Research Focus:

• Application of large language models as an information source and decision-support system
• Multimodal foundation models
• AI-based analysis for patients with brain metastases (AURORA Multicenter Study of the AG Stereotaxy of DEGRO)
• AI-based analysis of patients with anal carcinoma (DKTK Multicenter Study)
• AI-based analysis of patients with soft tissue sarcomas
• AI-based analysis of patients with prostate carcinoma (Co-IMPACT consortium)
• Development and improvement of neural networks for tumor and risk organ segmentation
• Prediction of side effects of radiation therapy

Current team members:

• Dr. med. Lucas Etzel (Physician Scientist)
• Dr. med. Mai Nguyen (Physician Scientist)
• Dr. med. Dr. rer. nat. Kim Kraus (Physician Scientist)
• Stefan Fischer (MSc; PhD Student)
• Johannes Kiechle (MSc; PhD Student)
• Can Erdur (MSc; PhD Student)
• Lukas Reuter (Medical Doctoral Thesis)
• Danai Pletzer (Medical Doctoral Thesis)
• Franziska Duschinger (Medical Doctoral Thesis)
• Ina Riegger (Medical Doctoral Thesis)
• Mohamed Shouman (Medical Doctoral Thesis)
• Nora Windeler (Medical Doctoral Thesis)
• Josef Buchner (Medical Doctoral Thesis)
• Maksym Oreshko (Medical Doctoral Thesis)
• Annika Domres (Medical Doctoral Thesis)

Selected publications:

• Peeken JC*, Etzel L*, Tomov T et al. Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas. Radiother Oncol. 2024 Aug;197:110338. doi: https://doi.org/10.1016/j.radonc.2024.110338. *shared authorship.
• Buchner JA, Kofler F, Mayinger M, ... , Peeken JC. Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy. Neuro Oncol. 2024 Sep 5;26(9):1638-1650. https://doi.org/10.1093/neuonc/noae098.
• Kraus KM, Oreshko M, Schnabel JA, ..., Peeken JC. Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation. Lung Cancer. 2024 Mar;189:107507. doi: https://doi.org/10.1016/j.lungcan.2024.107507.
• Zamboglou C*, Peeken JC*, Janbain A, et al. Development and Validation of a Multi-institutional Nomogram of Outcomes for PSMA-PET-Based Salvage Radiotherapy for Recurrent Prostate Cancer. JAMA Netw Open [Internet]. 2023; 6: e2314748. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2805173, * shared authorship.
• Spohn SKB, Schmidt-Hegemann N-S, Ruf J, ..., Peeken JC. Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy. Eur J Nucl Med Mol Imaging. 2023; Available at: https://doi.org/10.1007/s00259-023-06195-3.
• Buchner JA, Kofler F, Etzel L, ..., Peeken JC. Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study. Radiother Oncol. 2023; 178: 109425. available at: https://doi.org/10.1016/j.radonc.2022.11.014
• Spohn SKB, Farolfi A, Schandeler S, ..., Peeken JC. The maximum standardized uptake value in patients with recurrent or persistent prostate cancer after radical prostatectomy and PSMA-PET-guided salvage radiotherapy-a multicenter retrospective analysis. Eur J Nucl Med Mol Imaging. 2022 Dec;50(1):218-227. doi: https://doi.org/10.1007/s00259-022-05931-5.
• Peeken JC, Asadpour R, Specht K, et al. MRI-based Delta-Radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy. Radiother Oncol. 2021 Nov;164:73-82. https://doi.org/10.1016/j.radonc.2021.08.023.
• Peeken JC, Shouman MA, Kroenke M et al. A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients. Eur J Nucl Med Mol Imaging. 2020;47(13):2968-2977. https://doi.org/10.1007/s00259-020-04864-1.
• Peeken JC, Molina-Romero M, Diehl C et al. Deep learning derived tumor infiltration maps for personalized target definition in glioblastoma radiotherapy. Radiother Oncol. 2019;138:166-172. https://doi.org/10.1016/j.radonc.2019.06.031.