Development and characterization of a rat brain metastatic tumor model by multiparametric magnetic resonance imaging and histomorphology

Wang, Shuncong; Chen, Lei; Feng, Yuanbo; Yin, Ting; Yu, Jing; Keyzer, Frederik De; Peeters, Ronald; Ongeval, Chantal Van; Bormans, Guy; Swinnen, Johan; Soete, Jeroen; Wevers, Martine; Li, Yue; Ni, Yicheng

doi:10.1007/s10585-022-10155-w

Cited by 2 publications

(5 citation statements)

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“…Firstly, it is natively compatible with immune competent WAG/Rij rats, where reproduction of cancer-immunity interaction is possible. Secondly, its derived animal model exhibits similar MRI manifestation with clinical patients [ 22 ]. Thirdly, we aimed at developing models for brain or brain tumor segmentation, instead of elaborating on the biological disparity between different types of brain metastasis.…”

Section: Methodsmentioning

confidence: 99%

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AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study

Wang

Pang

Keyzer

et al. 2023

acta neuropathol commun

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Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-assisted segmentation. MRI images, including T2WI, T1WI and CE-T1WI, of brain tumor from 57 WAG/Rij rats in KU Leuven and 46 mice from the cancer imaging archive (TCIA) were collected. A 3D U-Net architecture was adopted for segmentation of tumor bearing brain and brain tumor. After training, these models were tested with both datasets after Gaussian noise addition. Reduction of inter-observer disparity by AI-assisted segmentation was also evaluated. The AI model segmented tumor-bearing brain well for both Leuven and TCIA datasets, with Dice similarity coefficients (DSCs) of 0.87 and 0.85 respectively. After noise addition, the performance remained unchanged when the signal–noise ratio (SNR) was higher than two or eight, respectively. For the segmentation of tumor lesions, AI-based model yielded DSCs of 0.70 and 0.61 for Leuven and TCIA datasets respectively. Similarly, the performance is uncompromised when the SNR was over two and eight respectively. AI-assisted segmentation could significantly reduce the inter-observer disparities and segmentation time in both rats and mice. Both AI models for segmenting brain or tumor lesions could improve inter-observer agreement and therefore contributed to the standardization of the following biomedical studies.

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Section: Methodsmentioning

confidence: 99%

“…Thirdly, we aimed at developing models for brain or brain tumor segmentation, instead of elaborating on the biological disparity between different types of brain metastasis. The brain metastasis model was induced by surgical implantation, as published before [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study

Wang

Pang

Keyzer

et al. 2023

acta neuropathol commun

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“…Fifteen WAG/Rij rats (eight male and seven female) of 11 weeks of age (Charles River Breeding Laboratories, Inc., Elbeuf, France) were surgically implanted with 1.0 mm 3 cubic rhabdomyosarcoma tissue. Intracranial and extracranial tumors were induced simultaneously, with the intracranial tumor based on a previously published methodology ( Figure 1 A) [ 23 ]. Extracranial tumor lesions were also created in the facial muscle at a cross-sectional level similar to that of the intracranial ones for direct comparison of lesions in the same slice of the MRI scan.…”

Section: Methodsmentioning

confidence: 99%

“…All the MRI sequences here were adopted from the clinically used ones after proper optimization. Specifically, the T2 value was estimated with 24 different echo times and the T1 value was approximated using the variable flip angle method with 9 different flip angles [ 23 ]. For the PWI, a gadolinium-based contrast agent (CA) Dotarem ® (Gd-DOTA, Guerbet, Roissy CdG Cedex, France) at 0.1 mmol/kg was used in the dynamic contrast enhancement setting, with 20 frames of pre-contrast, 80 frames of post-contrast scans, and a temporal resolution of 3.6 s [ 25 , 26 ] ( Figure 1 F,G).…”

Section: Methodsmentioning

confidence: 99%

“…CA4P, fosbretabulin, is a classical VDA derived originally from African bush willow Combretum caffrum, which binds to tubulin and destroys the cytoskeleton in the endothelial cells of tumor blood vessels. Thus, based on orthotopic craniofacial tumor models of immunocompetent rodents [ 23 ], the present intraindividual comparative study was designed to test the follwoing working hypotheses: (1) a VDA may impose diverse efficacies between intracranial and extracranial tumors with the same origin but different locations; and (2) pre-treatment MRI may provide suggestive information for therapeutic prediction and patient selection.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Blood–Brain Barrier to Delivering a Vascular-Disrupting Agent: Predictive Role of Multiparametric MRI in Rodent Craniofacial Metastasis Models

Wang

Feng

Chen

et al. 2022

Cancers

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Vascular-disrupting agents (VDAs) have shown a preliminary anti-cancer effect in extracranial tumors; however, the therapeutic potential of VDAs in intracranial metastatic lesions remains unclear. Simultaneous intracranial and extracranial tumors were induced by the implantation of rhabdomyosarcoma in 15 WAG/Rij rats. Pre-treatment characterizations were performed at a 3.0 T clinical magnet including a T2 relaxation map, T1 relaxation map, diffusion-weighted imaging (DWI), and perfusion-weighted imaging (PWI). Shortly afterward, a VDA was intravenously given and MRI scans at 1 h, 8 h, and 24 h after treatment were performed. In vivo findings were further confirmed by postmortem angiography and histopathology staining with H&E, Ki67, and CD31. Before VDA treatment, better perfusion (AUC30: 0.067 vs. 0.058, p < 0.05) and AUC300 value (0.193 vs. 0.063, p < 0.001) were observed in extracranial lesions, compared with intracranial lesions. After VDA treatment, more significant and persistent perfusion deficiency measured by PWI (AUC30: 0.067 vs. 0.008, p < 0.0001) and a T1 map (T1 ratio: 0.429 vs. 0.587, p < 0.05) were observed in extracranial tumors, in contrast to the intracranial tumor (AUC30: 0.058 vs. 0.049, p > 0.05, T1 ratio: 0.497 vs. 0.625, p < 0.05). Additionally, significant changes in the T2 value and apparent diffusion coefficient (ADC) value were observed in extracranial lesions, instead of intracranial lesions. Postmortem angiography and pathology showed a significantly larger H&E-stained area of necrosis (86.2% vs. 18.3%, p < 0.0001), lower CD31 level (42.7% vs. 54.3%, p < 0.05), and lower Ki67 level (12.2% vs. 32.3%, p < 0.01) in extracranial tumors, compared with intracranial lesions. The BBB functioned as a barrier against the delivery of VDA into intracranial tumors and multiparametric MRI may predict the efficacy of VDAs on craniofacial tumors.

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Development and characterization of a rat brain metastatic tumor model by multiparametric magnetic resonance imaging and histomorphology

Cited by 2 publications

References 36 publications

AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study

AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study

Impact of Blood–Brain Barrier to Delivering a Vascular-Disrupting Agent: Predictive Role of Multiparametric MRI in Rodent Craniofacial Metastasis Models

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