PD Dr. med. Jan C. Peeken

Group Leader, Artificial Intelligence and Radiomics in Radiation Oncology, Institute of Radiation Medicine (IRM)

PD Dr. med. Jan C. Peeken

Group Leader, Artificial Intelligence and Radiomics in Radiation Oncology, Institute of Radiation Medicine (IRM)

Click & View

Career

Dr. Peeken studied medicine at the University of Freiburg, Freiburg im Breisgau, Germany, with intermittent studies abroad at The University of Nice, Nice, France, and the Harvard Medical School, Boston, MA, USA. He received the M.D. degree for investigation in the pathophysiology of myeloproliferative disorders in molecular hematology, in 2018, and the Habilitation degree from the Medical Faculty, Technical University of Munich, Munich, Germany, in 2020. After Board certification in Radiation Oncology he is now working as an attending physician at the Department of Radiation Oncology at the “Klinikum rechts der Isar” University Hospital of the Technical University of Munich. In 2018 he began to establishing a research group at the Institute of Radiation Medicine, Helmholtz Munich. In 2019, he visited the Department of Radiation Oncology, University of Washington, Seattle WA, USA, as a Research Fellow.

His research interests include the applications of artificial intelligence techniques in radiation oncology for medical image analysis. His group’s projects aim to personalize cancer care by enabling non-invasive tumor characterization, prognostic assessment, tumor detection, and automated treatment planning. The group is part of multiple international multicentric data sharing consortia to pool patient data and allow for external validation.

Skills and Expertise

Radiation Oncology Medical Imaging Artificial Intelligence Radiomics CancerMRI, CT, PET

Professional Career

2021

Board Certification Radiation Oncology

Technical University Munich, Germany

2020

Habilitation

Technical University Munich, Germany

2015

State Exam Medicine

2018
Romius Research Award

2013
Young Investigator Travel Award

2013
Abstract Achievement Award

2008-2015
German Academic Scholarship Foundation

2022 Eur J Nucl Med Mol Imaging

Spohn SKB*, Farolfi A*, Schandeler S, Vogel MME, Ruf J, Mix M, …, Zamboglou C*, 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.

2021 Radiother Oncol.

Peeken JC, Asadpour R, Specht K, Chen EY, Klymenko O, Akinkuoroye V, Hippe DS, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Gersing AS, Woodruff HC, Lambin P, Nyflot MJ & Combs SE.

MRI-based Delta-Radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy.

2021 Cancers

Navarro F, Dapper H, Asadpour R, Knebel C, Spraker MB, Schwarze V, Schaub SK, Mayr NA, Specht K, Woodruff HC, Lambin P, Gersing AS, Nyflot MJ, Menze BH, Combs SE, Peeken JC.

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging.

2021 Cancers

Peeken JC, Neumann J, Asadpour R, Leonhardt Y, Moreira JR, Hippe DS, Klymenko O, Foreman SC, von Schacky CE, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Woodruff HC, Lambin P, Nyflot MJ, Gersing AS, Combs SE.

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.

2021 IEEE Trans Neural Networks Learn Syst

Thammasorn P, Chaovalitwongse WA, Hippe DS, Wootton LS, Ford EC, Spraker MB, Combs SE, Peeken JC & MJ Nyflot.

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data.

2020 Eur J Nucl Med Mol Imaging.

Peeken JC, Shouman MA, Kroenke M, Rauscher I, Maurer T, Gschwend JE, Eiber M, Combs SE.

A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients.