Author

J. Poleszczuk

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:clinical cohort / trial· 1 Approach:deep learning· 1 Organism:human· 1 Assay:imaging· 1 Organism:mouse· 1 Approach:statistical model· 1

Frequent Co-Authors

A. T. Pearson· 1 shared B. V. Jardim Perassi· 1 shared C. M. L. Loeffler· 1 shared C. Trautwein· 1 shared D. R. Reed· 1 shared G. V. Martinez· 1 shared H. Enderling· 1 shared H. S. Muti· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperCancer Biology

A. T. Pearson

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Author profile →

2 papers

Recent·Most cited

Classical Mathematical Models for Prediction of Response to Chemotherapy and Immunotherapy

N. Ghaffari Laleh, C. M. L. Loeffler, J. Grajek, K. Stankova, A. T. Pearson, H. S. Muti, C. Trautwein, H. Enderling, J. Poleszczuk, J. N. Kather

This study evaluates classical mathematical models for predicting chemotherapy and immunotherapy responses using real-world patient tumor volume data, finding the Gompertz model provides the best balance between fit and complexity.

bioRxiv · Oct 23, 2021 · PreprintCancer Biology

Targeting hypoxic habitats with hypoxia pro-drug evofosfamide in preclinical models of sarcoma

B. V. Jardim Perassi, W. Mu, S. Huang, M. R. Tomaszewski, J. Poleszczuk, M. A. Abdalah, M. M. Budzevich, W. Dominguez-Viqueira, D. R. Reed, M. M. Bui, J. O. Johnson, G. V. Martinez, R. J. Gillies

This paper develops deep-learning models to identify hypoxic tumor regions using MRI and histology, showing their effectiveness in predicting hypoxia and guiding TH-302 therapy response in sarcoma models.

bioRxiv · Oct 6, 2020 · PreprintCancer Biology