824-y · Cited by 74
Lee, B., Kwon, J.-T., Jeong, Y., Caris, H., Oh, D., Feng, M., Mejia, I. D., Zhang, X., Ishikawa, T., Watson, B. R., et al. (2025) · Cell · doi:10.1016/j.cell.2025.03.005 · Cited by 74
Open access image repositories: high-quality data to enable machine learning research
Prior, F., Almeida, J. S., Kathiravelu, P., Kurç, T., Smith, K., FitzGerald, T. J., & Saltz, J. H. (2019) · Clinical Radiology · doi:10.1016/j.crad.2019.04.002 · Cited by 75
The Digital Brain Tumour Atlas, an open histopathology resource
Roetzer-Pejrimovsky, T., Moser, A., Atli, B., Vogel, C., Mercea, P. A., Prihoda, R., Gelpí, E., Haberler, C., Höftberger, R., Hainfellner, J. A., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01157-0 · Cited by 74
Świderska-Chadaj, Ż., de Bel, T., Blanchet, L., Baidoshvili, A., Vossen, D. L. J., van der Laak, J. A. W. M., & Litjens, G. J. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-71420-0 · Cited by 73
Allogeneic double-negative CAR-T cells inhibit tumor growth without off-tumor toxicities
Vasic, D., Lee, J. B., Leung, Y. K., Khatri, I. A., Na, Y., Abate‐Daga, D., & Li, Z. (2022) · Science Immunology · doi:10.1126/sciimmunol.abl3642 · Cited by 73
A spatial expression atlas of the adult human proximal small intestine
Harnik, Y., Yakubovsky, O., Hoefflin, R., Novoselsky, R., Halpern, K. B., Barkai, T., Kohanim, Y. K., Egozi, A., Golani, O., Addadi, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07793-3 · Cited by 76
CRISPR–Cas9 screens reveal regulators of ageing in neural stem cells
Ruetz, T. J., Pogson, A. N., Kashiwagi, C. M., Gagnon, S. D., Morton, B., Sun, E., Na, J., Yeo, R. W., Leeman, D. S., Morgens, D. W., et al. (2024) · Nature · doi:10.1038/s41586-024-07972-2 · Cited by 75
The subfornical organ is a nucleus for gut-derived T cells that regulate behaviour
Yoshida, T. M., Nguyen, M. T., Zhang, L., Lu, B. Y., Zhu, B., Murray, K. N., Mineur, Y. S., Zhang, C., Xu, D., Lin, E., et al. (2025) · Nature · doi:10.1038/s41586-025-09050-7 · Cited by 73
Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem
Chen, J. G., Chávez, J., O’Brien, M., Xu, J., Ruiz, E. C., Wang, W., Amin, I., Sheridan, J. P., Shin, S. C., Hasyagar, S. V., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02817-w · Cited by 73
Starkl, P. M., Watzenboeck, M. L., Popov, L. M., Zahalka, S., Hladik, A., Lakovits, K., Radhouani, M., Haschemi, A., Marichal, T., Reber, L. L., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.08.002 · Cited by 72
de Vries, G., Rosas-Plaza, X., Meersma, G. J., Leeuwenburgh, V. C., Kok, K., Suurmeijer, A. J. H., van Vugt, M. A. T. M., Gietema, J. A., & de Jong, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-75518-3 · Cited by 72
McCowan, J., Fercoq, F., Kirkwood, P. M., T’Jonck, W., Hegarty, L. M., Mawer, C. M., Cunningham, R., Mirchandani, A. S., Hoy, A. M., Humphries, D. C., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abj2132 · Cited by 72
Zindel, J., Mittner, J., Bayer, J., April-Monn, S., Kohler, A., Nusse, Y. M., Dosch, M., Büchi, I., Sánchez‐Taltavull, D., Dawson, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27612-x · Cited by 72
Yang, K., Zhang, W., Zhong, L., Xiao, Y., Sahoo, S., Fassan, M., Zeng, K., Magee, P., Garofalo, M., & Shi, L. (2023) · Cell Death and Differentiation · doi:10.1038/s41418-023-01160-x · Cited by 72
Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics
Haggenmüller, S., Schmitt, M., Krieghoff‐Henning, E., Hekler, A., Maron, R. C., Wies, C., Utikal, J. S., Meier, F. E., Hobelsberger, S., Gellrich, F. F., et al. (2024) · JAMA Dermatology · doi:10.1001/jamadermatol.2023.5550 · Cited by 72
Bastos, J., O’Brien, C. A., Vara‐Pérez, M., Mampay, M., van Olst, L., Barry-Carroll, L., Kancheva, D., Leduc, S., Lievens, A. L., Ali, L., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.04.006 · Cited by 74
Astrocyte heterogeneity reveals region-specific astrogenesis in the white matter
Bocchi, R., Thorwirth, M., Simon-Ebert, T., Koupourtidou, C., Clavreul, S., Kolf, K., Della Vecchia, P., Bottes, S., Jessberger, S., Zhou, J., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01878-6 · Cited by 72
Acha-Sagredo, A., Andrei, P., Clayton, K., Taggart, E., Antoniotti, C., Woodman, C. A., Afrache, H., Fourny, C., Armero, M., Moinudeen, H. K., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2024.12.008 · Cited by 71
Effect of renal tubule-specific knockdown of the Na+/H+exchanger NHE3 in Akita diabetic mice
Onishi, A., Fu, Y., Darshi, M., Crespo-Masip, M., Huang, W., Song, P., Patel, R., Kim, Y. C., Nespoux, J., Freeman, B., et al. (2019) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00497.2018 · Cited by 71
Millar, E. K. A., Browne, L. H., Beretov, J., Lee, K., Lynch, J., Swarbrick, A., & Graham, P. (2020) · Cancers · doi:10.3390/cancers12123749 · Cited by 71
Wessels, F., Schmitt, M., Krieghoff‐Henning, E., Jutzi, T. B., Worst, T. S., Waldbillig, F., Neuberger, M., Maron, R. C., Steeg, M., Gaiser, T., et al. (2021) · British Journal of Urology · doi:10.1111/bju.15386 · Cited by 71
Tanti, A., Belliveau, C., Nagy, C., Maitra, M., Denux, F., Perlman, K., Chen, F. Y., Mpai, R., Canonne, C., Théberge, S., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01372-y · Cited by 71
Pacia, C. P., Yuan, J., Yue, Y., Xu, L., Nazeri, A., Desai, R. R., Gach, H. M., Wang, X., Talcott, M., Chaudhuri, A. A., et al. (2021) · Theranostics · doi:10.7150/thno.65597 · Cited by 71
Noto, J. M., Piazuelo, M. B., Shah, S. C., Romero–Gallo, J., Hart, J. L., Di, C., Carmichael, J. D., Delgado, A. G., Halvorson, A. E., Greevy, R. A., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci147822 · Cited by 73
Mondol, R. K., Millar, E. K. A., Graham, P., Browne, L. H., Sowmya, A., & Meijering, E. H. W. (2023) · Cancers · doi:10.3390/cancers15092569 · Cited by 71
Lei, P., Pereira, E. R., Andersson, P., Amoozgar, Z., van Wijnbergen, J. W., O’Melia, M. J., Zhou, H., Chatterjee, S., Ho, W. W., Posada, J. M., et al. (2023) · The Journal of Experimental Medicine · doi:10.1084/jem.20221847 · Cited by 71
Hou, Y., Yang, K., Wang, L., Wang, J., Huang, X., Piffkó, A., Luo, S. Z., Yu, X., Rao, E., Martinez, C. N., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-23-3206 · Cited by 71
Histopathological biomarkers for predicting the tumour accumulation of nanomedicines
May, J., Moss, J. I., Mueller, F., Golombek, S. K., Biancacci, I., Rizzo, L. Y., Elshafei, A. S., Gremse, F., Pola, R., Pechar, M., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01197-4 · Cited by 71
Pantanowitz, L., Pearce, T. M., Abukhiran, I. M., Hanna, M. G., Wheeler, S., Soong, T. R., Tafti, A. P., Pantanowitz, J., Lu, M. Y., Mahmood, F., et al. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100680 · Cited by 71
A versatile toolbox for semi-automatic cell-by-cell object-based colocalization analysis
Lunde, A., & Glover, J. C. (2020) · Scientific Reports · doi:10.1038/s41598-020-75835-7 · Cited by 70
Nuclear FGFR1 Regulates Gene Transcription and Promotes Antiestrogen Resistance in ER+ Breast Cancer
Servetto, A., Kollipara, R. K., Formisano, L., Lin, C.-C. A., Lee, K., Sudhan, D. R., Ericsson, P. I. G., Chatterjee, S., Zotano, Á. G., Mendiratta, S., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-3905 · Cited by 70
Effect of particle size on in vivo performances of long-acting injectable drug suspension
Ho, M. J., Jeong, M. Y., Jeong, H. T., Kim, M., Park, H. J., Kim, D. Y., Lee, H. C., Song, W. H., Kim, C. H., Lee, C. H., et al. (2021) · Journal of Controlled Release · doi:10.1016/j.jconrel.2021.12.011 · Cited by 70
Rivas, E. I., Linares, J., Zwick, M., Gómez‐Llonín, A., Guiu, M., Labernadie, A., Badia-Ramentol, J., Lladó, A., Bardia, L., Pérez-Núñez, I., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32782-3 · Cited by 70
de Ruyter, F. J. H., Morrema, T. H. J., den Haan, J., Bank, N. B., Twisk, J. W. R., de Boer, J. F., Scheltens, P., Boon, B. D. C., Thal, D. R., Rozemüller, A. J. M., et al. (2022) · Acta Neuropathologica · doi:10.1007/s00401-022-02525-1 · Cited by 70
Seeker, L. A., Bestard-Cuche, N., Jäkel, S., Kazakou, N.-L., Bøstrand, S. M. K., Wagstaff, L. J., Cholewa-Waclaw, J., Kilpatrick, A. M., van Bruggen, D., Kabbe, M., et al. (2023) · Acta Neuropathologica Communications · doi:10.1186/s40478-023-01568-z · Cited by 72
López‐Janeiro, Á., Padilla-Ansala, C., de Andrea, C. E., Hardisson, D., & Melero, I. (2020) · Modern Pathology · doi:10.1038/s41379-020-0534-z · Cited by 70
Yamamoto, A., Huang, Y., Krajina, B. A., McBirney, M., Doak, A. E., Qu, S., Wang, C. L., Haffner, M. C., & Cheung, K. J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2214888120 · Cited by 69
Aktürk, G., Sweeney, R. E., Remark, R., Mérad, M., & Gnjatic, S. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9773-2_23 · Cited by 69
Heath, O. M., Berlato, C., Maniati, E., Lakhani, A., Pegrum, C., Kotantaki, P., Elorbany, S., Böhm, S., Barry, S. T., Annibaldi, A., et al. (2021) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-20-0968 · Cited by 69
Lee, K., Lin, C.-C. A., Servetto, A., Bae, J., Kandagatla, V., Ye, D., Kim, G. M., Sudhan, D. R., Mendiratta, S., Ericsson, P. I. G., et al. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-21-0826 · Cited by 69
Zhang, Q., Yao, B., Long, X., Chen, Z., He, M. Y., Wu, Y., Qiao, N., Ma, Z., Ye, Z., Zhang, Y., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.100934 · Cited by 69
Garcia-Diaz, C., Pöysti, A., Mereu, E., Clements, M., Brooks, L. J., Gálvez‐Cancino, F., Castillo, S. P., Tang, W., Beattie, G., Courtot, L., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112472 · Cited by 71
Umemura, Y., Orringer, D. A., Junck, L., Varela, M. L., West, M. E. J., Faisal, S. M., Comba, A., Heth, J., Sagher, O., Leung, D., et al. (2023) · The Lancet Oncology · doi:10.1016/s1470-2045(23)00347-9 · Cited by 69
Ebrahim, N., AlSaihati, H., Alali, Z., Aleniz, F. Q., Mahmoud, S. Y., Badr, O. A. M., Dessouky, A. A., Mostafa, O., Hussien, N. I., Farid, A. S., et al. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.116836 · Cited by 69
RetractedChowdhury, C. S., Kinsella, R. L., McNehlan, M. E., Naik, S. K., Lane, D. S., Talukdar, P., Smirnov, A., Dubey, N., Rankin, A. N., McKee, S. R., et al. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.11.008 · Cited by 72
Ge, Y., Weygant, N., Qu, D., May, R. J., Berry, W. L., Yao, J., Chandrakesan, P., Zheng, W., Zhao, L., Zhao, K. L., et al. (2018) · International Journal of Cancer · doi:10.1002/ijc.31400 · Cited by 69
ONCR-177, an Oncolytic HSV-1 Designed to Potently Activate Systemic Antitumor Immunity
Haines, B. B., Denslow, A., Grzesik, P., Lee, J. S., Farkaly, T., Hewett, J., Wambua, D., Kong, L., Behera, P., Jacques, J., et al. (2020) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-20-0609 · Cited by 69
A Comparative Analysis of Imaging-Based Spatial Transcriptomics PlatformsPreprint
Cook, D. P., Jensen, K. B., Wise, K., Roach, M. J., Dezem, F. S., Ryan, N. K., Zamojski, M., Vlachos, I. S., Knott, S., Butler, L. M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.13.571385 · Cited by 69
A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification
Conceição, T., Braga, C., Rosado, L., & Vasconcelos, M. J. M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20205114 · Cited by 68
Li, B., Keikhosravi, A., Loeffler, A. G., & Eliceiri, K. W. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101938 · Cited by 68
Tulotta, C., Lefley, D. V., Moore, C., Amariutei, A. E., Spicer-Hadlington, A. R., Quayle, L. A., Hughes, R., Ahmed, K., Cookson, V. J., Evans, C. A., et al. (2021) · npj Breast Cancer · doi:10.1038/s41523-021-00305-w · Cited by 68
Chiaruttini, N., Burri, O., Haub, P., Guiet, R., Sordet‐Dessimoz, J., & Seitz, A. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.780026 · Cited by 68
BAF Complex Maintains Glioma Stem Cells in Pediatric H3K27M Glioma
Panditharatna, E., Marques, J. G., Wang, T., Trissal, M. C., Liu, I., Jiang, L., Beck, A., Groves, A. P., Dharia, N. V., Li, D., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1491 · Cited by 68
Boucher, Y., Posada, J. M., Subudhi, S., Rosario, S. R., Gu, L., Kumar, A. S., Kumra, H., Mino-Kenudson, M. A., Talele, N. P., Duda, D. G., et al. (2023) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-1630 · Cited by 68
Ragazzini, R., Boeing, S., Zanieri, L., Green, M. J., D’Agostino, G. A., Bartolović, K., Agua‐Doce, A., Greco, M., Watson, S. A., Batsivari, A., et al. (2023) · Developmental Cell · doi:10.1016/j.devcel.2023.08.017 · Cited by 68
Licaj, M., Mhaidly, R., Kieffer, Y., Croizer, H., Bonneau, C., Meng, A., Djerroudi, L., Mujangi-Ebeka, K., Hocine, H. R., Bourachot, B., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45595-3 · Cited by 71
p53 promotes revival stem cells in the regenerating intestine after severe radiation injury
Morral, C., Ayyaz, A., Kuo, H.-C., Fink, M., Verginadis, I. I., Daniel, A. R., Burner, D., Driver, L. M., Satow, S., Hasapis, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47124-8 · Cited by 68
Heterogeneous pericoerulear neurons tune arousal and exploratory behaviours
Luskin, A. T., Li, L., Fu, X., Martin, M. M., Barcomb, K., Girven, K. S., Blackburn, T., Wells, B. A., Thai, S., Li, E. M., et al. (2025) · Nature · doi:10.1038/s41586-025-08952-w · Cited by 68
Evolution and impact of high content imaging.
Way, G. P., Sailem, H., Shave, S., Kasprowicz, R., & Carragher, N. O. (2023) · SLAS Discovery · doi:10.1016/j.slasd.2023.08.009 · Cited by 67
A framework for ultra-low-input spatial tissue proteomics
Makhmut, A., Qin, D., Fritzsche, S., Nimo, J., König, J., & Coscia, F. (2023) · Cell Systems · doi:10.1016/j.cels.2023.10.003 · Cited by 67
Bringing Open Data to Whole Slide Imaging
Besson, S., Leigh, R., Linkert, M., Allan, C., Burel, J., Carroll, M., Gault, D., Gozim, R., Li, S., Lindner, D., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23937-4_1 · Cited by 67
Brück, O. E., Lallukka-Brück, S. E., Hohtari, H. R., Ianevski, A., Ebeling, F. T., Kovanen, P. E., Kytölä, S. I., Aittokallio, T. A., Ramos, P. M., Porkka, K., et al. (2021) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-20-0162 · Cited by 67
Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment
van Dam, S., Baars, M. J. D., & Vercoulen, Y. (2022) · Cancers · doi:10.3390/cancers14133170 · Cited by 67
Hu, B., He, R., Pang, K., Wang, G., Wang, N., Zhu, W., Sui, X., Teng, H., Liu, T., Zhu, J., et al. (2025) · Cell · doi:10.1016/j.cell.2024.12.023 · Cited by 70
Cai, H., Wang, C., Shukla, S., & Steinmetz, N. F. (2019) · Advanced Science · doi:10.1002/advs.201802281 · Cited by 67
Marcelis, L., Antoranz, A., Delsupehe, A.-M., Biesemans, P., Ferreiro, J. F., Debackere, K., Vandenberghe, P., Verhoef, G. E. G., Gheysens, O. S., Cattoretti, G., et al. (2020) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-020-02575-y · Cited by 67
CD4+ T cells persist for years in the human small intestine and display a TH1 cytokine profile
Bartolomé-Casado, R., Landsverk, O. J. B., Chauhan, S. K., Sætre, F., Hagen, K. T., Yaqub, S., Øyen, O. M., Horneland, R., Aandahl, E. M., Aabakken, L., et al. (2020) · Mucosal Immunology · doi:10.1038/s41385-020-0315-5 · Cited by 67
Software‐assisted decision support in digital histopathology
Huss, R., & Coupland, S. E. (2020) · The Journal of Pathology · doi:10.1002/path.5388 · Cited by 66
Prognostic significance of spatial and density analysis of T lymphocytes in colorectal cancer
Elomaa, H., Ahtiainen, M., Väyrynen, S. A., Ogino, S., Nowak, J. A., Friman, M., Helminen, O., Wirta, E., Seppälä, T. T., Böhm, J., et al. (2022) · British Journal of Cancer · doi:10.1038/s41416-022-01822-6 · Cited by 67
TMPRSS2 Is Essential for SARS-CoV-2 Beta and Omicron Infection
Metzdorf, K., Jacobsen, H., Greweling‐Pils, M. C., Hoffmann, M., Lüddecke, T., Miller, F., Melcher, L., Kempf, A. M., Nehlmeier, I., Bruder, D., et al. (2023) · Viruses · doi:10.3390/v15020271 · Cited by 66
Malla, S. B., Byrne, R. M., Lafarge, M. W., Corry, S. M., Fisher, N. C., Tsantoulis, P. K., Mills, M. L., Ridgway, R. A., Lannagan, T. R. M., Najumudeen, A. K., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01654-5 · Cited by 68
Self-citationFLT3L governs the development of partially overlapping hematopoietic lineages in humans and mice
Momenilandi, M., Lévy, R., Sobrino, S., Li, J., Lagresle‐Peyrou, C., Esmaeilzadeh, H., Fayand, A., Floc’h, C. L., Guérin, A., Mina, E. D., et al. (2024) · Cell · doi:10.1016/j.cell.2024.04.009 · Cited by 66
In vivo interaction screening reveals liver-derived constraints to metastasis
Borrelli, C., Roberts, M., Eletto, D., Hussherr, M., Fazilaty, H., Valenta, T., Lafzi, A., Kretz, J. A., Vinzoni, E. G., Karakatsani, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07715-3 · Cited by 67
Ma, D., Dai, L.-J., Wu, X., Liu, C., Zhao, S., Zhang, H., Chen, L., Xiao, Y., Li, M., Zhao, Y.-Z., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.017 · Cited by 66
Ongoing genome doubling shapes evolvability and immunity in ovarian cancer
McPherson, A., Vázquez-Garćıa, I., Myers, M. A., Al-Rawi, D. H., Zatzman, M., Weiner, A. C., Freeman, S. S., Mohibullah, N., Satas, G., Williams, M. J., et al. (2025) · Nature · doi:10.1038/s41586-025-09240-3 · Cited by 66
Martínez-Morilla, S., McGuire, J., Gaule, P., Moore, L. M., Ács, B., Cougot, D., Gown, A. M., Yaziji, H., Wang, W., Cartun, R. W., et al. (2019) · Laboratory Investigation · doi:10.1038/s41374-019-0295-9 · Cited by 66
Adaptive RSK‐EphA2‐GPRC5A signaling switch triggers chemotherapy resistance in ovarian cancer
Moyano‐Galceran, L., Pietilä, E. A., Turunen, S. P., Corvigno, S., Hjerpe, E., Bulanova, D. R., Joneborg, U., Alkasalias, T., Miki, Y., Yashiro, M., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201911177 · Cited by 66
Nikou, S.-A., Zhou, C., Griffiths, J. S., Kotowicz, N. K., Coleman, B. M., Green, M. J., Moyes, D. L., Gaffen, S. L., Naglik, J. R., & Parker, P. J. (2022) · Science Signaling · doi:10.1126/scisignal.abj6915 · Cited by 65
Girolami, I., Marletta, S., Pantanowitz, L., Torresani, E., Ghimenton, C., Barbareschi, M., Scarpa, A., Brunelli, M., Barresi, V., Trimboli, P., et al. (2020) · Cytopathology · doi:10.1111/cyt.12828 · Cited by 65
Brinker, T. J., Kiehl, L., Schmitt, M., Jutzi, T. B., Krieghoff‐Henning, E., Krahl, D., Kutzner, H., Gholam, P., Haferkamp, S., Klode, J., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.05.026 · Cited by 65
López-Martínez, S., Rodríguez‐Eguren, A., de Miguel–Gómez, L., Francés‐Herrero, E., Faus, A., Díaz, A., Pellicer, A., Ferrero, H., & Cervelló, I. (2021) · Acta Biomaterialia · doi:10.1016/j.actbio.2021.08.025 · Cited by 65
Pérez-Núñez, I., Rozalén, C., Palomeque, J. Á., Sangrador, I., Dalmau, M., Comerma, L., Hernández‐Prat, A., Casadevall, D., Menéndez, S., Liu, D. D., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00339-4 · Cited by 65
High-Mannose N-Glycans as Malignant Progression Markers in Early-Stage Colorectal Cancer
Boyaval, F., Dalebout, H., van Zeijl, R. J. M., Wang, W., Sarasqueta, A. F., Lageveen‐Kammeijer, G. S. M., Boonstra, J. J., McDonnell, L. A., Wuhrer, M., Morreau, H., et al. (2022) · Cancers · doi:10.3390/cancers14061552 · Cited by 65
Mi, H., Ho, W. J., Yarchoan, M. M., & Popel, A. S. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.892250 · Cited by 65
Histone methylation antagonism drives tumor immune evasion in squamous cell carcinomas
Li, Y., Goldberg, E. M., Chen, X., Xu, X., McGuire, J. T., Leuzzi, G., Karagiannis, D., Tate, T., Farhangdoost, N., Horth, C., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.09.007 · Cited by 65
Stashko, C., Hayward, M.-K., Northey, J. J., Pearson, N., Ironside, A. J., Lakins, J. N., Oria, R., Goyette, M.-A., Mayo, L. N., Russnes, H. G., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39085-1 · Cited by 65
Narmada, B. C., Khakpoor, A., Shirgaonkar, N., Narayanan, S., Aw, P. P. K., Singh, M., Ong, K. H., Owino, C. O., Ng, J. W. T., Yew, H.-C., et al. (2024) · Journal of Hepatology · doi:10.1016/j.jhep.2024.02.017 · Cited by 65
Chen, Z., Wang, X., Jin, Z., Li, B., Jiang, D., Wang, Y., Jiang, M., Zhang, D., Yuan, P., Zhao, Y., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00579-w · Cited by 65
A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies
Huang, Z., Yang, E., Shen, J., Gratzinger, D. A., Eyerer, F., Liang, B., Nirschl, J., Bingham, D. B., Dussaq, A., Kunder, C. A., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01223-5 · Cited by 65
Bormann, D., Knoflach, M., Poreba, E., Riedl, C. J., Testa, G., Orset, C., Levilly, A., Cottereau, A., Jauk, P., Hametner, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50465-z · Cited by 65
Punovuori, K., Bertillot, F., Miroshnikova, Y. A., Binner, M., Myllymäki, S.-M., Follain, G., Kruse, K., Routila, J., Huusko, T., Pellinen, T. S., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.046 · Cited by 65
Functional Circuitry of Neuro-Immune Communication in the Mesenteric Lymph Node and Spleen
Murray, K. M., Barboza, M., Rude, K. M., Brust‐Mascher, I., & Reardon, C. (2019) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2019.08.188 · Cited by 65
Maintaining protein stability of ∆Np63 via USP28 is required by squamous cancer cells
Prieto‐Garcia, C., Hartmann, O., Reissland, M., Braun, F., Fischer, T., Walz, S., Schülein‐Völk, C., Eilers, U., Ade, C. P., Calzado, M. A., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201911101 · Cited by 65
Craig, S. G., Humphries, M. P., Alderdice, M., Bingham, V., Richman, S. D., Loughrey, M. B., Coleman, H. G., Pulsawatdi, A. V., McCombe, K. D., Murray, G. I., et al. (2020) · British Journal of Cancer · doi:10.1038/s41416-020-0985-5 · Cited by 64
Self-citationTherapeutic inhibition of USP9x-mediated Notch signaling in triple-negative breast cancer
Jaiswal, A., Murakami, K., Elia, A., Shibahara, Y., Done, S. J., Wood, S. A., Donato, N. J., Ohashi, P. S., & Reedijk, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101592118 · Cited by 64
Deciphering the origins and fates of steroidogenic lineages in the mouse testis
Ademi, H., Djari, C., Mayère, C., Neirijnck, Y., Sararols, P., Rands, C. M., Stévant, I., Conne, B., & Nef, S. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110935 · Cited by 65
Wong, A. N. N., He, Z., Leung, K. L., To, C. C. K., Wong, C. Y., Wong, S. C. C., Yoo, J. S., Chan, C. K. R., Chan, A. Z., Lacambra, M. D., et al. (2022) · Cancers · doi:10.3390/cancers14153780 · Cited by 64
Metadata
- Authors
- Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, S., Gray, R. T., Murray, L. J., Coleman, H. G., James, J. A., Salto-Tellez, M., & Hamilton, P. W.
- Affiliations
- Queen's University Belfast (GB)
- Corresponding author
- Hamilton, Peter W.
- ORCID
- Venue
- Scientific Reports
- Published
- 2017-12-04
- Volume
- 7
- Issue
- 1
- Pages
- 16878-16878
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5715110
- OpenAlex
- W2952481429
- Cited by
- 8,908
- References
- 37
- Citations by year
- 2026: 1616 · 2025: 1896 · 2024: 1681 · 2023: 1359 · 2022: 1023 · 2021: 704 · 2020: 396 · 2019: 171 · 2018: 49 · 2017: 4
- FWCI
- 99.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- Invest Northern Ireland (RDO0712612)
- Friends of the Cancer Centre
- Cancer Research UK (20256, A15333, C37703/A15333, C11512, A20256, C11512/A20256, C50104/A17592, 15333)
- Public Health Agency (SPI/3315/06, EAT/4905/13)
- Health and Social Care Research and Development Division
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- QuPath, digital pathology, whole slide image analysis, open source software, tumor detection, biomarker analysis, batch processing, scripting, tissue image analysis
- MeSH terms
- Algorithms, Colonic Neoplasms, Humans, Image Interpretation, Computer-Assisted, Biomarkers, Tumor, User-Computer Interface, Kaplan-Meier Estimate, Programmed Cell Death 1 Ligand 2 Protein
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review