The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
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.
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
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 cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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.
Citations
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
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
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
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
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
A guided tour into subcellular colocalization analysis in light microscopy
Bolte, S., & Cordelières, F. P. (2006) · Journal of Microscopy · doi:10.1111/j.1365-2818.2006.01706.x · Cited by 5,506
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
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
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
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 650
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 551
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 196
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 205
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 155
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
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
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 100
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
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 103
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 91
Pasrija, P., Jha, P. K., Upadhyaya, P., Khan, M. S., & Chopra, M. (2022) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026622666220701091339 · Cited by 92
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
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 87
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 82
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
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 78
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 74
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 72
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 66
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
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
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 71
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
Tissue clearing and 3D imaging in developmental biology
Prado, A. V., & Renier, N. (2021) · Development · doi:10.1242/dev.199369 · Cited by 72
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 65
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
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 60
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
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
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
Mukund, A., Afridi, M. A., Karolak, A. M., Park, M. A., Permuth, J. B., & Rasool, G. (2024) · Cancers · doi:10.3390/cancers16122240 · Cited by 59
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
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 53
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 43
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 46
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
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 45
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
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
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
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 38
Self-citationTang, Y., & Cao, Y. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13030422 · Cited by 42
Circular RNA and tumor microenvironment
Song, H., Liu, Q., & Liao, Q. (2020) · Cancer Cell International · doi:10.1186/s12935-020-01301-z · Cited by 37
Wang, Q., Yuan, L., Ding, X., & Zhou, Z. (2021) · Clinical and Applied Thrombosis/Hemostasis · doi:10.1177/10760296211021162 · Cited by 37
Parra‐Damas, A., & Saura, C. A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00914 · Cited by 35
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 31
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 35
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
Animal models of brain metastasis
Miarka, L., & Valiente, M. (2021) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdab115 · Cited by 37
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 41
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
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 32
Optical Tissue Clearing: Illuminating Brain Function and Dysfunction
Liang, X., & Luo, H. (2021) · Theranostics · doi:10.7150/thno.53979 · Cited by 28
Shenoy, S. (2020) · Surgical Oncology · doi:10.1016/j.suronc.2020.04.017 · Cited by 29
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
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
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
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 27
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 29
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
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
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
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
Hofmann, J. I., Gadjalova, I., Mishra, R., Ruland, J., & Keppler, S. J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2020.599495 · Cited by 26
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
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
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 21
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
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
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
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
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 23
Hall, M. S., Decker, J. T., & Shea, L. D. (2020) · Biomaterials · doi:10.1016/j.biomaterials.2020.120189 · Cited by 20
Xu, Z., Wang, W., Lou, W., & Liu, L. (2022) · Journal of Pancreatology · doi:10.1097/jp9.0000000000000093 · Cited by 18
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
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 18
Ma, K. L., Jiang, Q., Yang, Y., & Zhang, F. (2024) · Journal of Materials Chemistry B · doi:10.1039/d4tb01957a · Cited by 19
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 20
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 19
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
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
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
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 17
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
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 15
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 16
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
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 15
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 12
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
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 13
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
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 13
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 10
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
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 12
Xun, D., Chen, D., Zhou, Y., Lauschke, V. M., Wang, R., & Wáng, Y. (2022) · iScience · doi:10.1016/j.isci.2022.105506 · Cited by 13
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
Metadata
- 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
