Detail

Keywords

Animal models; Digital Histopathology; Genomics; Molecular Imaging; Nuclear Medicine; Oncology

Research group(s)

• Christian Doppler Laboratory for Applied Metabolomics
  Head: Lukas Kenner
  Research Area: We investigate ways to better characterize tumors using non-invasive imaging techniques. This is important because tumours are constantly changing through mutations. In this way, an individual therapy should be possible and its success should be continuously monitored.
  Members:
  Marko Grahovac
  Kilian Kluge
  Lukas Kenner
  David Haberl
  Jing Ning

Research interests

My research primarily focuses on the intersection of medical imaging and computational analysis, particularly emphasizing the application of radiomics and advanced imaging techniques in oncology. I am dedicated to exploring how PET imaging and other imaging modalities can be leveraged to improve the early detection, accurate staging, and personalized treatment planning of various cancers, including but not limited to prostate cancer, glioma and extrahepatic cholangiocarcinoma. By integrating machine learning algorithms and radiomic feature extraction, my work aims to enhance the predictive modeling of tumor behavior, treatment response, and patient outcomes, ultimately contributing to more precise and effective cancer care.

Selected publications

1. Ning, J. et al. (2024) ‘A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study’, Theranostics, 14(12), pp. 4570–4581. Available at: https://doi.org/10.7150/thno.96921.
2. Spielvogel, C.P. et al. (2024) ‘Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study’, The Lancet Digital Health, 6(4), pp. e251–e260. Available at: https://doi.org/10.1016/s2589-7500(23)00265-0.
3. Ning, J. et al. (2023) ‘Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT’, Insights into Imaging, 14(1). Available at: https://doi.org/10.1186/s13244-023-01530-6.
4. Wu, M. et al. (2021) ‘Feasibility of In Vivo Imaging of Fibroblast Activation Protein in Human Arterial Walls’, Journal of Nuclear Medicine, 63(6), pp. 948–951. Available at: https://doi.org/10.2967/jnumed.121.262863.
5. Yu, P. et al. (2019) ‘Histogram analysis of 11C-methionine integrated PET/MRI may facilitate to determine the O6-methylguanylmethyltransferase methylation status in gliomas’, Nuclear Medicine Communications, 40(8), pp. 850–856. Available at: https://doi.org/10.1097/mnm.0000000000001039.