Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body
Open accessAbstract
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.
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?
Tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking.
“However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [6]
Several approaches for image analysis of LSFM datasets have been introduced in recent years, but these methods are typically limited to selected organs and do not provide integrated whole-body quantification of elongated structures.
“Although several approaches for image analysis of light-sheet fluorescence microscopy (LSFM) datasets have been introduced in recent years6–10, these methods are typically limited to selected organs and do not provide integrated whole-body quantification of elongated structures.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [6]
MouseMapper performs true 3D sliding-window inference on uncropped, multiterabyte datasets, rather than relying on 2D projections or precropped volumes.
“In contrast to previous methods for whole-body analysis6,10,18, MouseMapper performs true 3D sliding-window inference on uncropped, multiterabyte datasets, rather than relying on 2D projections or precropped volumes, ensuring unbiased system-wide screening of anatomical changes for elongated structures.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [6]
Previous methods such as DeepMACT, DELiVR, AIMOS and SCP-Nano targeted a limited set of biological systems.
“Previous methods such as DeepMACT6 (tumour metastasis quantification in manually annotated organs), DELiVR7 (neuronal activation and microglial somata quantification and mapping in whole brain), AIMOS18 (segmentation of six organs for CT/LSFM) and SCP-Nano10 (nanocarrier targeted cell quantification in manually annotated organs) targeted a limited set of biological systems.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [6]
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · 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
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 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
A guided tour into subcellular colocalization analysis in light microscopy
Bolte, S., & Cordelières, F. (2006) · Journal of Microscopy · doi:10.1111/j.1365-2818.2006.01706.x · Cited by 5,238 · see citing works
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
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 · see citing works
Emerging Biological Principles of Metastasis
Lambert, A. W., Pattabiraman, D. R., & Weinberg, R. A. (2017) · Cell · doi:10.1016/j.cell.2016.11.037 · Cited by 3,075 · see citing works
Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse
Hingorani, S. R., Petricoin, E. F., Maitra, A., Rajapakse, V., King, C., Jacobetz, M. A., Ross, S., Conrads, T. P., Veenstra, T. D., Hitt, B. A., et al. (2003) · Cancer Cell · doi:10.1016/s1535-6108(03)00309-x · Cited by 2,471 · see citing works
Ryan, D. P., Hong, T. S., & Bardeesy, N. (2014) · New England Journal of Medicine · doi:10.1056/nejmra1404198 · Cited by 2,348 · see citing works
Molecular imaging in living subjects: seeing fundamental biological processes in a new lightPreprint
Massoud, T. F., & Gambhir, S. S. (2003) · Genes & Development · doi:10.1101/gad.1047403 · Cited by 2,205 · see citing works
Metastatic colonization by circulating tumour cells
Massagué, J., & Obenauf, A. C. (2016) · Nature · doi:10.1038/nature17038 · Cited by 2,062 · see citing works
U-Net: deep learning for cell counting, detection, and morphometry
Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0261-2 · Cited by 1,912 · see citing works
Going deeper than microscopy: the optical imaging frontier in biology
Ntziachristos, V. (2010) · Nature Methods · doi:10.1038/nmeth.1483 · Cited by 1,896 · see citing works
Zipfel, W. R., Williams, R. M., Christie, R. H., Nikitin, A. Y., Hyman, B. T., & Webb, W. W. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0832308100 · Cited by 1,691 · see citing works
Whole-Brain Imaging with Single-Cell Resolution Using Chemical Cocktails and Computational Analysis
Susaki, E. A., Tainaka, K., Perrin, D., Kishino, F., Tawara, T., Watanabe, T. M., Yokoyama, C., Onoe, H., Eguchi, M., Yamaguchi, S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.03.042 · Cited by 1,301 · see citing works
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069 · see citing works
Three-dimensional imaging of solvent-cleared organs using 3DISCO
Ertürk, A., Becker, K., Jährling, N., Mauch, C. P., Hojer, C. D., Egen, J. G., Hellal, F., Bradke, F., Sheng, M., & Dodt, H. (2012) · Nature Protocols · doi:10.1038/nprot.2012.119 · Cited by 997 · see citing works
Self-citationNext-Generation Machine Learning for Biological Networks
Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018) · Cell · doi:10.1016/j.cell.2018.05.015 · Cited by 974 · see citing works
Single-Cell Phenotyping within Transparent Intact Tissue through Whole-Body Clearing
Yang, B., Treweek, J. B., Kulkarni, R. P., Deverman, B. E., Chen, C., Lubeck, E., Shah, S., Cai, L., & Gradinaru, V. (2014) · Cell · doi:10.1016/j.cell.2014.07.017 · Cited by 933 · see citing works
Deep learning enables cross-modality super-resolution in fluorescence microscopy
Wang, H., Rivenson, Y., Jin, Y., Wei, Z., Gao, R., Günaydın, H., Bentolila, L. A., Kural, C., & Özcan, A. (2018) · Nature Methods · doi:10.1038/s41592-018-0239-0 · Cited by 788 · see citing works
Mining the Wnt pathway for cancer therapeutics
Barker, N., & Clevers, H. C. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2154 · Cited by 746 · see citing works
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images
Christiansen, E. M., Yang, S. J., Ando, D. M., Javaherian, A., Skibinski, G., Lipnick, S., Mount, E., O’Neil, A., Shah, K., Lee, A. K., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.040 · Cited by 635 · see citing works
WNT/TCF Signaling through LEF1 and HOXB9 Mediates Lung Adenocarcinoma Metastasis
Nguyen, D. X., Chiang, A. C., Zhang, X. H.-F., Kim, J. Y., Kris, M. G., Ladanyi, M., Gerald, W. L., & Massagué, J. (2009) · Cell · doi:10.1016/j.cell.2009.04.030 · Cited by 622 · see citing works
Shrinkage-mediated imaging of entire organs and organisms using uDISCO
Pan, C., Cai, R., Quacquarelli, F. P., Ghasemigharagoz, A., Lourbopoulos, A. I., Matryba, P., Plesnila, N., Dichgans, M., Hellal, F., & Ertürk, A. (2016) · Nature Methods · doi:10.1038/nmeth.3964 · Cited by 609 · see citing works
Self-citationWhole-Body Imaging with Single-Cell Resolution by Tissue Decolorization
Tainaka, K., Kubota, S. I., Suyama, T. Q., Susaki, E. A., Perrin, D., Ukai‐Tadenuma, M., Ukai, H., & Ueda, H. R. (2014) · Cell · doi:10.1016/j.cell.2014.10.034 · Cited by 520 · see citing works
Handbook of Optical Biomedical Diagnostics
Tuchin, V. V. (2002) · Medical Entomology and Zoology · Cited by 501 · see citing works
Steiner, D. F., MacDonald, R., Liu, Y., Truszkowski, P., Hipp, J. D., Gammage, C., Thng, F., Peng, L., & Stumpe, M. C. (2018) · American Journal of Surgical Pathology · doi:10.1097/pas.0000000000001151 · Cited by 489 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse AnnotationPreprint
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.06650 · Cited by 488 · see citing works
Cai, R., Pan, C., Ghasemigharagoz, A., Todorov, M. I., Förstera, B., Zhao, S., Bhatia, H. S., Parra-Damas, A., Mrowka, L., Theodorou, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0301-3 · Cited by 470 · see citing works
Self-citationLiu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N. H., Peng, L. H., Hipp, J., & Stumpe, M. C. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0147-oa · Cited by 402 · see citing works
Sevenich, L., Bowman, R. L., Mason, S. D., Quail, D. F., Rapaport, F., Elie, B. T., Brogi, E., Brastianos, P. K., Hahn, W. C., Holsinger, L. J., et al. (2014) · Nature Cell Biology · doi:10.1038/ncb3011 · Cited by 384 · see citing works
Eser, S., Reiff, N., Messer, M., Seidler, B., Gottschalk, K., Dobler, M., Hieber, M., Arbeiter, A., Klein, S., Kong, B., et al. (2013) · Cancer Cell · doi:10.1016/j.ccr.2013.01.023 · Cited by 360 · see citing works
Self-citationvon Burstin, J., Eser, S., Paul, M. C., Seidler, B., Brandl, M., Messer, M., von Werder, A., Schmidt, A., Mages, J., Pagel, P., et al. (2009) · Gastroenterology · doi:10.1053/j.gastro.2009.04.004 · Cited by 356 · see citing works
Self-citationBiodistribution Mechanisms of Therapeutic Monoclonal Antibodies in Health and Disease
Tabrizi, M. A., Bornstein, G. G., & Suria, H. (2009) · The AAPS Journal · doi:10.1208/s12248-009-9157-5 · Cited by 307 · see citing works
Whole-Body Profiling of Cancer Metastasis with Single-Cell Resolution
Kubota, S. I., Takahashi, K., Nishida, J., Morishita, Y., Ehata, S., Tainaka, K., Miyazono, K., & Ueda, H. R. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.06.010 · Cited by 249 · see citing works
A next-generation dual-recombinase system for time- and host-specific targeting of pancreatic cancer
Schönhuber, N., Seidler, B., Schuck, K., Veltkamp, C., Schachtler, C., Zukowska, M., Eser, S., Feyerabend, T. B., Paul, M. C., Eser, P., et al. (2014) · Nature Medicine · doi:10.1038/nm.3646 · Cited by 238 · see citing works
Self-citationIn Vivo Imaging in CancerPreprint
Condeelis, J. S., & Weissleder, R. (2010) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a003848 · Cited by 231 · see citing works
Latest Advances in Molecular Imaging Instrumentation
Pichler, B. J., Wehrl, H. F., & Judenhofer, M. S. (2008) · Journal of Nuclear Medicine · doi:10.2967/jnumed.108.045880 · Cited by 224 · see citing works
Lung Cancer Detection: A Deep Learning Approach
Bhatia, S., Sinha, Y., & Goel, L. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1595-4_55 · Cited by 194 · see citing works
Sullivan, D. P., Winsnes, C. F., Åkesson, L., Hjelmare, M., Wiking, M., Schutten, R., Campbell, L., Leifsson, H., Rhodes, S., Nordgren, A., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4225 · Cited by 179 · see citing works
Imaging preclinical tumour models: improving translational power
de Jong, M., Essers, J., & van Weerden, W. M. (2014) · Nature reviews. Cancer · doi:10.1038/nrc3751 · Cited by 176 · see citing works
A New Mouse Model for the Study of Human Breast Cancer Metastasis
Iorns, E. J., Drews‐Elger, K., Ward, T. M., Dean, S., Clarke, J., Berry, D. L., El‐Ashry, D., & Lippman, M. E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047995 · Cited by 151 · see citing works
A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks
Wang, J., Fang, Z., Lang, N., Yuan, H., Su, M., & Baldi, P. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.03.024 · Cited by 139 · see citing works
Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study
Mavandadi, S., Dimitrov, S., Feng, S. W., Yu, F., Sikora, U., Yaglidere, O., Padmanabhan, S., Nielsen, K., & Özcan, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037245 · Cited by 122 · see citing works
Campbell, J. P., Merkel, A. R., Masood-Campbell, S. K., Elefteriou, F., & Sterling, J. A. R. (2012) · Journal of Visualized Experiments · doi:10.3791/4260 · Cited by 122 · see citing works
Integrating noninvasive molecular imaging into molecular medicine: an evolving paradigm
Massoud, T. F., & Gambhir, S. S. (2007) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2007.03.003 · Cited by 122 · see citing works
Vick, B., Rothenberg, M., Sandhöfer, N., Carlet, M., Finkenzeller, C., Krupka, C., Grunert, M., Trumpp, A., Corbacioglu, S., Ebinger, M., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0120925 · Cited by 99 · see citing works
Self-citationImaging molecular dynamics in vivo – from cell biology to animal models
Timpson, P., McGhee, E. J., & Anderson, K. I. (2011) · Journal of Cell Science · doi:10.1242/jcs.085191 · Cited by 97 · see citing works
von Burstin, J., Eser, S., Seidler, B., Meining, A. G., Bajbouj, M., Mages, J., Lang, R., Kind, A., Schnieke, A., Schmid, R. M., et al. (2008) · International Journal of Cancer · doi:10.1002/ijc.23780 · Cited by 84 · see citing works
Self-citationBattke, C., Kremmer, E., Mysliwietz, J., Gondi, G., Dumitru, C. A., Brandau, S., Lang, S., Vullo, D., Supuran, C. T., & Zeidler, R. (2011) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-011-0980-z · Cited by 75 · see citing works
Self-citationGondi, G., Mysliwietz, J., Hulı́ková, A., Jen, J. P., Swietach, P., Kremmer, E., & Zeidler, R. (2013) · Cancer Research · doi:10.1158/0008-5472.can-13-1110 · Cited by 60 · see citing works
Self-citationvon Neubeck, B., Gondi, G., Riganti, C., Pan, C., Parra‐Damas, A., Scherb, H., Ertürk, A., & Zeidler, R. (2018) · International Journal of Cancer · doi:10.1002/ijc.31607 · Cited by 51 · see citing works
Self-citationVestjens, J. H. M. J., Pepels, M. J. A. E., de Boer, M., Borm, G. F., van Deurzen, C. H. M., van Diest, P. J., van Dijck, J. A. A. M., Adang, E., Nortier, J. W. R., Rutgers, E. J. T., et al. (2012) · Annals of Oncology · doi:10.1093/annonc/mds072 · Cited by 47 · see citing works
Campbell, J. P., Merkel, A. R., Masood-Campbell, S. K., Elefteriou, F., & Sterling, J. A. R. (2012) · Journal of Visualized Experiments · doi:10.3791/4260-v · Cited by 47 · see citing works
Lenk, L., Pein, M., Will, O., Gomez, B. A., Viol, F., Hauser, C., Egberts, J. D., Gundlach, J.-P., Helm, O., Tiwari, S., et al. (2017) · OncoImmunology · doi:10.1080/2162402x.2017.1368603 · Cited by 42 · see citing works
Self-citationWang, N., Bian, C., Wang, Y., Xu, M., Qin, C., Yang, X., Wang, T., Li, A., Shen, D. G., & Ni, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_73 · Cited by 35 · see citing works
Mavandadi, S., Feng, S. W., Yu, F., Dimitrov, S., Nielsen‐Saines, K., Prescott, W. R., & Özcan, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046192 · Cited by 30 · see citing works
Monoclonal Antibodies as Therapeutics in Human Malignancies
Pandey, M., & Mahadevan, D. (2014) · Future Oncology · doi:10.2217/fon.13.197 · Cited by 27 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The updated landscape of tumor microenvironment and drug repurposing
Jin, M.-Z., & Jin, W.-L. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00280-x · Cited by 1,262 · see citing works
Tissue clearing and its applications in neuroscience
Ueda, H. R., Ertürk, A., Chung, K., Gradinaru, V., Chédotal, A., Tomančák, P., & Keller, P. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0250-1 · Cited by 619 · see citing works
Self-citationAspects of the Tumor Microenvironment Involved in Immune Resistance and Drug Resistance
Khalaf, K., Hana, D., Chou, J. T.-T., Singh, C., Mackiewicz, A. A., & Kaczmarek, M. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.656364 · Cited by 526 · see citing works
Distinct molecular profiles of skull bone marrow in health and neurological disorders
Kolabas, Z. I., Kuemmerle, L. B., Perneczky, R., Förstera, B., Ulukaya, S., Ali, M., Kapoor, S., Bartos, L. M., Büttner, M., Çalışkan, Ö. Ş., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.009 · Cited by 186 · see citing works
Self-citationRichardson, D. S., Guan, W., Matsumoto, K., Pan, C., Chung, K., Ertürk, A., Ueda, H. R., & Lichtman, J. W. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00080-9 · Cited by 188 · see citing works
Self-citationCharacterizing and targeting glioblastoma neuron-tumor networks with retrograde tracing
Tetzlaff, S. K., Reyhan, E., Layer, N., Bengtson, C. P., Heuer, A., Schroers, J., Faymonville, A. J., Langeroudi, A. P., Drewa, N., Keifert, E., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.002 · Cited by 147 · see citing works
Obesity-induced inflammation: connecting the periphery to the brain
Le Thuc, O., & García-Cáceres, C. (2024) · Nature Metabolism · doi:10.1038/s42255-024-01079-8 · Cited by 125 · see citing works
Deep learning-enabled multi-organ segmentation in whole-body mouse scans
Schoppe, O., Pan, C., Coronel, J., Mai, H., Rong, Z., Todorov, M. I., Müskes, A., Navarro, F., Li, H., Ertürk, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19449-7 · Cited by 104 · see citing works
Self-citationAntigen retrieval and clearing for whole-organ immunofluorescence by FLASH
Messal, H. A., Almagro, J., Thin, M. Z., Tedeschi, A., Ciccarelli, A., Blackie, L., Anderson, K. I., Miguel‐Aliaga, I., van Rheenen, J., & Behrens, A. (2020) · Nature Protocols · doi:10.1038/s41596-020-00414-z · Cited by 97 · see citing works
A guidebook for DISCO tissue clearing
Molbay, M., Kolabas, Z. I., Todorov, M. I., Ohn, T., & Ertürk, A. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209807 · Cited by 96 · see citing works
Self-citationCurrent methods for studying metastatic potential of tumor cells
Bouchalová, P., & Bouchal, P. (2022) · Cancer Cell International · doi:10.1186/s12935-022-02801-w · Cited by 99 · see citing works
Zaritsky, A., Jamieson, A. R., Welf, E. S., Nevarez, A. J., Cillay, J., Eskiocak, U., Cantarel, B. L., & Danuser, G. M. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.003 · Cited by 92 · see citing works
Pasrija, P., Jha, P. K., Upadhyaya, P., Khan, M. S., & Chopra, M. (2022) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026622666220701091339 · Cited by 86 · see citing works
Whole-mouse clearing and imaging at the cellular level with vDISCO
Cai, R., Kolabas, Z. I., Pan, C., Mai, H., Zhao, S., Kaltenecker, D., Voigt, F. F., Molbay, M., Ohn, T.-L., Vincke, C., et al. (2023) · Nature Protocols · doi:10.1038/s41596-022-00788-2 · Cited by 83 · see citing works
Self-citationTissue clearing to examine tumour complexity in three dimensions
Almagro, J., Messal, H. A., Thin, M. Z., van Rheenen, J., & Behrens, A. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-021-00382-w · Cited by 81 · see citing works
Current challenges in metastasis research and future innovation for clinical translation
Parker, A. L., Benguigui, M., Fornetti, J., Goddard, E. T., Lucotti, S., Insua‐Rodríguez, J., & Wiegmans, A. P. (2022) · Clinical & Experimental Metastasis · doi:10.1007/s10585-021-10144-5 · Cited by 80 · see citing works
Insights and opportunities at the crossroads of cancer and neuroscience
Pan, C., & Winkler, F. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00978-w · Cited by 75 · see citing works
Self-citationDissecting metastasis using preclinical models and methods
Hebert, J. D., Neal, J. W., & Winslow, M. M. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00568-4 · Cited by 75 · see citing works
Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level
Qi, R., & Zou, Q. (2023) · Research · doi:10.34133/research.0050 · Cited by 72 · see citing works
Single-Cell Techniques and Deep Learning in Predicting Drug Response
Wu, Z., Lawrence, P. J., Ma, A., Zhu, J., Xu, D., & Ma, Q. (2020) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.10.004 · Cited by 68 · see citing works
Geng, J., Zhang, X., Prabhu, S., Shahoei, S. H., Nelson, E. R., Swanson, K. S., Anastasio, M. A., & Smith, A. M. (2021) · Science Advances · doi:10.1126/sciadv.abe2480 · Cited by 67 · see citing works
Mukhopadhyay, S., Mahapatra, K. K., Praharaj, P. P., Patil, S., & Bhutia, S. K. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.09.003 · Cited by 67 · see citing works
Li, D., Hu, J., Zhang, L., Li, L., Yin, Q., Shi, J., Guo, H. W., Zhang, Y., & Zhuang, P. (2022) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2022.175260 · Cited by 67 · see citing works
Scalable tissue labeling and clearing of intact human organs
Mai, H., Rong, Z., Zhao, S., Cai, R., Steinke, H., Bechmann, I., & Ertürk, A. (2022) · Nature Protocols · doi:10.1038/s41596-022-00712-8 · Cited by 63 · see citing works
Self-citationLiang, S., Ma, J., Wang, G., Shao, J., Li, J., Deng, H., Wang, C., & Li, W. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.935080 · Cited by 66 · see citing works
Tissue clearing and 3D imaging in developmental biology
Prado, A. V., & Renier, N. (2021) · Development · doi:10.1242/dev.199369 · Cited by 61 · see citing works
Mammalian cell and tissue imaging using Raman and coherent Raman microscopy
Fung, A. A., & Shi, L. (2020) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1501 · Cited by 61 · see citing works
Chen, S., Zhang, N., Jiang, L., Gao, F., Shao, J., Wang, T., Zhang, E., Yu, H., Wang, X., & Zheng, J. (2020) · International Journal of Cancer · doi:10.1002/ijc.33288 · Cited by 57 · see citing works
Wang, X., Xiong, H., Liu, Y., Yang, T., Li, A., Huang, F., Yin, F., Su, L., Liu, L., Li, N., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.108709 · Cited by 57 · see citing works
Zhang, W., Xu, F., Jiang, Y., Mao, C., Zhu, M., Qian, M., Hu, J. J., Zhong, H., Zhou, J., Shi, X., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38009-3 · Cited by 57 · see citing works
Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning
Luo, J., Molbay, M., Chen, Y., Horvath, I., Kadletz, K., Kick, B., Zhao, S., Al-Maskari, R., Singh, I., Ali, M., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02528-1 · Cited by 53 · see citing works
Self-citationZhou, Q., Liu, Q., Wang, Y., Chen, J., Schmid, O., Rehberg, M., & Yang, L. (2024) · Advanced Science · doi:10.1002/advs.202308659 · Cited by 51 · see citing works
Mukund, A., Afridi, M. A., Karolak, A. M., Park, M. A., Permuth, J. B., & Rasool, G. (2024) · Cancers · doi:10.3390/cancers16122240 · Cited by 50 · see citing works
Visualization of human T lymphocyte-mediated eradication of cancer cells in vivo
He, X., Yin, X., Wu, J., Wickström, S. L., Duo, Y., Du, Q., Qin, S., Yao, S., Xu, J., Hosaka, K., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2009092117 · Cited by 48 · see citing works
Visualizing cancer extravasation: from mechanistic studies to drug development
Cheng, X., & Cheng, K. (2020) · Cancer and Metastasis Reviews · doi:10.1007/s10555-020-09942-2 · Cited by 50 · see citing works
Recent advances in self‐immobilizing fluorescent probes for in vivo imaging
Zhang, Y., Lv, X., Wang, Y., Chen, X., Zhang, J., & Su, D. (2024) · Smart Molecules · doi:10.1002/smo.20240031 · Cited by 45 · see citing works
Kiemen, A. L., Damanakis, A. I., Braxton, A. M., He, J. P., Laheru, D. A., Fishman, E. K., Chames, P., Almagro-Pérez, C., Wu, P., Wirtz, D., et al. (2023) · Med · doi:10.1016/j.medj.2022.11.009 · Cited by 42 · see citing works
Machine learning predictive model for severe COVID-19
Kang, J., Chen, T., Luo, H., Luo, Y., Du, G., & Yang, M. J. (2021) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2021.104737 · Cited by 42 · see citing works
RetractedTakahashi, K., Abe, K., Kubota, S. I., Fukatsu, N., Morishita, Y., Yoshimatsu, Y., Hirakawa, S., Kubota, Y., Watabe, T., Ehata, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32848-2 · Cited by 42 · see citing works
Gold Nanoparticle Mediated Multi-Modal CT Imaging of Hsp70 Membrane-Positive Tumors
Kimm, M. A., Shevtsov, M. A., Werner, C., Sievert, W., Wu, Z., Schoppe, O., Menze, B. H., Rummeny, E. J., Proksa, R., Bystrova, O. A., et al. (2020) · Cancers · doi:10.3390/cancers12051331 · Cited by 41 · see citing works
Self-citationIntravital and high-content multiplex imaging of the immune system
Hor, J. L., & Germain, R. N. (2021) · Trends in Cell Biology · doi:10.1016/j.tcb.2021.11.007 · Cited by 40 · see citing works
Virtual reality-empowered deep-learning analysis of brain cells
Kaltenecker, D., Al-Maskari, R., Negwer, M., Hoeher, L., Kofler, F., Zhao, S., Todorov, M., Rong, Z., Paetzold, J. C., Wiestler, B., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02245-2 · Cited by 38 · see citing works
Self-citationDeep 3D histology powered by tissue clearing, omics and AI
Ertürk, A. (2024) · Nature Methods · doi:10.1038/s41592-024-02327-1 · Cited by 37 · see citing works
Self-citationTang, Y., & Cao, Y. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13030422 · Cited by 38 · see citing works
Circular RNA and tumor microenvironment
Song, H., Liu, Q., & Liao, Q. (2020) · Cancer Cell International · doi:10.1186/s12935-020-01301-z · Cited by 36 · see citing works
Wang, Q., Yuan, L., Ding, X., & Zhou, Z. (2021) · Clinical and Applied Thrombosis/Hemostasis · doi:10.1177/10760296211021162 · Cited by 36 · see citing works
Parra‐Damas, A., & Saura, C. A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00914 · Cited by 35 · see citing works
Self-citationSkovbjerg, G., Roostalu, U., Salinas, C. G., Skytte, J. L., Perens, J., Clemmensen, C., Elster, L., Frich, C. K., Hansen, H. H., & Hecksher‐Sørensen, J. (2023) · Neuropharmacology · doi:10.1016/j.neuropharm.2023.109637 · Cited by 34 · see citing works
Revisiting the role of Dcc in visual system development with a novel eye clearing method
Vigouroux, R. J., Cesar, Q., Chédotal, A., & Nguyen-Ba-Charvet, K. T. (2020) · eLife · doi:10.7554/elife.51275 · Cited by 33 · see citing works
Deep learning is widely applicable to phenotyping embryonic development and disease
Naert, T., Çiçek, Ö., Ogar, P., Bürgi, M., Shaidani, N., Kaminski, M. M., Xu, Y., Grand, K., Vujanovic, M., Prata, D., et al. (2021) · Development · doi:10.1242/dev.199664 · Cited by 31 · see citing works
Animal models of brain metastasis
Miarka, L., & Valiente, M. (2021) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdab115 · Cited by 34 · see citing works
Liu, L., Zhang, X., Chai, Y., Zhang, J., Deng, Q., & Chen, X. (2025) · Cell Death and Disease · doi:10.1038/s41419-025-07336-2 · Cited by 32 · see citing works
Buchner, J. A., Kofler, F., Etzel, L., Mayinger, M. C., Christ, S. M., Brunner, T. B., Wittig, A., Menze, B. H., Zimmer, C., Meyer, B., et al. (2022) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2022.11.014 · Cited by 31 · see citing works
Self-citationOrganic solvent‐based tissue clearing techniques and their applications
Zhan, Y., Wu, H., Liu, L., Lin, J., & Zhang, S. (2021) · Journal of Biophotonics · doi:10.1002/jbio.202000413 · Cited by 30 · see citing works
Optical Tissue Clearing: Illuminating Brain Function and Dysfunction
Liang, X., & Luo, H. (2021) · Theranostics · doi:10.7150/thno.53979 · Cited by 28 · see citing works
Shenoy, S. (2020) · Surgical Oncology · doi:10.1016/j.suronc.2020.04.017 · Cited by 26 · see citing works
Multiscale Co‐reconstruction of Lung Architectures and Inhalable Materials Spatial Distribution
Sun, X., Zhang, X., Ren, X., Sun, H., Wu, L., Wang, C., Ye, X., York, P., Gao, Z., Jiang, H., et al. (2021) · Advanced Science · doi:10.1002/advs.202003941 · Cited by 26 · see citing works
Ren, X., Shu, J. H., Wang, J., Guo, Y., Zhang, Y., Yue, L., Yu, H., Chen, W., Chen, Z., Ma, J., et al. (2022) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2022.05.194 · Cited by 26 · see citing works
Sakamoto, D. M., Tamura, I., Yi, B., Hasegawa, S., Saito, Y., Yamada, N., Takakusagi, Y., Kubota, S. I., Kobayashi, M., Harada, H., et al. (2024) · ACS Nano · doi:10.1021/acsnano.3c12716 · Cited by 28 · see citing works
3D imaging for driving cancer discovery
van Ineveld, R. L., van Vliet, E. J., Wehrens, E. J., Alieva, M., & Rios, A. C. (2022) · The EMBO Journal · doi:10.15252/embj.2021109675 · Cited by 25 · see citing works
Yang, L., Liu, Q., Kumar, P., Sengupta, A., Farnoud, A., Shen, R., Trofimova, D., Ziegler, S., Davoudi, N., Doryab, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54267-1 · Cited by 26 · see citing works
Whole-organ analysis of TGF-β-mediated remodelling of the tumour microenvironment by tissue clearing
Kubota, S. I., Takahashi, K., Mano, T., Matsumoto, K., Katsumata, T., Shi, S., Tainaka, K., Ueda, H. R., Ehata, S., & Miyazono, K. (2021) · Communications Biology · doi:10.1038/s42003-021-01786-y · Cited by 24 · see citing works
blob loss: Instance Imbalance Aware Loss Functions for Semantic Segmentation
Kofler, F., Shit, S., Ezhov, I., Fidon, L., Horvath, I., Al-Maskari, R., Li, H., Bhatia, H. S., Loehr, T., Piraud, M., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_58 · Cited by 24 · see citing works
Self-citationEmerging paradigms in metastasis research
Pari, A. A. A., Singhal, M., & Augustin, H. G. (2020) · The Journal of Experimental Medicine · doi:10.1084/jem.20190218 · Cited by 24 · see citing works
Fang, Y., Zhu, D., Wei, J., Qian, L., Qiu, R., Jia, T., Huang, K., Zhao, S., Ouyang, J., Li, M., et al. (2024) · Science Advances · doi:10.1126/sciadv.ado2015 · Cited by 23 · see citing works
Hofmann, J. I., Gadjalova, I., Mishra, R., Ruland, J., & Keppler, S. J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.599495 · Cited by 22 · see citing works
Jiang, M., Lei, S., Zhang, J., Hou, L., Zhang, M., & Luo, Y. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/9322937 · Cited by 22 · see citing works
Gong, X., Zhuang, J., Chong, K. C., Xu, Q., Ling, X., Cao, L., Wu, M., Yang, J. N., & Liu, B. (2024) · Advanced Materials · doi:10.1002/adma.202402853 · Cited by 22 · see citing works
Kostrikov, S., Johnsen, K. B., Braunstein, T. H., Gudbergsson, J. M., Fliedner, F. P., Obara, E. A. A., Hamerlik, P., Hansen, A. E., Kjær, A., Hempel, C., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02275-y · Cited by 20 · see citing works
AI diagnosis of Bethesda category IV thyroid nodules
Yao, J., Zhang, Y., Shen, J., Lei, Z., Xiong, J., Feng, B., Li, X., Li, W., Ou, D., Lu, Y., et al. (2023) · iScience · doi:10.1016/j.isci.2023.108114 · Cited by 21 · see citing works
Kirschnick, N., Drees, D., Redder, E., Erapaneedi, R., da Graça, A. P., Schäfers, M. A., Jiang, X., & Kiefer, F. (2021) · iScience · doi:10.1016/j.isci.2021.102650 · Cited by 20 · see citing works
Umar, H., Rizaner, N., Usman, A. G., Aliyu, M. R., Adun, H. H., Ghali, U. M., Ozsahin, D. U., & Abba, S. I. (2023) · Pharmaceuticals · doi:10.3390/ph16060858 · Cited by 20 · see citing works
Recent advances in intravital microscopy for preclinical research
Giampetraglia, M., & Weigelin, B. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.05.010 · Cited by 20 · see citing works
SOLID: minimizing tissue distortion for brain-wide profiling of diverse architectures
Zhu, J., Liu, X., Liu, Z., Deng, Y., Xu, J., Liu, K., Zhang, R., Meng, X., Fei, P., Yu, T., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52560-7 · Cited by 19 · see citing works
Hall, M. S., Decker, J. T., & Shea, L. D. (2020) · Biomaterials · doi:10.1016/j.biomaterials.2020.120189 · Cited by 20 · see citing works
Xu, Z., Wang, W., Lou, W., & Liu, L. (2022) · Journal of Pancreatology · doi:10.1097/jp9.0000000000000093 · Cited by 18 · see citing works
High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt
Tang, J., Zhang, T., Gong, Z., & Huang, X. (2023) · Bioengineering · doi:10.3390/bioengineering10121424 · Cited by 19 · see citing works
Current Status of Tissue Clearing and the Path Forward in Neuroscience
Zhao, J., Lai, H. M., Qi, Y., He, D., & Sun, H. (2020) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.0c00563 · Cited by 17 · see citing works
Ma, K. L., Jiang, Q., Yang, Y., & Zhang, F. (2024) · Journal of Materials Chemistry B · doi:10.1039/d4tb01957a · Cited by 18 · see citing works
Yu, T., Zhong, X., Li, D., Zhu, J., Tuchin, V. V., & Zhu, D. (2024) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2024.115470 · Cited by 18 · see citing works
Innovations in ex vivo Light Sheet Fluorescence Microscopy
Delgado-Rodriguez, P., Brooks, C. J., Vaquero, J. J., & Muñoz‐Barrutia, A. (2021) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2021.07.002 · Cited by 18 · see citing works
Identifying core MRI sequences for reliable automatic brain metastasis segmentation
Buchner, J. A., Peeken, J. C., Etzel, L., Ezhov, I., Mayinger, M. C., Christ, S. M., Brunner, T. B., Wittig, A., Menze, B. H., Zimmer, C., et al. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109901 · Cited by 17 · see citing works
Self-citationInsights into the mechanobiology of cancer metastasis via microfluidic technologies
Liang, L., Song, X., Zhao, H., & Lim, C. T. (2024) · APL Bioengineering · doi:10.1063/5.0195389 · Cited by 19 · see citing works
Zherebtsov, E., Zajnulina, M., Kandurova, K., Potapova, E., Владимирович, Д. В., Mamoshin, A. V., Sokolovski, S. G., Dunaev, A., & Урихаевич, Р. Э. (2020) · Diagnostics · doi:10.3390/diagnostics10110873 · Cited by 17 · see citing works
Programmable Synthesis of Biobased Materials Using Cell‐Free Systems
Choi, Y., Cho, N., Lee, K., Gwon, D., Lee, J. W., & Lee, J. (2022) · Advanced Materials · doi:10.1002/adma.202203433 · Cited by 16 · see citing works
Nyström, N. N., McRae, S. W., Martínez, F. M., Kelly, J. J., Scholl, T. J., & Ronald, J. A. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-2770 · Cited by 16 · see citing works
Liu, S., Zeng, Y., Xu, Q., Liu, Q., Song, K., Kong, B., & Su, X. (2021) · Cytometry Part A · doi:10.1002/cyto.a.24349 · Cited by 16 · see citing works
Hahn, A., Bode, J., Alexander, A. A., Jazi, K. K., Schregel, K., Schwarz, D. F., Sommerkamp, A. C., Krüwel, T., Abdollahi, A., Wick, W., et al. (2020) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x20961854 · Cited by 15 · see citing works
Tissue clearing may alter emission and absorption properties of common fluorophores
Eliat, F., Sohn, R. E., Renner, H., Kagermeier, T., Volkery, S., Brinkmann, H., Kirschnick, N., Kiefer, F., Grabos, M., Becker, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-09303-9 · Cited by 14 · see citing works
Multi-omics and 3D-imaging reveal bone heterogeneity and unique calvaria cells in neuroinflammationPreprint
Kolabas, Z. I., Kuemmerle, L. B., Perneczky, R., Förstera, B., Büttner, M., Çalışkan, Ö. Ş., Ali, M., Rong, Z., Mai, H., Hummel, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.24.473988 · Cited by 13 · see citing works
Self-citationUnlocking the potential of large-scale 3D imaging with tissue clearing techniques
Susaki, E. A. (2024) · Microscopy · doi:10.1093/jmicro/dfae046 · Cited by 11 · see citing works
Enhancing cardiovascular research with whole-organ imaging
Coronado, P. E. R., & Red‐Horse, K. (2021) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000655 · Cited by 11 · see citing works
Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data
Liu, Y., Gargesha, M., Qutaish, M. Q., Zhou, Z., Qiao, P. L., Lu, Z., & Wilson, D. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-96838-y · Cited by 12 · see citing works
Bauer, N., Beckmann, D., Reinhardt, D., Frost, N., Bobe, S., Erapaneedi, R., Risse, B., & Kiefer, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-52268-0 · Cited by 11 · see citing works
Gao, F., Jiang, L., Guo, T., Lin, J., Xu, W., Lin, Y., Han, Y., Yang, J., Pan, Q., Chen, E., et al. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05382-6 · Cited by 11 · see citing works
3D light-sheet fluorescence microscopy in preclinical and clinical drug discovery
Roostalu, U., Hansen, H. H., & Hecksher‐Sørensen, J. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104196 · Cited by 11 · see citing works
Qu, W., Liu, Q., Jiao, X., Zhang, T., Wang, B., Li, N., Dong, T., & Cui, B. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.623818 · Cited by 11 · see citing works
Taranda, J., Mathew, G., Watrud, K. E., El-Amine, N., Lee, M. F., Elowsky, C., Bludova, A., Avelar, S. E., Nowak, D. G., Wee, T.-L., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110027 · Cited by 11 · see citing works
Xun, D., Chen, D., Zhou, Y., Lauschke, V. M., Wang, R., & Wáng, Y. (2022) · iScience · doi:10.1016/j.isci.2022.105506 · Cited by 11 · see citing works
McElliott, M. C., Al-Suraimi, A., Telang, A. C., Ference-Salo, J. T., Chowdhury, M., Soofi, A. A., Dressler, G. R., & Beamish, J. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-33433-3 · Cited by 11 · see citing works
- Authors
- Pan, C., Schoppe, O., Parra-Damas, A., Cai, R., Todorov, M. I., Gondi, G., von Neubeck, B., Böğürcü-Seidel, N., Seidel, S., Sleiman, K., Veltkamp, C., Förstera, B., Mai, H., Rong, Z., Trompak, O., Ghasemigharagoz, A., Reimer, M. A., Cuesta, A. M., Coronel, J., Jeremias, I., Saur, D., Acker-Palmer, A., Acker, T., Garvalov, B. K., Menze, B., Zeidler, R., & Ertürk, A.
- Affiliations
- Helmholtz Munich (DE)
- TUM Klinikum (DE)
- Goethe University Frankfurt (DE)
- German Cancer Research Center (DE)
- Justus-Liebig-Universität Gießen (DE)
- LMU Klinikum (DE)
- Technical University of Munich (DE)
- Corresponding author
- Ertürk, Ali
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 179
- Issue
- 7
- Pages
- 1661-1676.e19
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC7591821
- OpenAlex
- W2995153909
- Cited by
- 206
- References
- 62
- Citations by year
- 2026: 14 · 2025: 16 · 2024: 33 · 2023: 28 · 2022: 37 · 2021: 49 · 2020: 29
- FWCI
- 13.92
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Site cant be reached (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Vascular Dementia Research Foundation
- Synergy Excellence Cluster Munich
- Helmholtz-Center for Environment Health
- Fritz Thyssen Stiftung
- Deutsche Forschungsgemeinschaft
- German Federal Ministry of Education and Research
- DFG (SFB 1321 P06, S01)
- European Research Council (CoG 648521)
- Excellence Cluster Cardio-Pulmonary Institute
- ERC (Consolidator Grant 681524)
- Collaborative Research Center 1243 (project A05)
- NVIDIA (Titan XP)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- metastasis, disseminated tumor cells, tissue distribution, deep learning image analysis, therapeutic monoclonal antibodies, breast cancer, lung cancer, pancreatic cancer, organotropism
- MeSH terms
- Deep Learning, Animals, Antibodies, Diagnosis, Computer-Assisted, Drug Therapy, Computer-Assisted, Humans, Mice, Inbred C57BL, Mice, Nude, Neoplasm Metastasis, Neoplasms, Software, Mice, SCID, Mice, Tumor Microenvironment, MCF-7 Cells
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
