Artificial Intelligence Research Group
Source: https://radioonkologie.mri.tum.de/en/artificial-intelligence-research-group Parent: https://radioonkologie.mri.tum.de/en
Radiotherapy
Artificial Intelligence in radiation oncology research group
Use of AI in radiation oncology: Our research group develops AI models for prognosis, therapy prediction and segmentation in cancer.
- artificial-intelligence-research-group
Artificial Intelligence Research Group
The application of artificial intelligence (AI) methods opens up new possibilities in clinical medicine. In our interdisciplinary research group, we utilize state-of-the-art AI models to address clinically relevant questions in (radio)oncology. We develop methods to enhance the accuracy and safety of large language models (LLMs) in clinical applications. Furthermore, we evaluate the potential of multimodal AI models that can process various types of data. Another focus of our work lies in the analysis of medical imaging data (CT, MRI, and PET) for the non-invasive characterization of biological tissues. Our models aim to improve prognostic predictions for patients, assess therapy response, and determine non-invasive molecular-pathological properties. Additionally, we explore the added value of AI methods in medical therapy planning.
Head of the Artificial Intelligence Research Group\ is Priv.-Doz. Dr. med. Jan Peeken
.
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.
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Prof. Dr. med. Jan Peeken
Managing Senior Physician
Prof. Dr. med. Jan Peeken
Managing Senior Physician, Head of Research Group
Head of Research Group:
Telefon: +49 89 41404501
E-Mail: jan.peeken@tum.de