565-04-014 ISMRM Abstract

Comparative and Integrative Analysis of DCE-MRI, DWI-MRI, and Radiomic Features for Pre-Operative Grading of Meningiomas

Primary: Neuro - Tumors

Secondary: Analysis Methods - Classification and Prediction

565-04-014 · Neuroradiomics · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row F

Keywords: Radiomics DCE-MRI Diffusion-weighted MRI Meningiomas Machine learning (ML)

Accepted

Sanskriti Srivastava¹, Rakesh Kumar Gupta^2,3, Anup Singh^1,4,5

¹Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, India

²Department of Radiology, Fortis Memorial Research Institute, Gurugram, India

³Fortis Memorial Research Institute, Gurugram, India

⁴Yardi School of Artificial Intelligence, Indian Institute of Technology, Delhi, India

⁵Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

Presenting Author: Anirban Sengupta

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References

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2. Nasrallah MLP, Aldape KD. Molecular classification and grading of meningioma. J Neurooncol.Springer. 2023;161(2):373-381. doi:10.1007/s11060-022-04228-9 [doi]

3. Day SE, Halasz LM. Radiation therapy for WHO grade I meningioma. Chin Clin Oncol.AME Publishing Company. 2017;6. doi:10.21037/cco.2017.06.01 [doi]

4. Adeli A, Hess K, Mawrin C, Streckert EMS, Stummer W, Paulus W, Kemmling A, Holling M, Heindel W, Schmidt R, Spille DC, Sporns PB, Brokinkel B. Prediction of brain invasion in patients with meningiomas using preoperative magnetic resonance imaging. Oncotarget. 2018 Nov 13;9(89):35974-35982. doi: 10.18632/oncotarget.26313. PMID: 30542511; PMCID: PMC6267603. [doi] [pmid]

5. Siempis T, Tsakiris C, Alexiou GA, Xydis VG, Voulgaris S, Argyropoulou MI. Diagnostic performance of diffusion and perfusion MRI in differentiating high from low-grade meningiomas: A systematic review and meta-analysis: Diagnostic performance of diffusion and perfusion MRI in differentiating High from low-grade meningiomas: A systematic review and meta-analysis. Clin Neurol Neurosurg. 2020;190. doi:10.1016/j.clineuro.2019.105643 [doi]

6. Zhang J, Yao K, Liu P, et al. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study. EBioMedicine. 2020;58. doi:10.1016/j.ebiom.2020.102933 [doi]

7. Zhang J, Yao K, Liu P, et al. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study. EBioMedicine. 2020;58. doi:10.1016/j.ebiom.2020.102933 [doi]

8. Hashido T, Saito S, Ishida T. Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- And Perfusion-Weighted Magnetic Resonance Imaging. J Comput Assist Tomogr. 2021;45(4):606-613. doi:10.1097/RCT.0000000000001180 [doi]

9. Van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017;77(21):e104-e107. doi:10.1158/0008-5472.CAN-17-0339 [doi]

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http://echo.ismrm.org/p/ISMRM2026/565-04-014