The DOI matches its Crossref record; 138 of the 139 references it cites were found in a registry or an index.
Digital pathology and artificial intelligence in translational medicine and clinical practice
Open accessAbstract
Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.
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 check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“In radiology, AI can assist radiologists in improving diagnoses and enhancing patient care by analyzing chest X-rays, magnetic resonance images, and computed tomography scans (41).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [41]
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.
Citations
Cites
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
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
Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer
Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., Gottfried, M., Peled, N., Tafreshi, A., Cuffe, S., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1606774 · Cited by 10,089
Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer
Borghaei, H., Paz-Ares, L., Horn, L., Spigel, D. R., Steins, M., Ready, N. E., Chow, L. Q., Vokes, E. E., Felip, E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1507643 · Cited by 9,335
Herbst, R. S., Baas, P., Kim, D.-W., Felip, E., Pérez-Gracia, J. L., Han, J.-Y., Molina, J., Kim, J.-H., Arvis, C. D., Ahn, M.-J., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01281-7 · Cited by 6,449
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
Cortazar, P., Zhang, L., Untch, M., Mehta, K. M., Costantino, J. P., Wolmark, N., Bonnefoi, H. R., Cameron, D., Gianni, L., Valagussa, P., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62422-8 · Cited by 4,777
Rittmeyer, A., Barlési, F., Waterkamp, D., Park, K., Ciardiello, F., von Pawel, J., Gadgeel, S. M., Hida, T., Kowalski, D. M., Cobo, M., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)32517-x · Cited by 4,718
Durvalumab after chemoradiotherapy in stage III non–small-cell lung cancer
Antonia, S. J., Villegas, A., Daniel, D., Vicente, D., Murakami, S., Hui, R., Yokoi, T., Chiappori, A., Lee, K. H., de Wit, M., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1709937 · Cited by 4,593
Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer
Schmid, P., Adams, S., Rugo, H. S., Schneeweiss, A., Barrios, C. H., Iwata, H., Diéras, V., Hegg, R., Im, S.-A., Shaw Wright, G., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1809615 · Cited by 4,350
Rosenberg, J. E., Hoffman-Censits, J., Powles, T., van der Heijden, M. S., Balar, A. V., Necchi, A., Dawson, N., O'Donnell, P. H., Balmanoukian, A., Loriot, Y., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)00561-4 · Cited by 3,669
Pembrolizumab as Second-Line Therapy for Advanced Urothelial Carcinoma
Bellmunt, J., de Wit, R., Vaughn, D. J., Fradet, Y., Lee, J.-L., Fong, L., Vogelzang, N. J., Climent, M. A., Petrylak, D. P., Choueiri, T. K., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1613683 · Cited by 3,395
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930
Fehrenbacher, L., Spira, A., Ballinger, M., Kowanetz, M., Vansteenkiste, J., Mazieres, J., Park, K., Smith, D., Artal-Cortes, A., Lewanski, C., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)00587-0 · Cited by 2,756
First-Line Nivolumab in Stage IV or Recurrent Non–Small-Cell Lung Cancer
Carbone, D. P., Reck, M., Paz-Ares, L., Creelan, B., Horn, L., Steins, M., Felip, E., van den Heuvel, M. M., Ciuleanu, T.-E., Badin, F., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1613493 · Cited by 2,503
Sato, E., Olson, S. H., Ahn, J., Bundy, B. N., Nishikawa, H., Qian, F., Jungbluth, A. A., Frosina, D., Gnjatic, S., Ambrosone, C. B., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0509182102 · Cited by 2,445
Pagès, F., Mlecnik, B., Marliot, F., Bindea, G., Ou, F.-S., Bifulco, C., Lugli, A., Zlobec, I., Rau, T. T., Berger, M. D., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30789-x · Cited by 2,075
Maas, M., Nelemans, P. J., Valentini, V., Das, P., Rödel, C. M., Kuo, L., Calvo, F. Á., García‐Aguilar, J., Glynne‐Jones, R., Haustermans, K. M., et al. (2010) · The Lancet Oncology · doi:10.1016/s1470-2045(10)70172-8 · Cited by 1,945
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698
Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458
Langer, C. J., Gadgeel, S. M., Borghaei, H., Papadimitrakopoulou, V. A., Patnaik, A., Powell, S. F., Gentzler, R. D., Martins, R. G., Stevenson, J. P., Jalal, S. I., et al. (2016) · The Lancet Oncology · doi:10.1016/s1470-2045(16)30498-3 · Cited by 1,404
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
Janowczyk, A. R., & Madabhushi, A. (2016) · Journal of Pathology Informatics · doi:10.4103/2153-3539.186902 · Cited by 1,387
Sun, R., Limkin, E. J., Vakalopoulou, M., Dercle, L., Champiat, S., Han, S. R., Verlingue, L., Brandao, D., Lancia, A., Ammari, S., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30413-3 · Cited by 1,236
Gao, Q., Qiu, S., Fan, J., Zhou, J., Wang, X., Xiao, Y., Xu, Y., Li, Y., & Tang, Z. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2006.09.4565 · Cited by 1,134
Classification of breast cancer histology images using Convolutional Neural Networks
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177544 · Cited by 1,044
Deep learning in histopathology: the path to the clinic
van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902
Horn, L., Spigel, D. R., Vokes, E. E., Holgado, E., Ready, N., Steins, M. B., Poddubskaya, E. V., Borghaei, H., Felip, E., Paz-Ares, L. G., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.74.3062 · Cited by 875
Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-048008 · Cited by 852
Tsao, M., Kerr, K. M., Kockx, M. M., Beasley, M. B., Borczuk, A., Botling, J., Bubendorf, L., Chirieac, L. R., Chen, G., Chou, T., et al. (2018) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2018.05.013 · Cited by 805
Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
Beck, A. H., Sangoi, A. R., Leung, S. C. Y., Marinelli, R. J., Nielsen, T. O., van de Vijver, M. J., West, R. B., van de Rijn, M., & Koller, D. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002564 · Cited by 780
Stack, E. C., Wang, C., Roman, K., & Hoyt, C. C. (2014) · Methods · doi:10.1016/j.ymeth.2014.08.016 · Cited by 779
Rimm, D. L., Han, G., Taube, J. M., Yi, E. S., Bridge, J. A., Flieder, D. B., Homer, R., West, W. W., Wu, H., Roden, A. C., et al. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2017.0013 · Cited by 764
Deep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749
Martin, S., Heneghan, H., & Winter, D. C. (2012) · British journal of surgery · doi:10.1002/bjs.8702 · Cited by 641
Janjigian, Y. Y., Bendell, J. C., Calvo, E., Kim, J. W., Ascierto, P. A., Sharma, P., Ott, P. A., Peltola, K. J., Jaeger, D., Evans, J., et al. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2017.76.6212 · Cited by 636
Carstens, J. L., Correa de Sampaio, P., Yang, D., Barua, S., Wang, H., Rao, A., Allison, J. P., LeBleu, V. S., & Kalluri, R. (2017) · Nature Communications · doi:10.1038/ncomms15095 · Cited by 633
Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade
Lu, S., Stein, J. E., Rimm, D. L., Wang, D. W., Bell, M. A., Johnson, D. B., Sosman, J. A., Schalper, K. A., Anders, R. A., Wang, H., et al. (2019) · JAMA Oncology · doi:10.1001/jamaoncol.2019.1549 · Cited by 632
Pantanowitz, L., Sinard, J. H., Henricks, W. H., Fatheree, L. A., Carter, A. B., Contis, L. C., Beckwith, B. A., Evans, A. J., Lal, A., & Parwani, A. V. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2013-0093-cp · Cited by 622
Cottrell, T. R., Thompson, E. D., Forde, P. M., Stein, J. E., Duffield, A. S., Anagnostou, V., Rekhtman, N., Anders, R. A., Cuda, J. D., Illei, P. B., et al. (2018) · Annals of Oncology · doi:10.1093/annonc/mdy218 · Cited by 592
Tumeh, P. C., Hellmann, M. D., Hamid, O., Tsai, K. K., Loo, K. L., Gubens, M. A., Rosenblum, M. D., Harview, C. L., Taube, J. M., Handley, N. R., et al. (2017) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-16-0325 · Cited by 582
CD4 and CD8 T lymphocyte interplay in controlling tumor growth
Ostroumov, D., Fekete-Drimusz, N., Saborowski, M., Kühnel, F., & Woller, N. (2017) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-017-2686-7 · Cited by 566
Artificial Intelligence and Digital Pathology: Challenges and Opportunities
Tizhoosh, H. R., & Pantanowitz, L. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_53_18 · Cited by 544
Deep learning-based classification of mesothelioma improves prediction of patient outcome
Courtiol, P., Maussion, C., Moarii, M., Pronier, E., Pilcer, S., Sefta, M., Manceron, P., Toldo, S., Zaslavskiy, M., Le Stang, N., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0583-3 · Cited by 544
Roa, Á. A. C., Gilmore, H. L., Basavanhally, A. N., Feldman, M. D., Ganesan, S., Shih, N. N. C., Tomaszewski, J., Gonzalez, F. A., & Madabhushi, A. (2017) · Scientific Reports · doi:10.1038/srep46450 · Cited by 542
Lehmann, B. D., & Pietenpol, J. A. (2013) · The Journal of Pathology · doi:10.1002/path.4280 · Cited by 496
Abels, E., Pantanowitz, L., Aeffner, F., Zarella, M. D., van der Laak, J. A. W. M., Bui, M. M., Vemuri, V. N. P., Parwani, A. V., Gibbs, J., Agosto‐Arroyo, E., et al. (2019) · The Journal of Pathology · doi:10.1002/path.5331 · Cited by 469
Durán, J. M., & Jongsma, K. R. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-106820 · Cited by 446
Immunohistochemistry as an Important Tool in Biomarkers Detection and Clinical Practice
Matos, L. L., Trufelli, D. C., de Matos, M. G. L., & Pinhal, M. A. D. S. (2010) · Biomarker Insights · doi:10.4137/bmi.s2185 · Cited by 442
A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association
Zarella, M. D., Bowman, D., Aeffner, F., Farahani, N., Xthona, A., Absar, S. F., Parwani, A. V., Bui, M. M., & Hartman, D. J. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0343-ra · Cited by 431
Aeffner, F., Zarella, M. D., Buchbinder, N., Bui, M. M., Goodman, M. R., Hartman, D. J., Lujan, G. M., Molani, M. A., Parwani, A. V., Lillard, K., et al. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_82_18 · Cited by 427
Büttner, R., Gosney, J. R., Skov, B. G., Adam, J., Motoi, N., Bloom, K. J., Dietel, M., Longshore, J. W., López‐Ríos, F., Penault-Llorca, F. M., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.74.7642 · Cited by 398
Kramer, J. A., Sagartz, J. E., & Morris, D. L. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2378 · Cited by 377
HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides
Janowczyk, A. R., Zuo, R., Gilmore, H. L., Feldman, M. D., & Madabhushi, A. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00157 · Cited by 357
Taube, J. M., Aktürk, G., Angelo, M., Engle, E. L., Gnjatic, S., Greenbaum, S., Greenwald, N. F., Hedvat, C. V., Hollmann, T. J., Juco, J. W., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2019-000155 · Cited by 300
Gulley, J. L., Rajan, A., Spigel, D. R., Iannotti, N. O., Chandler, J. C., Wong, D. J. L., Leach, J. W., Edenfield, W. J., Wang, D., Grote, H. J., et al. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30240-1 · Cited by 286
Hekler, A., Utikal, J. S., Enk, A. H., Solass, W., Schmitt, M., Klode, J., Schadendorf, D., Sondermann, W., Franklin, C., Bestvater, F., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.06.012 · Cited by 285
Translational AI and deep learning in diagnostic pathology
Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M.-J., Diamond, J., O'Reilly, P., & Hamilton, P. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00185 · Cited by 274
Digital pathology and computational image analysis in nephropathology
Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A., & Balis, U. G. J. (2020) · Nature Reviews Nephrology · doi:10.1038/s41581-020-0321-6 · Cited by 264
Chen, M., Zhang, B., Topatana, W., Cao, J., Zhu, H.-P., Juengpanich, S., Mao, Q., Yu, H., & Cai, X. (2020) · npj Precision Oncology · doi:10.1038/s41698-020-0120-3 · Cited by 246
Novel technologies and emerging biomarkers for personalized cancer immunotherapy
Yuan, J. D., Hegde, P. S., Clynes, R., Foukas, P. G., Harari, A., Kleen, T. O., Kvistborg, P., Maccalli, C., Maecker, H. T., Page, D. B., et al. (2016) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-016-0107-3 · Cited by 235
Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology
Heindl, A., Nawaz, S., & Yuan, Y. (2015) · Laboratory Investigation · doi:10.1038/labinvest.2014.155 · Cited by 220
Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer
Vandenberghe, M. E., Scott, M. L. J., Scorer, P., Söderberg, M. P., Balcerzak, D., & Barker, C. (2017) · Scientific Reports · doi:10.1038/srep45938 · Cited by 215
Whole slide imaging equivalency and efficiency study: experience at a large academic center
Hanna, M. G., Reuter, V. E., Hameed, M., Tan, L. K., Chiang, S., Sigel, C., Hollmann, T. J., Giri, D. D., Samboy, J., Moradel, C., et al. (2019) · Modern Pathology · doi:10.1038/s41379-019-0205-0 · Cited by 215
The estimation of tumor cell percentage for molecular testing by pathologists is not accurate
Smits, A. J. J., Kummer, J. A., de Bruin, P. C., Bol, M., van den Tweel, J. G., Seldenrijk, K. A., Willems, S. M., Offerhaus, G. J. A., de Weger, R. A., van Diest, P. J., et al. (2013) · Modern Pathology · doi:10.1038/modpathol.2013.134 · Cited by 213
Brunnström, H., Johansson, A., Westbom-Fremer, S., Backman, M., Djureinovic, D., Patthey, A., Isaksson‐Mettävainio, M., Gulyás, M., & Micke, P. (2017) · Modern Pathology · doi:10.1038/modpathol.2017.59 · Cited by 204
Digital pathology and image analysis in tissue biomarker research
Hamilton, P. W., Bankhead, P., Wang, Y., Hutchinson, R. A., Kieran, D., McArt, D. G., James, J. A., & Salto‐Tellez, M. (2014) · Methods · doi:10.1016/j.ymeth.2014.06.015 · Cited by 191
Hierarchical graph representations in digital pathology
Pati, P., Jaume, G., Foncubierta–Rodríguez, A., Feroce, F., Anniciello, A. M., Scognamiglio, G., Brancati, N., Fiche, M., Dubruc, E., Riccio, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102264 · Cited by 185
Pan-Tumor Pathologic Scoring of Response to PD-(L)1 Blockade
Stein, J. E., Lipson, E. J., Cottrell, T. R., Forde, P. M., Anders, R. A., Cimino-Mathews, A., Thompson, E. D., Allaf, M. E., Yarchoan, M., Feliciano, J., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2379 · Cited by 177
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
Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network
Retámero, J. A., Aneiros‐Fernández, J., & del Moral, R. G. (2019) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0541-oa · Cited by 161
Systems pathology by multiplexed immunohistochemistry and whole-slide digital image analysis
Blom, S., Paavolainen, L., Bychkov, D., Turkki, R., Mäki-Teeri, P., Hemmes, A., Välimäki, K. E., Lundin, J. E., Kallioniemi, O., & Pellinen, T. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-15798-4 · Cited by 160
Noninvasive detection of macrophage activation with single-cell resolution through machine learning
Pavillon, N., Hobro, A. J., Akira, S., & Smith, N. I. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711872115 · Cited by 151
Precision immunoprofiling by image analysis and artificial intelligence
Koelzer, V. H., Sirinukunwattana, K., Rittscher, J., & Mertz, K. D. (2018) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-018-2485-z · Cited by 148
Quality control stress test for deep learning-based diagnostic model in digital pathology
Schömig-Markiefka, B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00859-x · Cited by 146
Shamai, G., Binenbaum, Y., Slossberg, R., Duek, I., Gil, Z., & Kimmel, R. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.7700 · Cited by 133
Tumor purity as a prognosis and immunotherapy relevant feature in gastric cancer
Gong, Z., Zhang, J., & Guo, W. (2020) · Cancer Medicine · doi:10.1002/cam4.3505 · Cited by 133
Wang, X., Janowczyk, A. R., Zhou, Y., Thawani, R., Fu, P., Schalper, K. A., Velcheti, V., & Madabhushi, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-13773-7 · Cited by 131
Feng, Z., Puri, S., Moudgil, T. L., Wood, W. J., Hoyt, C. C., Wang, C., Urba, W. J., Curti, B. D., Bifulco, C. B., & Fox, B. A. (2015) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-015-0091-z · Cited by 130
Patel, S. S., Weirather, J. L., Lipschitz, M., Lako, A., Chen, P.-H., Griffin, G. K., Armand, P. A., Shipp, M. A., & Rodig, S. J. (2019) · Blood · doi:10.1182/blood.2019002206 · Cited by 130
Buisseret, L., Desmedt, C., Garaud, S., Fornili, M., Wang, X., den Eyden, G. V., de Wind, A., Duquenne, S., Boisson, A., Naveaux, C., et al. (2017) · Modern Pathology · doi:10.1038/modpathol.2017.43 · Cited by 129
Kulkarni, P. M., Robinson, E. J., Pradhan, J. S., Gartrell, R. D., Rohr, B. R., Trager, M. H., Geskin, L. J., Kluger, H. M., Wong, P. F., Ács, B., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-1495 · Cited by 128
Introduction to digital pathology and computer-aided pathology
Nam, S., Chong, Y., Jung, C. K., Kwak, T.-Y., Lee, J. Y., Park, J., Rho, M. J., & Go, H. (2020) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2019.12.31 · Cited by 126
Evaluating tumor heterogeneity in immunohistochemistry-stained breast cancer tissue
Potts, S. J., Krueger, J. S., Landis, N. D., Eberhard, D. A., Young, G. D., Schmechel, S. C., & Lange, H. (2012) · Laboratory Investigation · doi:10.1038/labinvest.2012.91 · Cited by 125
Recent developments in multiplexing techniques for immunohistochemistry
Dixon, A. R., Bathany, C., Tsuei, M., White, J. B. R., Barald, K. F., & Takayama, S. (2015) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.2015.1069182 · Cited by 119
Taube, J. M., Roman, K., Engle, E. L., Wang, C., Ballesteros‐Merino, C., Jensen, S. M., McGuire, J., Jiang, M., Coltharp, C., Remeniuk, B., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-002197 · Cited by 114
The use of digital pathology and image analysis in clinical trials
Pell, R., Oien, K. A., Robinson, M., Pitman, H., Rajpoot, N., Rittscher, J., Snead, D. R. J., & Verrill, C. L. (2019) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.127 · Cited by 113
American Telemedicine Association clinical guidelines for telepathology
Pantanowitz, L., Dickinson, K., Evans, A. J., Hassell, L. A., Henricks, W. H., Lennerz, J. K., Lowe, A., Parwani, A. V., Riben, M. W., Smith, C. D., et al. (2014) · Journal of Pathology Informatics · doi:10.4103/2153-3539.143329 · Cited by 110
Representation Learning of Histopathology Images using Graph Neural Networks
Adnan, M., Kalra, S., & Tizhoosh, H. R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00502 · Cited by 109
Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy
Althammer, S., Tan, T. H., Spitzmüller, A., Rognoni, L., Wiestler, T., Herz, T., Widmaier, M., Rebelatto, M. C., Kaplon, H., Damotte, D., et al. (2019) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-019-0589-x · Cited by 100
Steele, K. E., Tan, T. H., Korn, R., Dacosta, K., Brown, C., Kuziora, M. A., Zimmermann, J., Laffin, B., Widmaier, M., Rognoni, L., et al. (2018) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-018-0326-x · Cited by 99
Solinas, C., Migliori, E., Silva, P. D., & Willard-Gallo, K. E. (2019) · Cancers · doi:10.3390/cancers11081213 · Cited by 95
Browning, L., Colling, R. T., Rakha, E. A., Rajpoot, N., Rittscher, J., James, J. A., Salto‐Tellez, M., Snead, D. R. J., & Verrill, C. L. (2020) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2020-206854 · Cited by 88
Neltner, J. H., Abner, E. L., Schmitt, F. A., Denison, S. K., Anderson, S., Patel, E., & Nelson, P. T. (2012) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e3182768de4 · Cited by 85
Bui, M. M., Riben, M. W., Allison, K. H., Chlipala, E. A., Colasacco, C., Kahn, A. G., Lacchetti, C., Madabhushi, A., Pantanowitz, L., Salama, M. E., et al. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0378-cp · Cited by 83
Petrillo, M., Zannoni, G. F., Tortorella, L., Anchora, L. P., Salutari, V., Ercoli, A., Margariti, P. A., Scambia, G., & Fagotti, A. (2014) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2014.06.034 · Cited by 80
Diagnostic Immunohistochemistry: What Can Go Wrong and How to Prevent It
Gown, A. M. (2016) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2016-0119-ra · Cited by 79
Jackisch, C., Hegg, R., Stroyakovskiy, D., Ahn, J., Melichar, B., Chen, S., Kim, S., Lichinitser, M., Starosławska, E., Kunz, G., et al. (2016) · European Journal of Cancer · doi:10.1016/j.ejca.2016.03.087 · Cited by 78
Dynamics of Allograft Fibrosis in Pediatric Liver Transplantation
Venturi, C., Sempoux, C. L., Quinones, J., Bourdeaux, C., Pérez‐Hoyos, S., Sokal, E. M., & Reding, R. (2014) · American Journal of Transplantation · doi:10.1111/ajt.12740 · Cited by 70
Netto, G. J., Eisenberger, M., & Epstein, J. I. (2010) · Urology · doi:10.1016/j.urology.2010.08.031 · Cited by 68
Ascierto, P. A., Bifulco, C. B., Palmieri, G., Peters, S., & Sidiropoulos, N. (2019) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2019.05.004 · Cited by 65
Schmitt, M., Maron, R. C., Hekler, A., Stenzinger, A., Hauschild, A., Weichenthal, M., Tiemann, M., Krahl, D., Kutzner, H., Utikal, J. S., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/23436 · Cited by 63
García‐Rojo, M. (2016) · Pathobiology · doi:10.1159/000441192 · Cited by 61
Prognostic Factors for Checkpoint Inhibitor Based Immunotherapy: An Update With New Evidences
Yan, X., Zhang, S., Deng, Y., Wang, P., Hou, Q., & Xu, H. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01050 · Cited by 59
Feliubadaló, L., Tonda, R., Gausachs, M., Trotta, J.-R., Castellanos, E., López‐Doriga, A., Teulé, À., Tornero, E., Valle, J. D., Gel, B., et al. (2017) · Scientific Reports · doi:10.1038/srep37984 · Cited by 54
Zhang, F., Yao, S., Li, Z., Liang, C., Zhao, K., Huang, Y., Gao, Y., Qu, J., Liu, Z., & Liu, Z. (2020) · Clinical and Translational Medicine · doi:10.1002/ctm2.110 · Cited by 54
Not Just Digital Pathology, Intelligent Digital Pathology
Ács, B., & Rimm, D. L. (2018) · JAMA Oncology · doi:10.1001/jamaoncol.2017.5449 · Cited by 46
Dong, J., Geng, Y., Lu, D., Li, B., Tian, L., Lin, D. C.-C., & Zhang, Y. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01629 · Cited by 46
Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors
Gong, C., Anders, R. A., Zhu, Q., Taube, J. M., Green, B., Cheng, W., Bartelink, I. H., Vicini, P., Wang, B., & Popel, A. S. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2018.00649 · Cited by 44
Bodén, A., Molin, J., Garvin, S., West, R. A., Lundström, C. F., & Treanor, D. E. (2021) · Histopathology · doi:10.1111/his.14356 · Cited by 44
Nativ, N. I., Chen, A. I., Yarmush, G., Henry, S. D., Lefkowitch, J. H., Klein, K. M., Maguire, T. J., Schloss, R. S., Guarrera, J. V., Francois Berthiaume, B., et al. (2013) · Liver Transplantation · doi:10.1002/lt.23782 · Cited by 42
Integrating mutation variant allele frequency into clinical practice in myeloid malignancies
Sallman, D. A., & Padron, E. (2016) · Hematology/Oncology and Stem Cell Therapy · doi:10.1016/j.hemonc.2016.04.003 · Cited by 39
Scientific and Regulatory Policy Committee (SRPC) Paper
Long, R. E., Smith, A., Machotka, S. V., Chlipala, E. A., Cann, J. A., Knight, B., Kawano, Y., Elin, J., & Lowe, A. (2012) · Toxicologic Pathology · doi:10.1177/0192623312451162 · Cited by 38
Durán, J. M., & Jongsma, K. R. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2021-107531 · Cited by 38
Tuomari, D., Kemp, R. K., Sellers, R. S., Yarrington, J. T., Geoly, F. J., Fouillet, X., Dybdal, N. O., & Perry, R. (2007) · Toxicologic Pathology · doi:10.1080/01926230701284509 · Cited by 37
Central Pathology Review for Phase III Clinical Trials: The Enabling Effect of Virtual Microscopy
Mróz, P., Parwani, A. V., & Kulesza, P. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2012-0093-ra · Cited by 35
Histo-genomics: digital pathology at the forefront of precision medicine
Barsoum, I. B., Tawedrous, E., Faragalla, H., & Yousef, G. M. (2018) · Diagnosis · doi:10.1515/dx-2018-0064 · Cited by 34
Zhang, B., Li, Q., Jia, S., Li, F., Li, Q., & Li, J. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02278-z · Cited by 34
Kim, H.-N., Jang, J., Heo, Y. J., Kim, B., Jung, H., Jang, Y., Kang, S. Y., Kim, S. T., Lee, J., Kang, W. K., et al. (2019) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-019-02653-2 · Cited by 27
Miesbach, W., Meijer, K., Coppens, M., Kampmann, P., Klamroth, D., Schutgens, R., Castaman, G., Seifried, E., Schwaeble, J., Bönig, H., et al. (2019) · Blood · doi:10.1182/blood-2019-122535 · Cited by 27
Pathology is a necessary and informative tool in oncology clinical trials
Nagtegaal, I. D., West, N. P., van Krieken, J. H., & Quirke, P. (2013) · The Journal of Pathology · doi:10.1002/path.4261 · Cited by 23
Digital pathology in drug discovery and development: multisite integration
Potts, S. J. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.06.013 · Cited by 22
Pathology in drug discovery and development
Jubb, A. M., Koeppen, H., & Reis-Filho, J. S. (2013) · The Journal of Pathology · doi:10.1002/path.4290 · Cited by 18
Comprehensive Immunohistochemistry: Digital, Analytical and Integrated
Laurinavičius, A., Plancoulaine, B., Herlin, P., & Laurinavičienė, A. (2016) · Pathobiology · doi:10.1159/000442389 · Cited by 18
Ahern, T. P., Beck, A. H., ROSNER, B. A., Glass, B., Frieling, G. W., Collins, L. C., & Tamimi, R. M. (2016) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2016-204107 · Cited by 18
Validation of Digital Pathology In a Healthcare Environment
Chlipala, E. A., Elin, J., Long, R. E., Schwartz, J. N., & Parwani, A. V. (2011) · Cited by 16
Paluch–Shimon, S., Wolf, I., Goldberg, H., Evron, E., Papa, M. Z., Shabtai, M., Barsuk, D., Yosepovich, A., Modiano, T. M., Catane, R., et al. (2008) · Acta Oncologica · doi:10.1080/02841860802060844 · Cited by 14
Barrera, C., Velu, P., Bera, K., Wang, X., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., Romero, E., & Madabhushi, A. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2018.36.15_suppl.12115 · Cited by 14
Breast cancer histopathology image analysis pipeline for tumor purity estimation
Azimi, V., Chang, Y. H., Thibault, G., Smith, J., Tsujikawa, T., Kukull, B., Jensen, B., Corless, C. L., Margolin, A., & Gray, J. W. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950717 · Cited by 13
Wang, X., Barrera, C., Velu, P., Bera, K., Prasanna, P., Khunger, M., Khunger, A., Velcheti, V., & Madabhushi, A. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2018.36.15_suppl.12061 · Cited by 13
Barriers and facilitators to the introduction of digital pathology for diagnostic work.
Randell, R., Ruddle, R. A., & Treanor, D. E. (2015) · PubMed · Cited by 12
Deciphering the immune microenvironment of a tissue by digital imaging and cognition network
Lopès, A., Casse, A. H., Billard, E., Boulcourt-Sambou, E., Roche, G., Larois, C., Barnich, N., Naimi, S., Bonnet, M., & Dumas, B. (2018) · Scientific Reports · doi:10.1038/s41598-018-34731-x · Cited by 11
Gavrielides, M. A., Ronnett, B. M., VANG, R. S., Barak, S., Lee, E., Staats, P. N., Jenson, E., Skaria, P., Sheikhzadeh, F., Miller, M., et al. (2021) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0579-oa · Cited by 8
Szabó, P. M., Lee, G., Ely, S. A., Baxi, V. A., Pokkalla, H., Elliott, H. L., Wang, D., Glass, B., Kerner, J. K., Wapinski, I. N., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.2019.37.15_suppl.2594 · Cited by 6
Self-citationDuan, C., Montalto, M., Lee, G., Pandya, D., Cohen, D., Chang, H., Tang, H., Agrawal, N., Elliott, H. L., Glass, B., et al. (2020) · Cancer Research · doi:10.1158/1538-7445.am2020-2017 · Cited by 5
Self-citationComputerized Histologic Image-Based Risk Score (IbRiS) Classifier for ER+ Breast Cancer.
Ganesan, S., Madabhushi, A., Basavanhally, A. N., Xu, J., Bhanot, G. V., Barnard, N., & Toppmeyer, D. (2009) · Cancer Research · doi:10.1158/0008-5472.sabcs-09-3046 · Cited by 4
Hedvat, C. V., Lee, G., Baxi, V. A., Dziuba, K., Locke, D., Li, B., & Edwards, R. H. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz269.039 · Cited by 4
Self-citation
Cited by
Sorted by citations
Serrano, D. R., Luciano, F. C., Anaya, B. J., Öngoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., et al. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16101328 · Cited by 594
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Shmatko, A., Ghaffari Laleh, N., Gerstung, M., & Kather, J. N. (2022) · Nature Cancer · doi:10.1038/s43018-022-00436-4 · Cited by 446
Multimodal data fusion for cancer biomarker discovery with deep learning
Steyaert, S., Pizurica, M., Nagaraj, D., Khandelwal, P., Hernandez-Boussard, T., Gentles, A. J., & Gevaert, O. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00633-5 · Cited by 402
High-throughput proteomics: a methodological mini-review
Cui, M., Cheng, C., & Zhang, L. (2022) · Laboratory Investigation · doi:10.1038/s41374-022-00830-7 · Cited by 398
Artificial intelligence for digital and computational pathology
Song, A. H., Jaume, G., Williamson, D. F. K., Lu, M. Y., Vaidya, A., Miller, T. R., & Mahmood, F. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00096-8 · Cited by 356
Metadata
- Authors
- Baxi, V., Edwards, R., Montalto, M., & Saha, S.
- Affiliations
- Bristol-Myers Squibb (United States) (US)
- PathAI (United States) (US)
- Corresponding author
- Baxi, Vipul
- Venue
- Modern Pathology
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 35
- Issue
- 1
- Pages
- 23-32
- ISSN
- 0893-3952 (ISSN-L)
- PMC
- PMC8491759
- OpenAlex
- W3204013916
- Cited by
- 592
- References
- 139
- Citations by year
- 2026: 107 · 2025: 158 · 2024: 175 · 2023: 102 · 2022: 49
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Modern Pathology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Bristol-Myers Squibb
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- digital pathology, artificial intelligence, whole slide imaging, cancer immunotherapy, biomarker discovery, patient selection, precision medicine, tumor microenvironment, companion diagnostics, translational medicine, clinical implementation barriers
- MeSH terms
- Translational Science, Biomedical, Algorithms, Artificial Intelligence, Humans, Pathology, Practice Patterns, Physicians', Biomarkers
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
