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Multiparametric MR for non-invasive evaluation of tumour tissue histological characteristics after radionuclide therapy.

Journal article
Authors Mikael Montelius
Oscar Jalnefjord
Johan Spetz
Ola Nilsson
Eva Forssell-Aronsson
Maria Ljungberg
Published in NMR in biomedicine
Volume 31
Issue 3
Pages e4060
ISSN 1099-1492
Publication year 2019
Published at Institute of Clinical Sciences, Department of Radiation Physics
Sahlgrenska Cancer Center
Institute of Biomedicine, Department of Pathology
Pages e4060
Language en
Links dx.doi.org/10.1002/nbm.4060
www.ncbi.nlm.nih.gov/entrez/query.f...
Subject categories Other Basic Medicine, Cancer and Oncology, Radiology, Nuclear Medicine and Medical Imaging, Diagnostic radiology, Radiological physics, Radiation biology

Abstract

Early non-invasive tumour therapy response assessment requires methods sensitive to biological and physiological tumour characteristics. The aim of this study was to find and evaluate magnetic resonance imaging (MRI) derived tumour tissue parameters that correlate with histological parameters and that reflect effects of radionuclide therapy. Mice bearing a subcutaneous human small-intestine neuroendocrine tumour were i.v. injected with 177 Lu-octreotate. MRI was performed (7 T Bruker Biospec) on different post-therapy intervals (1 and 13 days) using T2-weighted imaging, mapping of T2* and T1 relaxation time constants, as well as diffusion and dynamic contrast enhancement (DCE-MRI) techniques. After MRI, animals were killed and tumours excised. Four differently stained histological sections of the most central imaged tumour plane were digitized, and segmentation techniques were used to produce maps reflecting fibrotic and vascular density, apoptosis, and proliferation. Histological maps were aligned with MRI-derived parametric maps using landmark-based registration. Correlations and predictive power were evaluated using linear mixed-effects models and cross-validation, respectively. Several MR parameters showed statistically significant correlations with histological parameters. In particular, three DCE-MRI-derived parameters reflecting capillary function additionally showed high predictive power regarding apoptosis (2/3) and proliferation (1/3). T1 could be used to predict vascular density, and perfusion fraction derived from diffusion MRI could predict fibrotic density, although with lower predictive power. This work demonstrates the potential to use multiparametric MRI to retrieve important information on the tumour microenvironment after radiotherapy. The non-invasiveness of the method also allows longitudinal tumour tissue characterization. Further investigation is warranted to evaluate the parameters highlighted in this study longitudinally, in larger studies, and with additional histological methods.

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