Skip to main content

Advanced Image Analysis (Artificial Intelligence, Radiomics)

  1. Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for externa...

    Authors: Carol Oliveira, Florian Amstutz, Diem Vuong, Marta Bogowicz, Martin Hüllner, Robert Foerster, Lucas Basler, Christina Schröder, Eric I. Eboulet, Miklos Pless, Sandra Thierstein, Solange Peters, Sven Hillinger, Stephanie Tanadini-Lang and Matthias Guckenberger

    Citation: EJNMMI Research 2021 11:79

    Content type: Original research

    Published on:

  2. The prostate-specific membrane antigen (PSMA) is a relevant target in prostate cancer, and immunohistochemistry studies showed associations with outcome. PSMA-ligand positron emission tomography (PET) is incre...

    Authors: Hui Wang, Thomas Amiel, Christoph Würnschimmel, Thomas Langbein, Katja Steiger, Isabel Rauscher, Thomas Horn, Tobias Maurer, Wolfgang Weber, Hans-Juergen Wester, Karina Knorr and Matthias Eiber

    Citation: EJNMMI Research 2021 11:76

    Content type: Original research

    Published on:

  3. To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep...

    Authors: Zongyao Li, Kazuhiro Kitajima, Kenji Hirata, Ren Togo, Junki Takenaka, Yasuo Miyoshi, Kohsuke Kudo, Takahiro Ogawa and Miki Haseyama

    Citation: EJNMMI Research 2021 11:10

    Content type: Original research

    Published on:

  4. Careful selection of malignant pleural mesothelioma (MPM) patients for curative treatment is of highest importance, as the multimodal treatment regimen is challenging for patients and harbors a high risk of su...

    Authors: M. Pavic, M. Bogowicz, J. Kraft, D. Vuong, M. Mayinger, S. G. C. Kroeze, M. Friess, T. Frauenfelder, N. Andratschke, M. Huellner, W. Weder, M. Guckenberger, S. Tanadini-Lang and I. Opitz

    Citation: EJNMMI Research 2020 10:81

    Content type: Original research

    Published on:

  5. To establish and validate 18F-fluorodeoxyglucose (18F-FDG) PET/CT-based radiomics model and use it to predict the intermediate-high risk growth patterns in early invasive adenocarcinoma (IAC).

    Authors: Xiaonan Shao, Rong Niu, Xiaoliang Shao, Zhenxing Jiang and Yuetao Wang

    Citation: EJNMMI Research 2020 10:80

    Content type: Original research

    Published on:

  6. The aim of this study was to develop and validate a prognostic model incorporating [18F]FDG PET/CT radiomics for patients of minor salivary gland carcinoma (MSGC).

    Authors: Nai-Ming Cheng, Cheng-En Hsieh, Yu-Hua Dean Fang, Chun-Ta Liao, Shu-Hang Ng, Hung-Ming Wang, Wen-Chi Chou, Chien-Yu Lin and Tzu-Chen Yen

    Citation: EJNMMI Research 2020 10:74

    Content type: Original research

    Published on:

  7. Attenuation correction (AC) of PET data is usually performed using a second imaging for the generation of attenuation maps. In certain situations however—when CT- or MR-derived attenuation maps are corrupted o...

    Authors: Karim Armanious, Tobias Hepp, Thomas Küstner, Helmut Dittmann, Konstantin Nikolaou, Christian La Fougère, Bin Yang and Sergios Gatidis

    Citation: EJNMMI Research 2020 10:53

    Content type: Original research

    Published on: