Pearson, Alexander T.

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

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Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine

Academic integrity and plagiarismAcademic Writing and Publishing

Cited by 1,099

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022) · npj Digital Medicine

Academic integrity and plagiarismAcademic Writing and Publishing

Cited by 1,099

Deep learning in cancer pathology: a new generation of clinical biomarkers

Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021) · British Journal of Cancer

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 667

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Echle, A., Grabsch, H. I., Quirke, P., van den Brandt, P. A., West, N. P., Hutchins, G. G. A., Heij, L. R., Tan, X., Richman, S. D., Krause, J., et al. (2020) · Gastroenterology

Genetic factors in colorectal cancerAI in cancer detection

Cited by 376

Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., et al. (2024) · Annals of Oncology

Cancer Immunotherapy and BiomarkersRadiomics and Machine Learning in Medical Imaging

Cited by 340

The impact of site-specific digital histology signatures on deep learning model accuracy and bias

Howard, F. M., Dolezal, J., Kochanny, S., Schulte, J., Chen, H., Heij, L., Huo, D., Nanda, R., Olopade, O. I., Kather, J. N., et al. (2021) · Nature Communications

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 330

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology

Dolezal, J. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S., Cody, B., Mansfield, A. S., Rakshit, S., Bansal, R., Bois, M. C., et al. (2022) · Nature Communications

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 147

Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

Muti, H. S., Heij, L. R., Keller, G., Kohlruss, M., Langer, R., Dislich, B., Cheong, J.-H., Kim, Y.-W., Kim, H., Kook, M.-C., et al. (2021) · The Lancet Digital Health

AI in cancer detectionGastric Cancer Management and Outcomes

Cited by 126

Evaluation of large language models as a diagnostic aid for complex medical cases

Ríos-Hoyo, A., Shan, N. L., Li, A., Pearson, A. T., Pusztai, L., & Howard, F. M. (2024) · Frontiers in Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 66

Therapeutic inhibition of the MDM2-p53 interaction prevents recurrence of adenoid cystic carcinomas

Nör, F., Warner, K. A., Zhang, Z., Acasigua, G. A., Pearson, A. T., Kerk, S. A., Helman, J. I., Sant'Ana Filho, M., Wang, S., & Nör, J. E. (2017) · Clinical Cancer Research

Cancer-related Molecular PathwaysSalivary Gland Tumors Diagnosis and Treatment

Cited by 37

Association of immune-related adverse events (irAEs) with improved response, progression-free survival, and overall survival for patients with metastatic head and neck cancer receiving anti-PD-1 therapy.

Foster, C. C., Kochanny, S., Khattri, A., Acharya, R., Dekker, A., Tan, Y.-H. C., Klema, E., Brisson, R. J., Saloura, V., Pearson, A. T., et al. (2018) · Journal of Clinical Oncology

Cancer Immunotherapy and BiomarkersHead and Neck Cancer Studies

Cited by 28

Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit

Shamai, G., Cohen, S., Binenbaum, Y., Sabo, E., Cretu, A., Mayer, C., Barshack, I., Goldman, T., Bar-Sela, G., Polónia, A., et al. (2025) · medRxiv

AI in cancer detectionBreast Cancer Treatment Studies

Cited by 3