Utikal, Jochen

Genomic correlates of response to CTLA-4 blockade in metastatic melanoma

Van Allen, E. M., Miao, D., Schilling, B., Shukla, S. A., Blank, C., Zimmer, L., Sucker, A., Hillen, U., Geukes Foppen, M. H., Goldinger, S. M., et al. (2015) · Science

Cancer Immunotherapy and BiomarkersCutaneous Melanoma Detection and Management

Cited by 2,839

Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer

Sahin, U., Derhovanessian, E., Miller, M., Kloke, B.-P., Simon, P., Löwer, M., Bukur, V., Tadmor, A. D., Luxemburger, U., Schrörs, B., et al. (2017) · Nature

Immunotherapy and Immune ResponsesCancer Immunotherapy and Biomarkers

Cited by 2,488

Directly reprogrammed fibroblasts show global epigenetic remodeling and widespread tissue contribution

Maherali, N., Sridharan, R., Xie, W., Utikal, J., Eminli, S., Arnold, K., Stadtfeld, M., Yachechko, R., Tchieu, J., Jaenisch, R., et al. (2007) · Cell stem cell

Pluripotent Stem Cells ResearchEpigenetics and DNA Methylation

Cited by 1,725

Induced pluripotent stem cells generated without viral integration

Stadtfeld, M., Nagaya, M., Utikal, J., Weir, G., & Hochedlinger, K. (2008) · Science

Pluripotent Stem Cells ResearchCRISPR and Genetic Engineering

Cited by 1,683

An RNA vaccine drives immunity in checkpoint-inhibitor-treated melanoma

Sahin, U., Oehm, P., Derhovanessian, E., Jabulowsky, R. A., Vormehr, M., Gold, M., Maurus, D., Schwarck-Kokarakis, D., Kuhn, A. N., Omokoko, T., et al. (2020) · Nature

Immunotherapy and Immune ResponsesCancer Immunotherapy and Biomarkers

Cited by 1,052

Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma

Liu, D., Schilling, B., Liu, D., Sucker, A., Livingstone, E., Jerby-Arnon, L., Zimmer, L., Gutzmer, R., Satzger, I., Loquai, C., et al. (2019) · Nature Medicine

Cancer Immunotherapy and BiomarkersCutaneous Melanoma Detection and Management

Cited by 1,039

A High-Efficiency System for the Generation and Study of Human Induced Pluripotent Stem Cells

Maherali, N., Ahfeldt, T., Rigamonti, A., Utikal, J., Cowan, C., & Hochedlinger, K. (2008) · Cell Stem Cell

Pluripotent Stem Cells ResearchMesenchymal stem cell research

Cited by 563

Deep neural networks are superior to dermatologists in melanoma image classification

Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., Weichenthal, M., Klode, J., Schadendorf, D., Holland-Letz, T., et al. (2019) · European Journal of Cancer

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 415

Myeloid Cells and Related Chronic Inflammatory Factors as Novel Predictive Markers in Melanoma Treatment with Ipilimumab

Gebhardt, C., Sevko, A., Jiang, H., Lichtenberger, R., Reith, M., Tarnanidis, K., Holland-Letz, T., Umansky, L., Beckhove, P., Sucker, A., et al. (2015) · Clinical Cancer Research

Cancer Immunotherapy and BiomarkersImmune cells in cancer

Cited by 367

Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

Hekler, A., Utikal, J. S., Enk, A. H., Solass, W., Schmitt, M., Klode, J., Schadendorf, D., Sondermann, W., Franklin, C., Bestvater, F., et al. (2019) · European Journal of Cancer

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 285

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 267

Pathologist-level classification of histopathological melanoma images with deep neural networks

Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Schadendorf, D., Jansen, P., Franklin, C., Holland-Letz, T., Krahl, D., et al. (2019) · European Journal of Cancer

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 231

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Jutzi, T. B., Krieghoff-Henning, E. I., Holland-Letz, T., Utikal, J. S., Hauschild, A., Schadendorf, D., Sondermann, W., Fröhling, S., Hekler, A., Schmitt, M., et al. (2020) · Frontiers in Medicine

Cutaneous Melanoma Detection and ManagementArtificial Intelligence in Healthcare and Education

Cited by 196

Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark

Brinker, T. J., Hekler, A., Hauschild, A., Berking, C., Schilling, B., Enk, A. H., Haferkamp, S., Karoglan, A., von Kalle, C., Weichenthal, M., et al. (2019) · European Journal of Cancer

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 157

Opposing roles of eosinophils in cancer

Simon, S. C. S., Utikal, J., & Umansky, V. (2019) · Cancer Immunology, Immunotherapy

Immune cells in cancerCancer Immunotherapy and Biomarkers

Cited by 128

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification

Höhn, J., Krieghoff-Henning, E., Jutzi, T. B., von Kalle, C., Utikal, J. S., Meier, F., Gellrich, F. F., Hobelsberger, S., Hauschild, A., Schlager, J. G., et al. (2021) · European Journal of Cancer

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 106

Molecular genetics of xeroderma pigmentosum variant

Gratchev, A., Strein, P., Utikal, J., & Goerdt, S. (2003) · Experimental Dermatology

DNA Repair MechanismsGenetic and rare skin diseases.

Cited by 83

Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists

Brinker, T. J., Schmitt, M., Krieghoff-Henning, E. I., Barnhill, R., Beltraminelli, H., Braun, S. A., Carr, R., Fernandez-Figueras, M.-T., Ferrara, G., Fraitag, S., et al. (2022) · Journal of the American Academy of Dermatology

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 70

Effects of Label Noise on Deep Learning-Based Skin Cancer Classification

Hekler, A., Kather, J. N., Krieghoff-Henning, E., Utikal, J. S., Meier, F., Gellrich, F. F., Upmeier zu Belzen, J., French, L., Schlager, J. G., Ghoreschi, K., et al. (2020) · Frontiers in Medicine

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 58

FOXD1 promotes dedifferentiation and targeted therapy resistance in melanoma by regulating the expression of connective tissue growth factor

Sun, Q., Novak, D., Hüser, L., Poelchen, J., Wu, H., Granados, K., Federico, A., Liu, K., Steinfass, T., Vierthaler, M., et al. (2021) · International Journal of Cancer

Melanoma and MAPK PathwaysCutaneous Melanoma Detection and Management

Cited by 20

A 3-Dimensional Histology Computer Model of Malignant Melanoma and Its Implications for Digital Pathology

Kurz, A., Krahl, D., Kutzner, H., Barnhill, R., Perasole, A., Figueras, M. T. F., Ferrara, G., Braun, S. A., Starz, H., Llamas-Velasco, M., et al. (2023) · European Journal of Cancer

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 10