Author

N. Ghaffari Laleh

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· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:clinical cohort / trial· 2 Approach:deep learning· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

J. N. Kather· 3 shared C. M. L. Loeffler· 2 shared C. Trautwein· 2 shared H. S. Muti· 2 shared K. Stankova· 2 shared A. Echle· 1 shared A. T. Pearson· 1 shared D. Cifci· 1 shared

Activity by Year

2021-2021 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 32 mutual co-author links

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3 shared papersCancer Biology

J. N. Kather

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

M. van Treeck, D. Cifci, N. Ghaffari Laleh, O. L. Saldanha, C. M. L. Loeffler, K. J. Hewitt, H. S. Muti, A. Echle, T. Seibel, T. P. Seraphin, C. Trautwein, S. Foersch, T. Luedde, D. Truhn, J. N. Kather

DeepMed provides a modular, scalable AI pipeline for predicting molecular and histopathological features from histopathology images, with public benchmark data.

bioRxiv · Dec 19, 2021 · PreprintCancer Biology

Improving Mathematical Models of Cancer throughGame-Theoretic Modelling: A Study in Non-SmallCell Lung Cancer

V. Ardevol Martinez, N. Ghaffari Laleh, M. Salvioli, F. Thuijsman, J. S. Brown, R. Cavill, J. N. Kather, K. Stankova

This study applies a game-theoretic model to 590 Non-Small Cell Lung Cancer patients, showing it better fits tumor dynamics than classical models and predicts treatment outcomes.

bioRxiv · Oct 29, 2021 · PreprintCancer Biology

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