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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.
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“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]
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“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]
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“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]
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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
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