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doi:10.22028/D291-43712 | Title: | Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma - knowledge obtained from the CheckRad-CD8 trial |
| Author(s): | Hecht, Markus Frey, Benjamin Gaipl, Udo S. Tianyu, Xie Eckstein, Markus Donaubauer, Anna-Jasmina Klautke, Gunther Illmer, Thomas Fleischmann, Maximilian Laban, Simon Hautmann, Matthias G. Tamaskovics, Bálint Brunner, Thomas B. Becker, Ina Zhou, Jian-Guo Hartmann, Arndt Fietkau, Rainer Iro, Heinrich Döllinger, Michael Gostian, Antoniu-Oreste Kist, Andreas M. |
| Language: | English |
| Title: | Neoplasia |
| Volume: | 49 |
| Publisher/Platform: | Stockton Press |
| Year of Publication: | 2024 |
| Free key words: | Chemotherapy Immunotherapy HNSCC Induction therapy Immune phenotyping |
| DDC notations: | 610 Medicine and health |
| Publikation type: | Journal Article |
| Abstract: | Individual prediction of treatment response is crucial for personalized treatment in multimodal approaches against head-and-neck squamous cell carcinoma (HNSCC). So far, no reliable predictive parameters for treatment schemes containing immunotherapy have been identified. This study aims to predict treatment response to induction chemo-immunotherapy based on the peripheral blood immune status in patients with locally advanced HNSCC. |
| DOI of the first publication: | 10.1016/j.neo.2023.100953 |
| URL of the first publication: | https://www.sciencedirect.com/science/article/pii/S1476558623000775 |
| Link to this record: | urn:nbn:de:bsz:291--ds-437128 hdl:20.500.11880/39160 http://dx.doi.org/10.22028/D291-43712 |
| ISSN: | 1476-5586 1522-8002 |
| Date of registration: | 11-Dec-2024 |
| Faculty: | M - Medizinische Fakultät |
| Department: | M - Radiologie |
| Professorship: | M - Keiner Professur zugeordnet |
| Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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|---|---|---|---|---|
| 1-s2.0-S1476558623000775-main.pdf | 3,64 MB | Adobe PDF | View/Open |
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