Name: positron emission tomography-based dose painting radiation therapy in a glioblastoma rat model using the small animal radiation research platform
Uploaded: 2022-03-24T13:00:00
Description: Watch this Scientific Journal Video about Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform at JoVE.com

The authors would like to thank Lux Luka Foundation for supporting this work.

The study was approved by the local ethics committee for animal experiments (ECD 18/21).&#160;Anesthesia monitoring is performed by acquiring the respiratory rate of the animals using a sensor.
1. F98 GB rat cell model
<ol>
	<li>Culture the F98 GB cells in a monolayer using Dulbecco&#39;s Modified Eagle Medium, supplemented with 10% calf serum, 1% penicillin, 1% streptomycin, and 1% L-glutamine, and place them in a CO2 incubator (5% CO2 and 37 &#176;C).</li>
	<li>Inocul...

<ol>
	<li>Louis, D. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+2016+World+Health+Organization+classification+of+tumors+of+the+central+nervous+system:+a+summary.">The 2016 World Health Organization classification of tumors of the central nervous system: a summary.</a> Acta Neuropathologica. 131 (6), 803-820 (2016).</li><li>Wadajkar, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumor-targeted+nanotherapeutics:+Overcoming+treatment+barriers+for+glioblastoma.">Tumor-targeted nanotherapeutics: Overcoming treatment barriers for glioblastoma.</a> Wiley Interdisciplinary Reviews. Nanomedicine &amp; Nanobiotechnology. 9 (4), (2016).</li><li>Lim, M., Xia, Y., Bettegowda, C., Weller, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+state+of+immunotherapy+for+glioblastoma.">Current state of immunotherapy for glioblastoma.</a> Nature Reviews. Clinical Oncology. 15 (7), 422 (2018).</li><li>McGranahan, T., Li, G., Nagpal, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=History+and+current+state+of+immunotherapy+in+glioma+and+brain+metastasis.">History and current state of immunotherapy in glioma and brain metastasis.</a> Therapeutic Advances in Medical Oncology. 9 (5), 347-368 (2017).</li><li>Stupp, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Radiotherapy+plus+concomitant+and+adjuvant+temozolomide+for+glioblastoma.">Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma.</a> The New England Journal of Medicine. 352 (10), 987-996 (2005).</li><li>Von Neubeck, C., Seidlitz, A., Kitzler, H. H., Beuthien-Baumann, B., Krause, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glioblastoma+multiforme:+Emerging+treatments+and+stratification+markers+beyond+new+drugs.">Glioblastoma multiforme: Emerging treatments and stratification markers beyond new drugs.</a> The British Journal of Radiology. 88 (1053), 20150354 (2015).</li><li>Mann, J., Ramakrishna, R., Magge, R., Wernicke, A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+radiotherapy+for+glioblastoma.">Advances in radiotherapy for glioblastoma.</a> Frontiers in Neurology. 8, 748 (2018).</li><li>Ling, C. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+multidimensional+radiotherapy+(MD-CRT):+Biological+imaging+and+biological+conformality.">Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality.</a> International Journal of Radiation Oncolology, Biology, Physics. 47 (3), 551-560 (2000).</li><li>Bentzen, S. M., Gregoire, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+imaging-based+dose+painting:+a+novel+paradigm+for+radiation+therapy+prescription.">Molecular imaging-based dose painting: a novel paradigm for radiation therapy prescription.</a> Seminars in Radiation Oncology. 21 (2), 101-110 (2011).</li><li>Bentzen, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Theragnostic+imaging+for+radiation+oncology:+Dose-painting+by+numbers.">Theragnostic imaging for radiation oncology: Dose-painting by numbers.</a> The Lancet. Oncology. 6 (2), 112-117 (2005).</li><li>Wong, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution,+small+animal+radiation+research+platform+with+X-ray+tomographic+guidance+capabilities.">High-resolution, small animal radiation research platform with X-ray tomographic guidance capabilities.</a> International Journal of Radiation Oncolology, Biology, Physics. 71 (5), 1591-1599 (2008).</li><li>Van Hoof, S. J., Granton, P. V., Verhaegen, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+validation+of+a+treatment+planning+system+for+small+animal+radiotherapy:+SmART-Plan.">Development and validation of a treatment planning system for small animal radiotherapy: SmART-Plan.</a> Radiotherapy and Oncology. 109 (3), 361-366 (2013).</li><li>Verhaegen, F., Granton, P., Tryggestad, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small+animal+radiotherapy+research+platforms.">Small animal radiotherapy research platforms.</a> Physics in Medicine &amp; Biology. 56 (12), 55-83 (2011).</li><li>Butterworth, K. T., Prise, K. M., Verhaegen, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small+animal+image-guided+radiotherapy:+Status,+considerations+and+potential+for+translational+impact.">Small animal image-guided radiotherapy: Status, considerations and potential for translational impact.</a> The British Journal of Radiology. 88 (1045), 20140634 (2015).</li><li>Nasr, A., Habash, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dosimetric+analytic+comparison+of+inverse+and+forward+planned+IMRT+techniques+in+the+treatment+of+head+and+neck+cancer.">Dosimetric analytic comparison of inverse and forward planned IMRT techniques in the treatment of head and neck cancer.</a> Journal of the Egyptian National Cancer Institute. 26 (3), 119-125 (2014).</li><li>Matinfar, M., Iyer, S., Ford, E., Wong, J., Kazanzides, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+guided+complex+dose+delivery+for+small+animal+radiotherapy.">Image guided complex dose delivery for small animal radiotherapy.</a> IEEE International Symposium on Biomedical Imaging: From Nano to Macro. , 1243-1246 (2009).</li><li>Matinfar, M., Iordachita, I., Wong, J., Kazanzides, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robotic+delivery+of+complex+radiation+volumes+for+small+animal+research.">Robotic delivery of complex radiation volumes for small animal research.</a> IEEE International Conference on Robotics and Automation. , 2056-2061 (2010).</li><li>Balvert, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+framework+for+inverse+planning+of+beam-on+times+for+3D+small+animal+radiotherapy+using+interactive+multi-objective+optimisation.">A framework for inverse planning of beam-on times for 3D small animal radiotherapy using interactive multi-objective optimisation.</a> Physics in Medicine &amp; Biology. 60 (14), 5681-5698 (2015).</li><li>Cho, N. B., Wong, J., Kazanzides, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dose+Painting+with+a+Variable+Collimator+for+the+Small+Animal+Radiation+Research+Platform+(SARRP).">Dose Painting with a Variable Collimator for the Small Animal Radiation Research Platform (SARRP).</a> The Midas Journal. , 1-8 (2014).</li><li>Bolcaen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MRI-guided+3D+conformal+arc+micro-irradiation+of+a+F98+glioblastoma+rat+model+using+the+Small+Animal+Radiation+Research+Platform+(SARRP).">MRI-guided 3D conformal arc micro-irradiation of a F98 glioblastoma rat model using the Small Animal Radiation Research Platform (SARRP).</a> Journal of Neuro-oncology. 120 (2), 257-266 (2014).</li><li>Bolcaen, J., Descamps, B., Boterberg, T., Vanhove, C., Goethals, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PET+and+MRI+guided+irradiation+of+a+glioblastoma+rat+model+using+a+micro-irradiator.">PET and MRI guided irradiation of a glioblastoma rat model using a micro-irradiator.</a> Journal of Visualized Experiments: JoVE. (130), e56601 (2017).</li><li>Verhoeven, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Technical+feasibility+of+[18F]FET+and+[18F]FAZA+PET+guided+radiotherapy+in+a+F98+glioblastoma+rat+model.">Technical feasibility of [18F]FET and [18F]FAZA PET guided radiotherapy in a F98 glioblastoma rat model.</a> Radiation Oncology. 14 (1), 89 (2019).</li><li>Hutterer, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FET+PET:+a+valuable+diagnostic+tool+in+neuro-oncology,+but+not+all+that+glitters+is+glioma.">FET PET: a valuable diagnostic tool in neuro-oncology, but not all that glitters is glioma.</a> Neuro-oncology. 15 (3), 341-351 (2013).</li><li>Stockhammer, F., Plotkin, M., Amthauer, H., Landeghem, F. K. H., Woiciechowsky, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correlation+of+F-18-fluoro-ethyl-tyrosin+uptake+with+vascular+and+cell+density+in+non-contrast-enhancing+gliomas.">Correlation of F-18-fluoro-ethyl-tyrosin uptake with vascular and cell density in non-contrast-enhancing gliomas.</a> Journal of Neuro-oncology. 88 (2), 205-210 (2008).</li><li>. Mricron dicom to nifti converter. neuroimaging informatics tools and resources clearinghouse (nitrc) Available from: <a target="_blank" href="https://www.nitrc.org/projects/mricron">https://www.nitrc.org/projects/mricron</a> (2015)</li><li>. SPM12 Manual Available from: <a target="_blank" href="https://www.fil.ion.ucl.ac.uk/spm/doc/spm12_manual.pdf">https://www.fil.ion.ucl.ac.uk/spm/doc/spm12_manual.pdf</a> (2014)</li><li>Espa&#241;a, S., Marcinkowski, R., Keereman, V., Vandenberghe, S., Van Holen, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DigiPET:+Sub-millimeter+spatial+resolution+small-animal+PET+imaging+using+thin+monolithic+scintillators.">DigiPET: Sub-millimeter spatial resolution small-animal PET imaging using thin monolithic scintillators.</a> Physics in Medicine &amp; Biology. 59 (13), 3405-3420 (2014).</li></ol>

A rat GB model to mimic the chemo-radiation treatment in the clinic for glioblastoma patients was previously described20. Similar to the clinical method, CT and MRI were combined during the treatment-planning process to obtain more precise irradiation. A multimodality bed to minimize (head) movement was used when the animal was moved from one imaging system to another. Subsequently, PET imaging was added to the treatment-planning process, and PET-based sub-volume boosting could be successfully imp...

Glioblastoma (GB) is a malignant and very aggressive primary brain tumor. GB is a solid heterogeneous tumor typically characterized by infiltrative boundaries, nuclear atypia, and necrosis1. The presence of the blood-brain-barrier and the brain&#39;s status as an immune-privileged site makes the discovery of novel targets for chemo- and immunotherapy a daunting task2,3,4. It is noteworthy that the treatment of GB patients has barely changed since the introduction, in 2005, of the Stupp protocol that combines external beam radiation therapy (RT) with concomitant temozolomide, usually followed by adjuvant temozolomide5. Typically, the Stupp protocol is preceded by maximal surgical resection. Therefore, alternative treatment approaches are of pivotal importance.
Current radiation therapy for glioblastoma patients delivers a uniform radiation dose to the defined tumor volume. In radiation oncology, there is an important dose-response correlation for glioblastoma with increasing dose, which seems to cap around 60 Gy, due to increased toxicity to the normal brain6,7. However, tumors can be very (radiobiologically) heterogeneous, with gradients of oxygen level and/or large differences in cellular density. Metabolic imaging techniques, such as PET, can visualize these biological features and can be utilized to customize the dose prescription. This approach is known as dose painting RT. This term was introduced by Ling et al. in 2000. The authors defined dose painting RT as producing &#34;exquisitely conformal dose distributions within the constraints of radiation propagation and scatter&#34;8.
There are two types of dose painting RT, dose painting by contours (DPBC), by which a dose is prescribed to a set of nested sub-volumes, and dose painting by numbers (DPBN), whereby a dose is prescribed at the voxel level. The dose distribution for DPBN RT can be extracted from functional images. The dose in each voxel is determined by the intensity I of the corresponding voxel in the image, with a lower and upper limit, to make sure that, on the one hand, a sufficient dose is delivered to every part of the tumor. On the other hand, doses&#160;do not exceed an upper limit to protect organs at risk and avoid toxicity. The most straightforward method is a linear interpolation (see Eq. 1) between minimum dose&#160;Dmin and maximum dose Dmax, proportionally varying between minimum intensity Imax and maximum intensity within the target volume9,10
<img alt="Equation 1" src="/files/ftp_upload/62560/62560eq01.jpg" />&#160;Eq. 1
Because there is some skepticism about the quality assurance of DPBN RT, the deposition of the dose should be verified through preclinical and clinical research10. However, only limited data can be acquired from clinical trials, and it has been hypothesized that more insights can be obtained by downscaling to laboratory animals11,12. Hence, preclinical studies utilizing precision image-guided radiation research platforms that allow coupling with some very specific techniques, such as autoradiography, are suited for examining open issues and paving the way towards personalized medicine and novel treatment strategies, such as dose painting RT13,14. However, the interpretation of preclinical data must be performed with caution, and drawbacks of these preclinical setups have to be considered14.
Micro-irradiation systems, such as the Small Animal Radiation Research Platform (SARRP), are equipped with similar technologies as their clinical counterpart. They include on-board cone-beam CT (CBCT) imaging, a preclinical treatment-planning system (PCTPS), and provide sub-millimeter precision. Clinical dose calculations are performed by inverse treatment planning, whereby one initiates from the desired dose distribution to determine the beams via an iterative algorithm. Preclinical irradiators often use forward planning. In forward planning, the required amount and angle of the beams are selected, and the PCTPS then calculates the dose distribution. The optimization of the plans is performed by manual iteration, which is labor-intensive15.
After 2009, novel developments have made the implementation of inverse planning on these research platforms possible16,17,18. To increase the similarity with the clinical method, a motorized variable rectangular collimator (MVC) was developed as a preclinical counterpart of the multi-leaf collimator. A two-dimensional dose painting method utilizing a variable collimator was published by Cho et al.19. This research group implemented a three-dimensional (3D) inverse treatment-planning protocol on a micro-irradiator and determined minimum and maximum doses for the target volume and a maximum dose for the organs at risk. These techniques have mainly been evaluated in silico, and their preclinical applications need to be explored.
This paper presents an in silico study to compare two methodologies for [18F]-fluoro-ethyl-L-tyrosine ([18F]FET) PET-based dose painting in a GB rat model20,21,22 using a small animal radiation research platform. These two methodologies are (1) sub-volume boosting using predefined beam sizes and (2) dose painting using a motorized variable collimator where jaw dimensions are modified based on the PET tracer uptake in the tumor volume. [18F]FET is a PET tracer often used in neuro-oncology because of its ability to detect brain tumors23. [18F]FET is an artificial amino acid that is internalized into tumoral cells but not incorporated into cell proteins. [18F]FET uptake corresponds with cell proliferation rate, tumor cell density, and angiogenesis24. As this is the most commonly used oncologic brain PET tracer in these authors&#39; institute, this radiotracer was chosen to evaluate the new workflow.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Cell culture</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>F98 Glioblastoma Cell Line</td>
			<td>ATCC</td>
			<td>CRL-2397</td>
			<td>https://www.lgcstandards-atcc.org/products/all/CRL-2397</td>
		</tr>
		<tr>
			<td>Dulbeco&#39;s Modified Eagle Medium</td>
			<td>Thermo Fisher Scientific</td>
			<td>22320-030</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell culture flasks</td>
			<td>Thermo Fisher Scientific</td>
			<td>178883</td>
			<td>75 cm&#178;</td>
		</tr>
		<tr>
			<td>FBS</td>
			<td>Thermo Fisher Scientific</td>
			<td>10270106</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Glutamine</td>
			<td>Thermo Fisher Scientific</td>
			<td>25030-032</td>
			<td>200 mM</td>
		</tr>
		<tr>
			<td>Penicilline-Streptomycin</td>
			<td>Thermo Fisher Scientific</td>
			<td>15140-148</td>
			<td>10,000 U/mL</td>
		</tr>
		<tr>
			<td>Phosphate-Buffered Saline (PBS)</td>
			<td>Thermo Fisher Scientific</td>
			<td>14040-224</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin-EDTA</td>
			<td>Thermo Fisher Scientific</td>
			<td>25300-062</td>
			<td>0.05%</td>
		</tr>
		<tr>
			<td>GB Rat Model</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Ball-shaped burr</td>
			<td>Foredom</td>
			<td>A-228</td>
			<td>1.8 mm</td>
		</tr>
		<tr>
			<td>Bone Wax</td>
			<td>Aesculap</td>
			<td>1029754</td>
			<td>https://www.aesculapusa.com/en/healthcare-professionals/or-solutions/or-solutions-cranial-closure/hemostatic-bone-wax.html</td>
		</tr>
		<tr>
			<td>Ethilon</td>
			<td>Ethicon</td>
			<td>662G/662H</td>
			<td>FS-2, 4-0, 3/8, 19 mm</td>
		</tr>
		<tr>
			<td>Fischer F344/Ico crl Rats</td>
			<td>Charles River</td>
			<td>-</td>
			<td></td>
		</tr>
		<tr>
			<td>Insulin Syringe Microfine</td>
			<td>Beckton-Dickinson</td>
			<td>320924</td>
			<td>1 mL, 29 G</td>
		</tr>
		<tr>
			<td>IR Lamp</td>
			<td>Philips</td>
			<td>HP3616/01</td>
			<td></td>
		</tr>
		<tr>
			<td>Meloxicam (Metacam)</td>
			<td>Boehringer Ingelheim</td>
			<td>-</td>
			<td>2 mg/mL</td>
		</tr>
		<tr>
			<td>Micromotor rotary tool</td>
			<td>Foredom</td>
			<td>K.1090-22</td>
			<td></td>
		</tr>
		<tr>
			<td>Micropump system</td>
			<td>Stoelting Co.</td>
			<td>53312</td>
			<td>Stoelting Stereotaxic Injector</td>
		</tr>
		<tr>
			<td>Stereotactic frame</td>
			<td>Stoelting Co.</td>
			<td>51600</td>
			<td></td>
		</tr>
		<tr>
			<td>Xylocaine (1%, with adrenaline 1:200,000)</td>
			<td>Aspen</td>
			<td>-</td>
			<td>1%, with adrenaline 1:200,000</td>
		</tr>
		<tr>
			<td>Xylocaine gel (2%)</td>
			<td>Aspen</td>
			<td>-</td>
			<td>2%</td>
		</tr>
		<tr>
			<td>Animal Irradiation</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Micro-irradiator</td>
			<td>X-Strahl</td>
			<td>SARRP</td>
			<td>Version 4.2.0</td>
		</tr>
		<tr>
			<td>Software</td>
			<td>X-Strahl</td>
			<td>Muriplan</td>
			<td>Preclinical treatment planning system (PCTPC), version 2.2.2</td>
		</tr>
		<tr>
			<td>Small Animal PET</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>[18F]FET</td>
			<td>Inhouse made</td>
			<td>-</td>
			<td>PET tracer; along with Prohance: MRI/PET agent</td>
		</tr>
		<tr>
			<td>Micro-PET</td>
			<td>Molecubes</td>
			<td>Beta-Cube</td>
			<td>https://www.molecubes.com/b-cube/</td>
		</tr>
		<tr>
			<td>Small Animal MRI</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Micro-MRI</td>
			<td>Bruker Biospin</td>
			<td>Pharmascan 70/16</td>
			<td>https://www.bruker.com/products/mr/preclinical-mri/pharmascan.html</td>
		</tr>
		<tr>
			<td>30 G Needle for IV injection</td>
			<td>Beckton-Dickinson</td>
			<td>305128</td>
			<td></td>
		</tr>
		<tr>
			<td>PE 10 Tubing</td>
			<td>Instech Laboratories Inc</td>
			<td>BTPE-10</td>
			<td>BTPE-10, polyethylene tubing 0.011 x 0.024 in (0.28 x 60 mm), non sterile, 30 m (98 ft) spool, Instech laboratories, Inc Plymouth meeting PA USA- (800) 443-4227- http://www.instechlabs.com</td>
		</tr>
		<tr>
			<td>Prohance contrast agent</td>
			<td>Bracco Imaging</td>
			<td>-</td>
			<td>279.3 mg/mL, gadolinium-contrast agent (along with [18F]FET: MRI/PET agent)</td>
		</tr>
		<tr>
			<td>Tx/Rx Rat Brain - Mouse Whole Body Volumecoil</td>
			<td>Bruker Biospin</td>
			<td>-</td>
			<td>40 mm diameter</td>
		</tr>
		<tr>
			<td>Water-based Heating Unit</td>
			<td>Bruker Biospin</td>
			<td>MT0125</td>
			<td></td>
		</tr>
		<tr>
			<td>Consumables</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Isoflurane</td>
			<td>Zoetis</td>
			<td>B506</td>
			<td>Anesthesia</td>
		</tr>
		<tr>
			<td>Insulin Syringe Microfine</td>
			<td>Beckton-Dickinson</td>
			<td>320924</td>
			<td>1 mL, 29 G</td>
		</tr>
		<tr>
			<td>Image Analysis</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>Mathworks</td>
			<td>-</td>
			<td>Version R2019b</td>
		</tr>
		<tr>
			<td>PMOD</td>
			<td>PMOD technologies LLC</td>
			<td></td>
			<td>Preclinical and molecular imaging software</td>
		</tr>
	</tbody>
</table>

positron emission tomography-based dose painting radiation therapy in a glioblastoma rat model using the small animal radiation research platform

A rat glioblastoma model to mimic chemo-radiation treatment of human glioblastoma in the clinic was previously established. Similar to the clinical treatment, computed tomography (CT) and magnetic resonance imaging (MRI) were combined during the treatment-planning process. Positron emission tomography (PET) imaging was subsequently added to implement sub-volume boosting using a micro-irradiation system. However, combining three imaging modalities (CT, MRI, and PET) using a micro-irradiation system proved to be labor-intensive because multimodal imaging, treatment planning, and dose delivery have to be completed sequentially in the preclinical setting. This also results in a workflow that is more prone to human error. Therefore, a user-friendly algorithm to further optimize preclinical multimodal imaging-based radiation treatment planning was implemented. This software tool was used to evaluate the accuracy and efficiency of dose painting radiation therapy with micro-irradiation by using an in silico study design. The new methodology for dose painting radiation therapy is superior to the previously described method in terms of accuracy, time efficiency, and intra- and inter-user variability. It is also an important step towards the implementation of inverse treatment planning on micro-irradiators, where forward planning is still commonly used, in contrast to clinical systems.

The feasibility of PET- and MRI-guided irradiation in a glioblastoma rat model using the SARRP to mimic the human treatment strategy has been previously described20,21,22. While the animal was fixed on a multimodality bed made in-house, it was possible to create an acceptable radiation treatment plan combining three imaging modalities: PET, MRI, and CT. In these methods, an external software package (see the Table of Mat...

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Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Here we present a protocol to perform preclinical positron emission tomography-based radiotherapy in a rat glioblastoma model using algorithms developed in-house to optimize the accuracy and efficiency.

A rat glioblastoma model to mimic chemo-radiation treatment of human glioblastoma in the clinic was previously established. Similar to the clinical ...

The protocol describes an optimized workflow for preclinical PET-based radiotherapy in a rat glioblastoma model using in-house developed algorithms. The optimized approach favors automation and is less time consuming when compared to the previously developed workflow. After anesthetizing an F98 glioblastoma rat harboring a seven to eight millimeter diameter tumor, inject 10 to 12 megabecquerel of fluorine-18 FET dissolved in 200 microliters of saline in the lateral tail vein one hour before PET acquisition.Let the animal regain consciousness while the tracer gets distributed throughout the body. Meanwhile, fix a capillary filled with the MRI PET agent for easier co-registration. Then anesthetize the animal again and place it on a multi-modality bed.Secure the animal using hook and loop fasteners to maintain a fixed position during the imaging and micro-irradiation, then wrap the animal in bubble wrap to preserve its body temperature during the multi-modality imaging and therapy. One hour after the injection of the PET tracer, perform a PET scan. Reconstruct the PET scan into a 3D volume with a 0.4 millimeter voxel size using 30 iterations of the maximum likelihood expectation maximization algorithm.Next, inject an MRI contrast agent into the tail vein, then place the rat still fixed on the multi-modality bed in the animal holder of the MRI scanner. Perform a localizer scan followed by a contrast enhanced T1 weighted spin echo sequence. Then place the animal still fixed on the multi-modality bed on a plastic holder secured onto the four-axis robotic positioning table on the micro-irradiator.Perform a high-resolution treatment planning cone beam CT requiring a total of 360 projections over 360 degrees. Reconstruct the CT images with an isotropic voxel size of 0.275 millimeters. For image co-registration, place the three image modalities into one folder, then import the converted images into MATLAB.Next, run the dose painting co-registration MATLAB script which converts the DICOM images to the NIfTI format, filters the PET image with a one millimeter full width half max Gaussian filter, crops the PET image and moves the image centers close to each other and performs the actual rigid body co-registration using statistical parametric mapping. Evaluate the result of the automatic co-registration before proceeding to treatment planning. To apply method one, run the dose painting radiation planning MATLAB script and load the three different imaging modalities into the MATLAB app.Next, place a generous bounding box around the contrast enhancement on the transverse, sagittal and frontal views of the T1 weighted MRI scan. Save the location of the bounding box, then finalize the box. Determine the contrast enhanced volume using a threshold.If multiple regions have been selected, select only the largest volume, the center of which is considered as the first isocenter to deliver a prescribed dose for radiation therapy. Expand the previously determined MRI contrast enhancement by 10 pixels in each direction. If multiple regions are detected, retain only the largest PET volume, the center of which is considered the second isocenter to deliver a prescribed dose for radiation therapy.For the first isocenter, deliver a prescribed dose of 2, 000 centigray using three non-coplanar arcs at couch positions zero, negative 45 and negative 90 degrees with a gantry rotation of 120, 120, and 60 degrees respectively. Use a fixed collimator size of 10 by 10 millimeters. However, for smaller tumors, use an appropriate size such as five by five millimeters.For the second isocenter, deliver a prescribed dose of 800 centigray using three non-coplanar arcs at couch positions zero, negative 45 and negative 90 degrees with a gantry rotation of 120, 120, and 60 degrees respectively. Use a fixed collimator size of one millimeter. Calculate the dose distribution within the animal and the beam delivery parameters.To apply method two, load the three different imaging modalities into the MATLAB app as demonstrated earlier, then place a generous bounding box around the contrast enhancement on the transverse, sagittal and frontal views of the fluorine-18 FET PET image and save the locations of the bounding box. After finalizing the bounding box, use the appropriate MATLAB script to determine V50, V60, V70, V80, and V90 in the isocenters and the jaw dimensions for each beam required to guide the motorized variable collimator. To deliver a prescribed dose of 2, 000 centigray distributed over 16 beams for V50 and a dose of 800 centigray distributed over 40 beams for V60 to V90, select the output file generated by the MATLAB script and import the 56 beams into the treatment planning software.After verifying that all 56 beams have been imported correctly, calculate the dose distribution within the animal and the beam delivery parameters. Both methods of PET-based dose painting radiation therapy were applied to three different cases. Case one has a spherical homogenous PET uptake while cases two and three have a ring-shaped uptake where the reduced PET uptake is most likely necrotic tissue.The dose volume histograms for method two are systematically closer to the ideal dose distribution than those for method one. A substantial tumor volume receives insufficient irradiation in cases two and three when treated with method one. The D90 and D50 values are considerably lower for method one than for method two.Ideally, Q volume histograms make a sharp drop at a Q value equal to one. Method two always results in dose distributions that are closer to the dose objective than method one. Furthermore, the overall Q factors for method two were superior to those for method one.This methodology is a crucial step towards inverse planning which is generally used in clinical routine and further narrows the gap between preclinical radiation research and the clinic.

positron-emission-tomography-based-dose-painting-radiation-therapy

Research

JoVE Journal

Cancer Research

Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones

Patients with a limited number of hepatic metastases and slow rates of progression can be successfully treated with local treatment approaches1,2. However, little is known about the heterogeneity of liver metastases, and animal models capable of evaluating the development of individual metastatic colonies are needed. Here, we present an advanced model of hepatic metastases that provides the ability to quantitatively visualize the development of individual tumor clones in the liver and estimate their growth kinetics and colonization efficiency. We generated a panel of monoclonal derivatives of HCT116 human colorectal cancer cells stably labeled with luciferase and tdTomato and possessing different growth properties. With a splenic injection followed by a splenectomy, the majority of these clones are able to generate hepatic metastases, but with different frequencies of colonization and varying growth rates. Using the In Vivo&#160;Imaging System (IVIS), it is possible to visualize and quantify metastasis development with in vivo luminescent and ex vivo fluorescent imaging. In addition, Diffuse Luminescent Imaging Tomography (DLIT) provides a 3D distribution of liver metastases in vivo. Ex vivo fluorescent imaging of harvested livers provides quantitative measurements of individual hepatic metastatic colonies, allowing for the evaluation of the frequency of liver colonization and the growth kinetics of metastases. Since the model is similar to clinically observed liver metastases, it can serve as a modality for detecting genes associated with liver metastasis and for testing potential ablative or adjuvant treatments for liver metastatic disease.

The ability of metastatic clones to colonize distant sites depends on their proliferation capacity and/or their ability to survive in the host microenvironment without significant proliferation. Here, we present an animal model that allows quantitative visualization of both types of liver colonization by metastatic clones.

Patients with a limited number of hepatic metastases and slow rates of progression can be successfully treated with local treatment approaches1,2. ...

PET and MRI Guided Irradiation of a Glioblastoma Rat Model Using a Micro-irradiator

For decades, small animal radiation research was mostly performed using fairly crude experimental setups applying simple single-beam techniques without the ability to target a specific or well-delineated tumor volume. The delivery of radiation was achieved using fixed radiation sources or linear accelerators producing megavoltage (MV) X-rays. These devices are unable to achieve sub-millimeter precision required for small animals. Furthermore, the high doses delivered to healthy surrounding tissue hamper response assessment. To increase the translation between small animal studies and humans, our goal was to mimic the treatment of human glioblastoma in a rat model. To enable a more accurate irradiation in a preclinical setting, recently, precision image-guided small animal radiation research platforms were developed. Similar to human planning systems, treatment planning on these micro-irradiators is based on computed tomography (CT). However, low soft-tissue contrast on CT makes it very challenging to localize targets in certain tissues, such as the brain. Therefore, incorporating magnetic resonance imaging (MRI), which has excellent soft-tissue contrast compared to CT, would enable a more precise delineation of the target for irradiation. In the last decade also biological imaging techniques, such as positron emission tomography (PET) gained interest for radiation therapy treatment guidance. PET enables the visualization of e.g., glucose consumption, amino-acid transport, or hypoxia, present in the tumor. Targeting those highly proliferative or radio-resistant parts of the tumor with a higher dose could give a survival benefit. This hypothesis led to the introduction of the biological tumor volume (BTV), besides the conventional gross target volume (GTV), clinical target volume (CTV), and planned target volume (PTV).
At the preclinical imaging lab of Ghent University, a micro-irradiator, a small animal PET, and a 7 T small animal MRI are available. The goal was to incorporate MRI-guided irradiation and PET-guided sub-volume boosting in a glioblastoma rat model.

In the past, small animal irradiation was usually performed without the ability to target a well-delineated tumor volume. The goal was to mimic the treatment of human glioblastoma in rats. Using a small animal irradiation platform, we performed MRI-guided 3D conformal irradiation with PET-based sub-volume boosting in a preclinical setting.

For decades, small animal radiation research was mostly performed using fairly crude experimental setups applying simple single-beam techniques without ...

An In Vivo Murine Sciatic Nerve Model of Perineural Invasion

Cancer cells invade nerves through a process termed perineural invasion (PNI), in which cancer cells proliferate and migrate in the nerve microenvironment. This type of invasion is exhibited by a variety of cancer types, and very frequently is found in pancreatic cancer. The microscopic size of nerve fibers within mouse pancreas renders the study of PNI difficult in orthotopic murine models. Here, we describe a heterotopic in vivo model of PNI, where we inject syngeneic pancreatic cancer cell line Panc02-H7 into the murine sciatic nerve. In this model, sciatic nerves of anesthetized mice are exposed and injected with cancer cells. The cancer cells invade in the nerves proximally toward the spinal cord from the point of injection. The invaded sciatic nerves are then extracted and processed with OCT for frozen sectioning. H&amp;E and immunofluorescence staining of these sections allow quantification of both the degree of invasion and changes in protein expression. This model can be applied to a variety of studies on PNI given its versatility. Using mice with different genetic modifications and/or different types of cancer cells allows for investigation of the cellular and molecular mechanisms of PNI and for different cancer types. Furthermore, the effects of therapeutic agents on nerve invasion can be studied by applying treatment to these mice.

An In Vivo Murine Sciatic Nerve Model of Perineural Invasion

We describe an in vivo murine model of perineural invasion by injecting syngeneic pancreatic cancer cells into the sciatic nerve. The model allows for quantification of the extent of nerve invasion, and supports investigation of the cellular and molecular mechanisms of perineural invasion.

Cancer cells invade nerves through a process termed perineural invasion (PNI), in which cancer cells proliferate and migrate in the nerve ...

The Clinical Application of Tumor Treating Fields Therapy in Glioblastoma

Glioblastoma is the most common and lethal form of brain cancer, with a median survival of 15 months after diagnosis and a 5 year survival rate of only 5% with current standard of care. Tumors often recur within 9 months following initial surgery, radiation and chemotherapy, at which point treatment options become limited. This highlights the pressing need for the development of better therapeutics to prolong survival and increase the quality of life for these patients.
Tumor Treating Fields (TTFields) therapy was developed to take advantage of the effect of low frequency alternating electrical fields on cells for cancer therapy. TTFields have been demonstrated to disrupt cells during mitosis and slow tumor growth. There is also growing evidence that they act through stimulating immune responses within exposed tumors. The advantages of TTFields therapy include its noninvasive approach and increased quality of life compared to other treatment modalities such as cytotoxic chemotherapies. The Food and Drug Administration approved TTFields therapy for the treatment of recurrent glioblastoma in 2011 and for newly diagnosed glioblastoma in 2015. We report on the effects of TTFields during mitosis, the results of electric fields modeling, and proper transducer array placement. Our protocol outlines the clinical application of TTFields on a patient post-surgery, using the second-generation device.

Glioblastoma is the most common and aggressive primary brain malignancy in adults, with most tumors recurring after initial treatment. Tumor Treating Fields (TTFields) therapy is the newest treatment modality for glioblastoma. Here, we describe the proper application of TTFields-transducer arrays on patients and discuss theory and aspects of treatment.

Glioblastoma is the most common and lethal form of brain cancer, with a median survival of 15 months after diagnosis and a 5 year survival rate of only 5% ...

Studying Normal Tissue Radiation Effects using Extracellular Matrix Hydrogels

Radiation is a therapy for patients with triple negative breast cancer. The effect of radiation on the extracellular matrix (ECM) of healthy breast tissue and its role in local recurrence at the primary tumor site are unknown. Here we present a method for the decellularization, lyophilization, and fabrication of ECM hydrogels derived from murine mammary fat pads. Results are presented on the effectiveness of the decellularization process, and rheological parameters were assessed. GFP- and luciferase-labeled breast cancer cells encapsulated in the hydrogels demonstrated an increase in proliferation in irradiated hydrogels. Finally, phalloidin conjugate staining was employed to visualize cytoskeleton organization of encapsulated tumor cells. Our goal is to present a method for fabricating hydrogels for in vitro study that mimic the in vivo breast tissue environment and its response to radiation in order to study tumor cell behavior.

This protocol presents a method for decellularization and subsequent hydrogel formation of murine mammary fat pads following ex vivo irradiation.

Radiation is a therapy for patients with triple negative breast cancer. The effect of radiation on the extracellular matrix (ECM) of healthy breast tissue ...

Cell Cycle-specific Measurement of &#947;H2AX and Apoptosis After Genotoxic Stress by Flow Cytometry

The presented method or slightly modified versions have been devised to study specific treatment responses and side effects of various anti-cancer treatments as used in clinical oncology. It enables a quantitative and longitudinal analysis of the DNA damage response after genotoxic stress, as induced by radiotherapy and a multitude of anti-cancer drugs. The method covers all stages of the DNA damage response, providing endpoints for induction and repair of DNA double-strand breaks (DSBs), cell cycle arrest and cell death by apoptosis in case of repair failure. Combining these measurements provides information about cell cycle-dependent treatment effects and thus allows an in-depth study of the interplay between cellular proliferation and coping mechanisms against DNA damage. As the effect of many cancer therapeutics including chemotherapeutic agents and ionizing radiation is limited to or strongly varies according to specific cell cycle phases, correlative analyses rely on a robust and feasible method to assess the treatment effects on the DNA in a cell cycle-specific manner. This is not possible with single-endpoint assays and an important advantage of the presented method. The method is not restricted to any particular cell line and has been thoroughly tested in a multitude of tumor and normal tissue cell lines. It can be widely applied as a comprehensive genotoxicity assay in many fields of oncology besides radio-oncology, including environmental risk factor assessment, drug screening and evaluation of genetic instability in tumor cells.

The presented method combines the quantitative analysis of DNA double-strand breaks (DSBs), cell cycle distribution and apoptosis to enable cell cycle-specific evaluation of DSB induction and repair as well as the consequences of repair failure.

The presented method or slightly modified versions have been devised to study specific treatment responses and side effects of various anti-cancer ...

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

Positron emission tomography (PET) combined with X-ray computed tomography (CT) is an important molecular imaging platform that is required for accurate diagnosis and clinical staging of a variety of diseases. The advantage of PET imaging is the ability to visualize and quantify a myriad of biological processes in vivo with high sensitivity and accuracy. However, there are multiple factors that determine image quality and quantitative accuracy of PET images. One of the foremost factors influencing image quality in PET imaging of the thorax and upper abdomen is respiratory motion, resulting in respiration-induced motion blurring of anatomical structures. Correction of these artefacts is required for providing optimal image quality and quantitative accuracy of PET images.
Several respiratory gating techniques have been developed, typically relying on acquisition of a respiratory signal simultaneously with PET data. Based on the respiratory signal acquired, PET data is selected for reconstruction of a motion-free image. Although these methods have been shown to effectively remove respiratory motion artefacts from PET images, the performance is dependent on the quality of the respiratory signal being acquired. In this study, the use of an amplitude-based optimal respiratory gating (ORG) algorithm is discussed. In contrast to many other respiratory gating algorithms, ORG permits the user to have control over image quality versus the amount of rejected motion in the reconstructed PET images. This is achieved by calculating an optimal amplitude range based on the acquired surrogate signal and a user-specified duty cycle (the percentage of PET data used for image reconstruction). The optimal amplitude range is defined as the smallest amplitude range still containing the amount of PET data required for image reconstruction. It was shown that ORG results in effective removal of respiration-induced image blurring in PET imaging of the thorax and upper abdomen, resulting in improved image quality and quantitative accuracy.

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

Amplitude-based optimal respiratory gating (ORG) effectively removes respiratory-induced motion blurring from clinical 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images. Correction of FDG-PET images for these respiratory motion artefacts improves image quality, diagnostic and quantitative accuracy. Removal of respiratory motion artefacts is important for adequate clinical management of patients using PET.

Positron emission tomography (PET) combined with X-ray computed tomography (CT) is an important molecular imaging platform that is required for accurate ...

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less aggressive breast tumors, the 5-year survival rate of TNBC patients is 77% due to their characteristic drug-resistant phenotype and metastatic burden. Toward this end, murine models have been established aimed at identifying novel therapeutic strategies limiting TNBC tumor growth and metastatic spread. This work describes a practical guide for the TNBC orthotopic model where MDA-MB-231 breast cancer cells suspended in a basement membrane matrix are implanted in the fourth mammary fat pad, which closely mimics the cancer cell behavior in humans. Measurement of tumors by caliper, lung metastasis assessment via in vivo and ex vivo imaging, and molecular detection are discussed. This model provides an excellent platform to study therapeutic efficacy and is especially suitable for the study of the interaction between the primary tumor and distal metastatic sites.

This work presets an advanced protocol to accurately assess tumor loading by detection of green fluorescent protein and bioluminescence signals as well as the integration of quantitative molecular detection technique.

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less ...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations.
The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model&#8217;s tumor-take rate is 60%&#8211;80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth.
Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups: one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy ...

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous metastasis models are available. Thus, the experimental metastasis model involving tail-vein injection of suitable cell lines is a mainstay of metastasis research. When cancer cells are injected into the lateral tail-vein, the lung is their preferred site of colonization. A potential limitation of this technique is the accurate quantification of the metastatic lung tumor burden. While some investigators count macrometastases of a pre-defined size and/or include micrometastases following sectioning of tissue, others determine the area of metastatic lesions relative to normal tissue area. Both of these quantification methods can be exceedingly difficult when the metastatic burden is high. Herein, we demonstrate an intravenous injection model of lung metastasis followed by an advanced method for quantifying metastatic tumor burden using image analysis software. This process allows for investigation of multiple end-point parameters, including average metastasis size, total number of metastases, and total metastasis area, to provide a comprehensive analysis. Furthermore, this method has been reviewed by a veterinary pathologist board-certified by the American College of Veterinary Pathologists (SEK) to ensure accuracy.

Intravenous injection of cancer cells is often used in metastasis research, but the metastatic tumor burden can be difficult to analyze. Herein, we demonstrate a tail-vein injection model of metastasis and include a novel approach to analyze the resulting metastatic lung tumor burden.

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous ...