Radiomics and Visual Analysis for Predicting Success of Transplantation of Heterotopic Glioblastoma in Mice with MRI

Overview

Journal J Neurooncol

Publisher Springer

Date 2024 Jul 3

PMID 38960965

Authors

Sabine Wagner

Christian Ewald

Diana Freitag

Karl-Heinz Herrmann

Arend Koch

Johannes Bauer

Thomas J Vogl

Andre Kemmling

Hubert Gufler

Affiliations

Soon will be listed here.

Abstract

Background: Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP).

Methods: Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry.

Results: 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05).

Conclusion: Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.

References

Driehuys B, Nouls J, Badea A, Bucholz E, Ghaghada K, Petiet A . Small animal imaging with magnetic resonance microscopy. ILAR J. 2008; 49(1):35-53. PMC: 2770253. DOI: 10.1093/ilar.49.1.35. View

Kuczynski E, Vermeulen P, Pezzella F, Kerbel R, Reynolds A . Vessel co-option in cancer. Nat Rev Clin Oncol. 2019; 16(8):469-493. DOI: 10.1038/s41571-019-0181-9. View

Lisson C, Lisson C, Flosdorf K, Mayer-Steinacker R, Schultheiss M, von Baer A . Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study. Eur Radiol. 2017; 28(2):468-477. DOI: 10.1007/s00330-017-5014-6. View

Chaddad A, Daniel P, Desrosiers C, Toews M, Abdulkarim B . Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time. IEEE J Biomed Health Inform. 2018; 23(2):795-804. DOI: 10.1109/JBHI.2018.2825027. View

Hunter S, Brat D, Olson J, von Deimling A, Zhou W, Van Meir E . Alterations in molecular pathways of diffusely infiltrating glial neoplasms: application to tumor classification and anti-tumor therapy (Review). Int J Oncol. 2003; 23(4):857-69. View

Kovalikova Z, Gersonde K, Porschen R, Mittermayer C, Franke R . Age-dependent variation of T1 and T2 relaxation times of adenocarcinoma in mice. Radiology. 1987; 164(2):543-8. DOI: 10.1148/radiology.164.2.3602399. View

Verburg N, de Witt Hamer P . State-of-the-art imaging for glioma surgery. Neurosurg Rev. 2020; 44(3):1331-1343. PMC: 8121714. DOI: 10.1007/s10143-020-01337-9. View

Arshad H, Ahmad Z, Hasan S . Gliomas: correlation of histologic grade, Ki67 and p53 expression with patient survival. Asian Pac J Cancer Prev. 2011; 11(6):1637-40. View

Rong Y, Durden D, Van Meir E, Brat D . 'Pseudopalisading' necrosis in glioblastoma: a familiar morphologic feature that links vascular pathology, hypoxia, and angiogenesis. J Neuropathol Exp Neurol. 2006; 65(6):529-39. DOI: 10.1097/00005072-200606000-00001. View

10.

Mayerhoefer M, Materka A, Langs G, Haggstrom I, Szczypinski P, Gibbs P . Introduction to Radiomics. J Nucl Med. 2020; 61(4):488-495. PMC: 9374044. DOI: 10.2967/jnumed.118.222893. View

11.

Lugano R, Ramachandran M, Dimberg A . Tumor angiogenesis: causes, consequences, challenges and opportunities. Cell Mol Life Sci. 2019; 77(9):1745-1770. PMC: 7190605. DOI: 10.1007/s00018-019-03351-7. View

12.

Dahlrot R, Bangso J, Petersen J, Rosager A, Sorensen M, Reifenberger G . Prognostic role of Ki-67 in glioblastomas excluding contribution from non-neoplastic cells. Sci Rep. 2021; 11(1):17918. PMC: 8429554. DOI: 10.1038/s41598-021-95958-9. View

13.

Bigner D, Bigner S, Ponten J, Westermark B, MAHALEY M, Ruoslahti E . Heterogeneity of Genotypic and phenotypic characteristics of fifteen permanent cell lines derived from human gliomas. J Neuropathol Exp Neurol. 1981; 40(3):201-29. DOI: 10.1097/00005072-198105000-00001. View

14.

Thorsen F, Ersland L, Nordli H, Enger P, Huszthy P, Lundervold A . Imaging of experimental rat gliomas using a clinical MR scanner. J Neurooncol. 2003; 63(3):225-31. DOI: 10.1023/a:1024241905888. View

15.

Ferris S, Hofmann J, Solomon D, Perry A . Characterization of gliomas: from morphology to molecules. Virchows Arch. 2017; 471(2):257-269. DOI: 10.1007/s00428-017-2181-4. View

16.

Ion-Margineanu A, Van Cauter S, Sima D, Maes F, Sunaert S, Himmelreich U . Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features. Front Neurosci. 2017; 10:615. PMC: 5225114. DOI: 10.3389/fnins.2016.00615. View

17.

Herrmann K, Schmidt S, Kretz A, Haenold R, Krumbein I, Metzler M . Possibilities and limitations for high resolution small animal MRI on a clinical whole-body 3T scanner. MAGMA. 2011; 25(3):233-44. DOI: 10.1007/s10334-011-0284-5. View

18.

Richmond A, Su Y . Mouse xenograft models vs GEM models for human cancer therapeutics. Dis Model Mech. 2008; 1(2-3):78-82. PMC: 2562196. DOI: 10.1242/dmm.000976. View

19.

de Vries N, Beijnen J, van Tellingen O . High-grade glioma mouse models and their applicability for preclinical testing. Cancer Treat Rev. 2009; 35(8):714-23. DOI: 10.1016/j.ctrv.2009.08.011. View

20.

Jalil O, Afaq A, Ganeshan B, Patel U, Boone D, Endozo R . Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long-term survival in locally advanced rectal cancer treated by chemoradiotherapy. Colorectal Dis. 2016; 19(4):349-362. DOI: 10.1111/codi.13496. View