Neuro-Oncology Research

The team utilises Dynamic Contrast Enhanced (DCE) MR perfusion, H1 MR spectroscopy, diffusion imaging and lipid imaging to study focal brain and spinal lesions. The higher-grade angiogenic tumours have hallmarks of increased number of microvessels and irregular branching with poorly formed endothelial walls, contributing to microvascular hyperpermeability. The SGH team collaborates with the NCSC Oncologic Imaging Team on the use of distributed parameter model in DCE-MRI to interrogate tumour leakiness which allows for quantification of more perfusion parameters. We also integrate other MRI techniques as adjunct to provide additional information on the tumour characteristics such as diffusion imaging to reflect tumour cellularity and vascularity, and magnetic resonance spectroscopy (MRS) that provide additional information on neurometabolite concentrations. On top of that, lipid metabolism change has unexplored potentials for the characterisation of tumours given the important role of lipid in cancer metabolism and tumour development. In-and opposed-phase is a simple alternative to lipid quantification which reflects lipid metabolic change other than MRS.

Brain tumor patient with a non-specific enhancing right occipital lesion (a-d) delineated on (a) post-contrast T1-weighted and (b) T2 FLAIR images, demonstrating low and high signal on the (c) v1 (ratio of blood plasma volume to tissue volume) and (d) v2 (ratio of extra-vascular extracellular space volume to tissue volume) maps of dynamic contrast-enhanced (DCE) scan favouring post treatment change.

Contact

Please contact the team consisting of Dr Lim Kheng Choon, Dr Septian Hartono and Dr Seow Poh Choo for more information.

• Dr Lim Kheng Choon - lim.kheng.choon@singhealth.com.sg
• Dr Septian Hartono - septian.hartono@sgh.com.sg
• Dr Seow Poh Choo - seow.poh.choo@sgh.com.sg
   

Publications:

1. Pang JHW, Saffari SE, Lee GR, Yu WY, Lim CCT, Lim KC, Lee CC, Koh WY, Chia WTD, Chua KLM, Tham CK, Low YYS, Ng WH, Low CYD, Lin X. Tumour growth rate predicts overall survival in patients with recurrent WHO grade 4 glioma. BMC Med Imaging. 2024 May 27;24(1):125. doi: 10.1186/s12880-024-01263-y. PMID: 38802734; PMCID: PMC11131225.
2. de Godoy LL*, Lim KC*, Rajan A, Verma G, Hanaoka M, O'Rourke DM, Lee JYK, Desai A, Chawla S, Mohan S. Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI. Cancers. 2023;15(18):4453. *shared first authorship
3. Choi YS, Bae S, Chang JH, Kang SG, Kim SH, Kim J, Rim TH, Choi SH, Jain R, Lee SK. Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics. Neuro Oncol. 2021 Feb 25;23(2):304-313. doi: 10.1093/neuonc/noaa177. PMID: 32706862; PMCID: PMC7906063.
4. Seow P, Hernowo AT, Narayanan V, Wong JHD, Bahuri NFA, Cham CY, Abdullah NA, Kadir KAA, Rahmat K, Ramli N. Neural Fiber Integrity in High- Versus Low-Grade Glioma using Probabilistic Fiber Tracking. Acad Radiol. 2021 Dec;28(12):1721-1732. doi: 10.1016/j.acra.2020.09.007. Epub 2020 Oct 3. PMID: 33023809.
5. Seow P, Narayanan V, Hernowo AT, Wong JHD, Ramli N. (2018). Quantification and Visualisation of Lipid Landscape in Glioma using In -and Opposed-Phase Imaging. Neuroimage: Clinical. 2018 (20): 531-536
6. Rumpel H, Chong Y, Porter DA, Chan LL. Benign versus metastatic vertebral compression fractures: combined diffusion-weighted MRI and MR spectroscopy aids differentiation. Eur Radiol. 2013 Feb;23(2):541-50. doi: 10.1007/s00330-012-2620-1.