Longitudinal Phenotyping of Circulating Tumor Cells Using a Scalable Deep Learning Framework
- DOI
- 10.1158/1078-0432.ccr-26-0754
- Published
- 2026-09-10
- Container
- Clinical Cancer Research
- Publisher
- American Association for Cancer Research (AACR)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1158/1078-0432.ccr-26-0754,
title = {Longitudinal Phenotyping of Circulating Tumor Cells Using a Scalable Deep Learning Framework},
author = {Matthew L. Bootsma and Marina N. Sharifi and Jamie M. Sperger and Jennifer L. Schehr and William M. Stump and Hamza Bakhtiar and Matthew Mannino and Viridiana Carreno and Alex H. Chang and Charlotte Stahlfeld and Emily Abella and Muhammad Dar and Kaitlin Durnen and Hannah M. Krause and David Gallo and Amy K. Taylor and Petros Grivas and Pedro C. Barata and Nan Sethakorn and Ticiana A. Leal and Cristina I. Truica and Ruth M. O’Regan and Xiao X. Wei and William A. Hall and Hamid Emamekhoo and Christos E. Kyriakopoulos and David F. Jarrard and Anthony Serritella and Vincent T. Ma and Rana R. McKay and Kari B. Wisinski and Scott Tagawa and Scott M. Dehm and Joshua M. Lang and Shuang G. Zhao},
year = {2026},
journal = {Clinical Cancer Research},
doi = {10.1158/1078-0432.ccr-26-0754},
url = {https://doi.org/10.1158/1078-0432.ccr-26-0754}
}RIS
TY - JOUR TI - Longitudinal Phenotyping of Circulating Tumor Cells Using a Scalable Deep Learning Framework AU - Matthew L. Bootsma AU - Marina N. Sharifi AU - Jamie M. Sperger AU - Jennifer L. Schehr AU - William M. Stump AU - Hamza Bakhtiar AU - Matthew Mannino AU - Viridiana Carreno AU - Alex H. Chang AU - Charlotte Stahlfeld AU - Emily Abella AU - Muhammad Dar AU - Kaitlin Durnen AU - Hannah M. Krause AU - David Gallo AU - Amy K. Taylor AU - Petros Grivas AU - Pedro C. Barata AU - Nan Sethakorn AU - Ticiana A. Leal AU - Cristina I. Truica AU - Ruth M. O’Regan AU - Xiao X. Wei AU - William A. Hall AU - Hamid Emamekhoo AU - Christos E. Kyriakopoulos AU - David F. Jarrard AU - Anthony Serritella AU - Vincent T. Ma AU - Rana R. McKay AU - Kari B. Wisinski AU - Scott Tagawa AU - Scott M. Dehm AU - Joshua M. Lang AU - Shuang G. Zhao PY - 2026 JO - Clinical Cancer Research DO - 10.1158/1078-0432.ccr-26-0754 UR - https://doi.org/10.1158/1078-0432.ccr-26-0754 ER -
APA
Bootsma, M. L., Sharifi, M. N., Sperger, J. M., Schehr, J. L., Stump, W. M., Bakhtiar, H., Mannino, M., Carreno, V., Chang, A. H., Stahlfeld, C., Abella, E., Dar, M., Durnen, K., Krause, H. M., Gallo, D., Taylor, A. K., Grivas, P., Barata, P. C., Sethakorn, N., Leal, T. A., Truica, C. I., O’Regan, R. M., Wei, X. X., Hall, W. A., Emamekhoo, H., Kyriakopoulos, C. E., Jarrard, D. F., Serritella, A., Ma, V. T., McKay, R. R., Wisinski, K. B., Tagawa, S., Dehm, S. M., Lang, J. M., & Zhao, S. G. (2026). Longitudinal Phenotyping of Circulating Tumor Cells Using a Scalable Deep Learning Framework. Clinical Cancer Research. https://doi.org/10.1158/1078-0432.ccr-26-0754
Source records
- crossref · retrieved 2026-09-26T11:48:18.096Z