Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers
Open accessAbstract
Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72–0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.
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
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?
TME components and their interactions can influence NAC response in patients with TNBC.
“TME components and their interactions can influence NAC response in patients with TNBC [70–72].”
From Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer (2024) · cites this paper as [70]
The Introduction states that immune-system and tumor-microenvironment interactions play a role in clinical outcomes for patients with TNBC. It does not specifically link those interactions to neoadjuvant chemotherapy (NAC) response; the Methods (§2.1) say patients who received neoadjuvant chemotherapy were excluded. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793 · see citing works
Nonparametric Estimation from Incomplete Observations
Kaplan, E. L., & Meier, P. (1958) · Journal of the American Statistical Association · doi:10.1080/01621459.1958.10501452 · Cited by 39,158 · see citing works
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 · see citing works
Foulkes, W. D., Smith, I. E., & Reis-Filho, J. S. (2010) · New England Journal of Medicine · doi:10.1056/nejmra1001389 · Cited by 4,719 · see citing works
Salgado, R., Denkert, C., Demaria, S., Sirtaine, N., Klauschen, F., Pruneri, G., Wienert, S., den Eynden, G. V., Baehner, F. L., Penault-Llorca, F. M., et al. (2014) · Annals of Oncology · doi:10.1093/annonc/mdu450 · Cited by 3,142 · see citing works
Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
Aebi, S. P., Davidson, T., Gruber, G., & Castiglione, M. (2010) · Annals of Oncology · doi:10.1093/annonc/mdq159 · Cited by 2,849 · see citing works
Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
Cardoso, F., Kyriakides, S., Ohno, S., Penault-Llorca, F., Poortmans, P., Rubio, I. T., Zackrisson, S., & Senkus, E. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz173 · Cited by 2,214 · see citing works
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)
McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/dji237 · Cited by 2,162 · see citing works
Denkert, C., von Minckwitz, G., Darb-Esfahani, S., Lederer, B., Heppner, B. I., Weber, K. E., Budczies, J., Huober, J., Klauschen, F., Furlanetto, J., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30904-x · Cited by 2,157 · see citing works
Adams, S., Gray, R. J., Demaria, S., Goldstein, L. J., Perez, E. A., Shulman, L. N., Martino, S., Wang, M., Jones, V. E., Saphner, T. J., et al. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2013.55.0491 · Cited by 1,301 · see citing works
Use of Archived Specimens in Evaluation of Prognostic and Predictive Biomarkers
Simon, R. M., Paik, S., & Hayes, D. F. (2009) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djp335 · Cited by 1,040 · see citing works
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.086 · Cited by 1,027 · see citing works
Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), Savas, P., Virassamy, B., Ye, C., Salim, A., Mintoff, C. P., Caramia, F., Salgado, R., Byrne, D. J., Teo, Z. L., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0078-7 · Cited by 992 · see citing works
Loi, S., Drubay, D., Adams, S., Pruneri, G., Francis, P. A., Lacroix-Triki, M., Joensuu, H., Dieci, M. V., Badve, S., Demaria, S., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.18.01010 · Cited by 893 · see citing works
Clinical relevance of host immunity in breast cancer: from TILs to the clinic
Savas, P., Salgado, R., Denkert, C., Sotiriou, C., Darcy, P. K., Smyth, M. J., & Loi, S. (2016) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2015.215 · Cited by 890 · see citing works
Hendry, S. A., Salgado, R., Gevaert, T., Russell, P. A., John, T., Thapa, B., Christie, M., de Vijver, K. K. V., Estrada, M. V., Ericsson, P. I. G., et al. (2017) · Advances in Anatomic Pathology · doi:10.1097/pap.0000000000000162 · Cited by 707 · see citing works
Maximally Selected Rank Statistics
Lausen, B., & Schumacher, M. C. (1992) · Biometrics · doi:10.2307/2532740 · Cited by 644 · see citing works
Salgado, R., Denkert, C., Campbell, C., Savas, P. S., Nuciforo, P. G., Aura, C. M., de Azambuja, E., Eidtmann, H., Ellis, C. E., Baselga, J. M., et al. (2015) · JAMA Oncology · doi:10.1001/jamaoncol.2015.0830 · Cited by 605 · see citing works
Leukocyte composition of human breast cancer
Ruffell, B., Au, A., Rugo, H. S., Esserman, L. J., Hwang, E. S., & Coussens, L. M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1104303108 · Cited by 481 · see citing works
Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling
Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R., Gräf, S., Chin, S.-F., Schwarz, R. F., Curtis, C., Dunning, M. J., Bardwell, H., et al. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3004330 · Cited by 426 · see citing works
2011: the immune hallmarks of cancer
Cavallo, F., Giovanni, C. D., Nanni, P., Forni, G., & Lollini, P. (2011) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-010-0968-0 · Cited by 417 · see citing works
Wimberly, H., Brown, J. R., Schalper, K. A., Haack, H., Silver, M. R., Nixon, C., Bossuyt, V., Pusztai, L., Lannin, D. R., & Rimm, D. L. (2014) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-14-0133 · Cited by 395 · see citing works
Structured crowdsourcing enables convolutional segmentation of histology images
Amgad, M., Elfandy, H., Hussein, H., Atteya, L. A., Elsebaie, M. A. T., Abo Elnasr, L. S., Sakr, R. A., Salem, H. S. E., Ismail, A. F., Saad, A. M., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz083 · Cited by 315 · see citing works
Plevritis, S. K., Muñoz, D. F., Kurian, A. W., Stout, N. K., Alagöz, O., Near, A. M., Lee, S. J., van den Broek, J. J., Huang, X., Schechter, C. B., et al. (2018) · JAMA · doi:10.1001/jama.2017.19130 · Cited by 303 · see citing works
Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820199 · Cited by 288 · see citing works
The immune system and response to HER2-targeted treatment in breast cancer
Bianchini, G., & Gianni, L. (2014) · The Lancet Oncology · doi:10.1016/s1470-2045(13)70477-7 · Cited by 282 · see citing works
Diao, J. A., Wang, J. K., Chui, W. F., Mountain, V., Gullapally, S. C., Srinivasan, R., Mitchell, R. N., Glass, B., Hoffman, S., Rao, S. K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21896-9 · Cited by 281 · see citing works
Basavanhally, A. N., Ganesan, S., Agner, S. C., Monaco, J. P., Feldman, M. D., Tomaszewski, J. E., Bhanot, G. V., & Madabhushi, A. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2035305 · Cited by 270 · see citing works
Relevance of tumor-infiltrating lymphocytes in breast cancer
Dushyanthen, S., Beavis, P. A., Savas, P. S., Teo, Z. L., Zhou, C., Mansour, M., Darcy, P. K., & Loi, S. (2015) · BMC Medicine · doi:10.1186/s12916-015-0431-3 · Cited by 225 · see citing works
Wu, J., Liang, C., Chen, M., & Su, W. (2016) · Oncotarget · doi:10.18632/oncotarget.12135 · Cited by 209 · see citing works
Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer
Kos, Z., Roblin, E., Kim, R. S., Michiels, S., Gallas, B. D., Chen, W., de Vijver, K. K. V., Goel, S., Adams, S., Demaria, S., et al. (2020) · npj Breast Cancer · doi:10.1038/s41523-020-0156-0 · Cited by 206 · see citing works
Self-citationBalić, M., Thomssen, C., Würstlein, R., Gnant, M., & Harbeck, N. (2019) · Breast Care · doi:10.1159/000499931 · Cited by 171 · see citing works
Amgad, M. T., Stovgaard, E. S., Balslev, E., Thagaard, J., Chen, W., Dudgeon, S. N., Sharma, A., Kerner, J. K., Denkert, C., Yuan, Y., et al. (2020) · npj Breast Cancer · doi:10.1038/s41523-020-0154-2 · Cited by 170 · see citing works
Self-citationLearning to detect lymphocytes in immunohistochemistry with deep learning
Świderska-Chadaj, Ż., Pinckaers, H., van Rijthoven, M., Balkenhol, M. C. A., Melnikova, M., Geessink, O. G. F., Manson, Q. F., Sherman, M. E., Polónia, A., Parry, J., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101547 · Cited by 163 · see citing works
Bulten, W., Bándi, P., Hoven, J., van de Loo, R., Lotz, J., Weiss, N., van der Laak, J. A. W. M., van Ginneken, B., de Kaa, C. H., & Litjens, G. J. S. (2019) · Scientific Reports · doi:10.1038/s41598-018-37257-4 · Cited by 161 · see citing works
Breast cancer genomics and immuno-oncological markers to guide immune therapies
Hammerl, D., Smid, M., Timmermans, A. M., Sleijfer, S., Martens, J. W. M., & Debets, R. (2017) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2017.11.003 · Cited by 152 · see citing works
Le, H., Gupta, R., Hou, L., Abousamra, S., Fassler, D., Torre-Healy, L. A., Moffitt, R. A., Kurç, T., Samaras, D., Batiste, R. C., et al. (2020) · American Journal Of Pathology · doi:10.1016/j.ajpath.2020.03.012 · Cited by 125 · see citing works
Triple-Negative Breast Cancer: Current Practice and Future Directions
Costa, R. L. B., & Gradishar, W. J. (2017) · Journal of Oncology Practice · doi:10.1200/jop.2017.023333 · Cited by 101 · see citing works
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 71 · see citing works
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 · see citing works
Medical students need artificial intelligence and machine learning training
Pucchio, A., Eisenhauer, E. A., & Moraes, F. Y. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00846-2 · Cited by 61 · see citing works
Kim, R. S., Song, N., Gavin, P. G., Salgado, R., Bandos, H., Kos, Z., Floris, G., den Eynden, G. G. G. M. V., Badve, S., Demaria, S., et al. (2019) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djz032 · Cited by 56 · see citing works
Amgad, M. T., Sarkar, A., Srinivas, C., Redman, R., Ratra, S., Bechert, C. J., Calhoun, B. C., Mrazeck, K., Kurkure, U., Cooper, L., et al. (2019) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2512892 · Cited by 54 · see citing works
SISTRUNK, W. E., & MacCarty, W. C. (1922) · PubMed Central · Cited by 54 · see citing works
Yuan, Y., Lee, J. S., Yost, S. E., Li, S. M., Frankel, P. H., Ruel, C., Schmolze, D. D., Robinson, K., Tang, A., Martínez, N., et al. (2020) · The Oncologist · doi:10.1002/onco.13574 · Cited by 45 · see citing works
Can You Trust Predictive Uncertainty Under Real Dataset Shifts in Digital Pathology?
Thagaard, J., Hauberg, S., van der Vegt, B., Ebstrup, T., Hansen, J. D., & Dahl, A. B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59710-8_80 · Cited by 36 · see citing works
Self-citationOptimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics
Balkenhol, M. C. A., Ciompi, F., Świderska-Chadaj, Ż., van de Loo, R., Intezar, M., Otte‐Höller, I., Geijs, D. J., Lotz, J., Weiss, N., de Bel, T., et al. (2021) · The Breast · doi:10.1016/j.breast.2021.02.007 · Cited by 36 · see citing works
Unmasking the immune microecology of ductal carcinoma in situ with deep learning
Narayanan, P., Raza, S. E. A., Hall, A. H. S., Marks, J. R., King, L. M., West, R. B., Hernández, L., Guppy, N., Dowsett, M., Gusterson, B. A., et al. (2021) · npj Breast Cancer · doi:10.1038/s41523-020-00205-5 · Cited by 35 · see citing works
He, T.-F., Yost, S. E., Frankel, P. H., Dagis, A., Cao, Y. W., Wang, R., Rosario, A., Tu, T. Y., Solomon, S. T., Schmolze, D. D., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229955 · Cited by 32 · see citing works
Morigi, C. (2019) · ecancermedicalscience · doi:10.3332/ecancer.2019.924 · Cited by 31 · see citing works
Stenman, S., Bychkov, D., Kücükel, H., Linder, N., Haglund, C. H., Arola, J. T., & Lundin, J. E. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2994970 · Cited by 30 · see citing works
Dudgeon, S. N., Wen, S., Hanna, M. G., Gupta, R., Amgad, M. T., Sheth, M., Marble, H. D., Huang, R., Herrmann, M. D., Szu, C. H., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_83_20 · Cited by 29 · see citing works
Hudeček, J., Voorwerk, L., van Seijen, M., Nederlof, I., de Maaker, M., van den Berg, J. G., de Vijver, K. K. V., Sikorska, K., Adams, S., Demaria, S., et al. (2020) · npj Breast Cancer · doi:10.1038/s41523-020-0155-1 · Cited by 20 · see citing works
Self-citationTumor-Infiltrating Lymphocyctes in Triple-Negative Breast Cancer
Brown, L. C., Salgado, R., Luen, S. J., Savas, P. S., & Loi, S. (2021) · The Cancer Journal · doi:10.1097/ppo.0000000000000501 · Cited by 19 · see citing works
Fuchs, T. L., Pearson, A., Pickett, J., Diakos, C. I., Dewar, R., Chan, D. L. H., Guminski, A. D., Menzies, A. M., Baron-Hay, S. E., Sheen, A., et al. (2020) · Pathology · doi:10.1016/j.pathol.2020.04.004 · Cited by 8 · see citing works
Flagged first, then by citations
1 of the 59 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [36]Chen L.-C. Papandreou G. Schroff F. Adam H. Rethinking atrous convolution for semantic image segmentation arXiv 2017 1706.05587
Cited by
Sorted by citations
Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression
Fassler, D., Torre-Healy, L. A., Gupta, R., Hamilton, A. M., Kobayashi, S., Alsten, S. C. V., Zhang, Y., Kurç, T., Moffitt, R. A., Troester, M. A., et al. (2022) · Cancers · doi:10.3390/cancers14092148 · Cited by 60 · see citing works
Thagaard, J., Broeckx, G., Page, D. B., Jahangir, C. A., Verbandt, S., Kos, Z., Gupta, R., Khiroya, R., AbdulJabbar, K., Haab, G. A., et al. (2023) · The Journal of Pathology · doi:10.1002/path.6155 · Cited by 58 · see citing works
Self-citationChoi, S., Cho, S. I., Jung, W., Lee, T., Choi, S. J., Song, S., Park, G., Park, S., Ma, M., Pereira, S., et al. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00577-4 · Cited by 58 · see citing works
McCaffrey, C., Jahangir, C. A., Murphy, C. L., Burke, C., Gallagher, W. M., & Rahman, A. (2024) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2024.2346545 · Cited by 60 · see citing works
Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review
Tafavvoghi, M., Bongo, L. A., Shvetsov, N., Busund, L., & Møllersen, K. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100363 · Cited by 53 · see citing works
Multi-modality artificial intelligence in digital pathology
Qiao, Y., Zhao, L., Luo, C., Luo, Y., Wu, Y., Li, S., Bu, D., & Zhao, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac367 · Cited by 44 · see citing works
Fisher, T. B., Saini, G., Rekha, T. S., Krishnamurthy, J., Bhattarai, S., Callagy, G., Webber, M., Janssen, E. A. M., Kong, J., & Aneja, R. (2024) · Breast Cancer Research · doi:10.1186/s13058-023-01752-y · Cited by 42 · see citing works
“TME components and their interactions can influence NAC response in patients with TNBC [70–72].”
BackgroundAswolinskiy, W., Munari, E., Horlings, H. M., Mulder, L., Bogina, G. S., Sanders, J., Liu, Y.-H., van den Belt‐Dusebout, A. W., Tessier, L., Balkenhol, M. C. A., et al. (2023) · Breast Cancer Research · doi:10.1186/s13058-023-01726-0 · Cited by 37 · see citing works
Sandarenu, P., Millar, E. K. A., Song, Y., Browne, L. H., Beretov, J., Lynch, J., Graham, P., Jonnagaddala, J., Hawkins, N. J., Huang, J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-18647-1 · Cited by 33 · see citing works
Deep-learning based breast cancer detection for cross-staining histopathology images
Huang, P., Ouyang, H., Hsu, B.-Y., Chang, Y.-R., Lin, Y.-C., Chen, Y.-A., Hsieh, Y.-H., Fu, C., Li, C., Lin, C., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13171 · Cited by 32 · see citing works
Verdicchio, M., Brancato, V., Cavaliere, C., Isgrò, F., Salvatore, M., & Aiello, M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e14371 · Cited by 32 · see citing works
Zhang, H., Chen, L., Li, L., Liu, Y., Das, B., Zhai, S., Tan, J., Jiang, Y., Turco, S., Yao, Y., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00866-0 · Cited by 23 · see citing works
Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning
Bhattarai, S., Saini, G., Li, H., Duanmu, H., Seth, G., Fisher, T. B., Janssen, E. A. M., Kiraz, U., Kong, J., & Aneja, R. (2023) · Diagnostics · doi:10.3390/diagnostics14010074 · Cited by 23 · see citing works
Baharun, N. B., Adam, A., Zailani, M. A. H., Rajpoot, N., Xu, Q., & Zin, R. R. M. (2024) · BMC Cancer · doi:10.1186/s12885-024-12962-8 · Cited by 19 · see citing works
Finkelman, B. S., Zhang, H., Hicks, D. G., Rimm, D. L., & Turner, B. M. (2025) · Human Pathology · doi:10.1016/j.humpath.2025.105866 · Cited by 17 · see citing works
Garcia, V. L., Elfer, K. N., Peeters, D. J. E., Ehinger, A., Werness, B., Ly, A., Li, X., Hanna, M. G., Blenman, K., Salgado, R., et al. (2022) · Cancers · doi:10.3390/cancers14102467 · Cited by 16 · see citing works
Millward, J., He, Z., Nibali, A., Mouradov, D., Mielke, L. A., Tran, K., Chou, A. S. Y., Hawkins, N. J., Ward, R. L., Gill, A. J., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06254-3 · Cited by 12 · see citing works
WeakSTIL: weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need
Schirris, Y., Engelaer, M., Panteli, A., Horlings, H. M., Gavves, E., & Teuwen, J. (2022) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2611528 · Cited by 11 · see citing works
Zhang, X., Zhou, H., Chen, Q., Wang, X., Fu, Y., Jin, C., Zhou, F., Wang, J.-P., Cai, Q.-Y., Wang, J., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103557 · Cited by 10 · see citing works
Schirris, Y., Voorthuis, R. A. B., Opdam, M., Liefaard, M. C., Sonke, G. S., Dackus, G. M. H. E., de Jong, V., Wang, Y., van Rossum, A. G. J., Steenbruggen, T. G., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100921 · Cited by 9 · see citing works
Liu, A., Li, X., Wu, H., Guo, B., Jonnagaddala, J., Zhang, H., & Xu, S. X. (2023) · JCO Precision Oncology · doi:10.1200/po.22.00522 · Cited by 9 · see citing works
Victor, B., Nibali, A., Newman, S., Coram, T. E., Pinto, F., Reynolds, M. P., Furbank, R. T., & He, Z. (2024) · Remote Sensing · doi:10.3390/rs16020282 · Cited by 8 · see citing works
Artificial Intelligence in digital pathology of breast cancer, new era of practice?
Li, W., Ye, S., Jin, Z., Chen, L., Chao, Y., Wei, G., Huang, Q., Tu, H., & Wang, Q. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002953 · Cited by 7 · see citing works
Baharun, N. B., Zailani, M. A. H., Adam, A., Xu, Q., Mustangin, M., & Zin, R. R. M. (2025) · Diagnostics · doi:10.3390/diagnostics15192492 · Cited by 6 · see citing works
Spathas, N., GOUSSIA, A. C., Koliou, G.-A., Gogas, H. J., Zagouri, F., Batistatou, A. K., Charchanti, A. V., Papoudou‐Bai, A., Bobos, M., Chrisafi, S., et al. (2022) · Cancers · doi:10.3390/cancers14225635 · Cited by 6 · see citing works
Wang, B. G., Yao, Y., Huang, H., Zhou, J., & Ni, C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1131259 · Cited by 6 · see citing works
Computational pathology for breast cancer: Where do we stand for prognostic applications?
Gessain, G., & Lacroix‐Triki, M. (2025) · The Breast · doi:10.1016/j.breast.2025.104464 · Cited by 5 · see citing works
Bendani, H., Boumajdi, N., Belyamani, L., & Ibrahimi, A. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5010009 · Cited by 4 · see citing works
Cai, X., Chen, Y., Li, Q., Wei, T., Yang, Y.-C., Ye, R., Chao, X., Li, M., He, J., Luo, R., et al. (2025) · The Breast · doi:10.1016/j.breast.2025.104634 · Cited by 4 · see citing works
Zhang, H., Huang, Q., Shang, B., Su, G., & Tu, H. K. (2026) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-014429 · Cited by 2 · see citing works
Gonzàlez‐Farré, M., Gibert, J., Díaz, P. S., Santos, J., García, P., Massó, J., Bellosillo, B., Lloveras, B., Albanell, J., Vázquez, I., et al. (2023) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-023-03608-4 · Cited by 2 · see citing works
Clavel, M., Sockeel, S., Sockeel, M., Miquel, C., Adam, J., Lantéri, E., & Pozin, N. (2023) · doi:10.36227/techrxiv.21981614.v1 · Cited by 2 · see citing works
Prioritizing cases from a multi-institutional cohort for a dataset of pathologist annotations
Garcia, V. L., Gardecki, E., Jou, S., Li, X., Shroyer, K. R., Saltz, J. H., Ács, B., Elfer, K. N., Lennerz, J. K., Salgado, R., et al. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2024.100411 · Cited by 1 · see citing works
Baharun, N. B., Zailani, M. A. H., Adam, A., Rajpoot, N., Qiaoyi, X. U., & Zin, R. R. M. (2025) · Egyptian Journal of Medical Human Genetics · doi:10.1186/s43042-025-00797-2 · Cited by 1 · see citing works
Tang, Z., Huang, T., & Yang, T.-L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262311596 · Cited by 1 · see citing works
Wang, X., Xiong, D., Cui, S., Duan, B., Hung, Y., Jing, H. E., Ding, G., Tang, Y., & (15087726), Q. W. (2026) · Discover Oncology · doi:10.1007/s12672-026-04864-x · Cited by 3 · see citing works
Gonzalez, L. E. L., Giudici, F., Salgado, R., Yan, H., Rojo, F., Comerma, L., Wienert, S., Palacios, J., Kos, Z., de Haas, S. L., et al. (2026) · The Lancet Oncology · doi:10.1016/s1470-2045(26)00247-0 · Cited by 1 · see citing works
Aswolinskiy, W., Munari, E., Horlings, H. M., Mulder, L., Bogina, G. S., Sanders, J., Liu, Y.-H., van den Belt‐Dusebout, A. W., Tessier, L., Balkenhol, M. C. A., et al. (2022) · medRxiv · doi:10.1101/2022.11.11.22282205
Clavel, M., Sockeel, S., Sockeel, M., Miquel, C., Adam, J., Lantéri, E., & Pozin, N. (2023) · doi:10.36227/techrxiv.21981614
Clavel, M., Sockeel, S., Sockeel, M., Miquel, C., Adam, J., Lantéri, E., & Pozin, N. (2023) · doi:10.36227/techrxiv.21981614.v2
Artificial intelligence and the interplay between cancer and immunity
Gupta, R., Kurç, T., & Saltz, J. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95359-7.00015-7
Kiraz, U., Rewcastle, E., Pettersen, K. B., Abono, D. M., Raghe, S. H., Gudlaugsson, E. G., Baak, J. P. A., & Janssen, E. A. M. (2025) · Journal of Clinical Pathology · doi:10.1136/jcp-2024-209855
van Rijthoven, M., Aswolinskiy, W., Tessier, L., Balkenhol, M. C. A., Bogaerts, J. M. A., Drubay, D., Comerma, L., Peeters, D. J. E., Stovgaard, E. S., Lænkholm, A., et al. (2025) · medRxiv · doi:10.1101/2025.02.28.25323078
Self-citationApplications of Artificial Intelligence in Computational Cytology
Dey, P. (2025) · Springer eBooks · doi:10.1007/978-981-95-3272-8_9 · Cited by 1 · see citing works
Fisher, T. B., Saini, G., Rekha, T. S., Krishnamurthy, J., Bhattarai, S., Callagy, G. M., Webber, M., Janssen, E. A. M., Kong, J., & Aneja, R. (2024) · Figshare · doi:10.6084/m9.figshare.25025860
Aswolinskiy, W., Munari, E., Horlings, H. M., Mulder, L., Bogina, G. S., Sanders, J., Liu, Y.-H., van den Belt‐Dusebout, A. W., Tessier, L., Balkenhol, M. C. A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26639184
Bian, B., Cao, Y., & Yang, J. Y. H. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag176
Çapar, A., Aloglu, I., Aker, F., Ertano, M., Mese, Y. E., Ungor, A., & Yildiz, B. E. (2026) · medRxiv · doi:10.64898/2026.05.29.26354196
Zhang, W., Hui, C., & Tan, M. (2024) · Journal of Medical Sciences and Interdisciplinary Research · doi:10.51847/q9wh3rzha1
Dunenova, G., Sarsembayev, A., Oshibayeva, A., Kyrykbayeva, S., Abdrakhmanova, Z., Omarova, B., Ivankov, A., Ualiyeva, A. Y., Zhakypova, B., Zhagiparova, Z., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1908349
- Authors
- Thagaard, J., Stovgaard, E. S., Vognsen, L. G., Hauberg, S., Dahl, A., Ebstrup, T., Doré, J., Vincentz, R. E., Jepsen, R. K., Roslind, A., Kümler, I., Nielsen, D., & Balslev, E.
- Affiliations
- Technical University of Denmark (DK)
- Herlev Hospital (DK)
- Visiopharm A/S, 2970 Hørsholm, Denmark
- Corresponding author
- Thagaard, Jeppe
- ORCID
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2021-06-18
- Volume
- 13
- Issue
- 12
- Pages
- 3050
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC8235502
- OpenAlex
- W3176612188
- Cited by
- 50
- References
- 59
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 10 · 2023: 12 · 2022: 7
- FWCI
- 2.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Funders
- Innovationsfonden (8053-00008B)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- triple-negative breast cancer, stromal tumor-infiltrating lymphocytes, prognostic factors, overall survival, computational pathology, prognostic stratification
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
