Name: predicting treatment response to image-guided therapies using machine learning: an example for trans-arterial treatment of hepatocellular carcinoma
Uploaded: 2018-10-10T15:00:00
Description: Watch this Scientific Journal Video about Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma at JoVE.co

A.A. received funding support from the Office of Student Research, Yale School of Medicine.
L.J.S. receives grants from the National Institutes of Health (NIH/NCI R01CA206180), Leopoldina Postdoctoral Fellowship, and the Rolf W. Guenther Foundation of Radiological Sciences (Aachen, Germany).
J.C. receives grants from the National Institutes of Health (NIH/NCI R01CA206180), Philips Healthcare, and the German-Israeli Foundation for Scientific Research and Development (Jerusalem, Israel and Neuherberg, Germany); and scholarships from the Rolf W. Guenther Foundation of Radiological Sciences and the Charite Berlin Institute of Health Clinical Scientist Program (Berlin, Germany).
J.S.D. and M.L. receive grants from the National Institutes of Health (NIH/NCI R01CA206180) and Philips Healthcare (Best, The Netherlands).
J.F.G. receives grants from the National Institutes of Health (NIH/NCI R01CA206180), Philips Healthcare, BTG (London, United Kingdom), Boston Scientific (Marlborough, Massachusetts), and Guerbet Healthcare (Villepinte, France)

1 . Workstation Setup for Machine Learning
<ol>
	<li>Use a system with the following: 
	Intel Core 2 Duo or higher CPU at 2.0 GHz 
	4 GB or more system memory 
	POSIX-compliant operating system (Linux or Mac OS) or Microsoft Windows 7 
	User permissions for executing programs and saving files</li>
	<li>Install the following tools: 
	Anaconda Python3: https://www.anaconda.com/download 
	DICOM to NIfTI converter (dcm2niix) - https://github.com/rordenlab/dcm2niix 
	Sublime Text Editor: h...

<ol>
	<li>Benson, A., 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=NCCN+clinical+practice+guidelines+in+oncology:+hepatobiliary+cancers.">NCCN clinical practice guidelines in oncology: hepatobiliary cancers.</a> J National Comprehensive Cancer Network. 7 (4), 350-391 (2009).</li><li>Siegel, R., Miller, K., Jemal, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+statistics,+2016.">Cancer statistics, 2016.</a> CA Cancer J Clin. 66 (1), 7-30 (2016).</li><li>Bruix, 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=Clinical+management+of+hepatocellular+carcinoma.+Conclusions+of+the+Barcelona-2000+European+Association+for+the+Study+of+the+Liver+conference.">Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 European Association for the Study of the Liver conference.</a> Journal of Hepatology. 35 (3), 421-430 (2001).</li><li>Eisenhauer, E., 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=New+response+evaluation+criteria+in+solid+tumours:+revised+RECIST+guideline+(version+1.1).">New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).</a> European Journal of Cancer. 45 (2), 228-247 (2009).</li><li>Gillmore, 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=EASL+and+mRECIST+responses+are+independent+prognostic+factors+for+survival+in+hepatocellular+cancer+patients+treated+with+transarterial+embolization.">EASL and mRECIST responses are independent prognostic factors for survival in hepatocellular cancer patients treated with transarterial embolization.</a> Journal of Hepatology. 55 (6), 1309-1316 (2011).</li><li>Lin, 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=Quantitative+and+volumetric+European+Association+for+the+Study+of+the+Liver+and+Response+Evaluation+Criteria+in+Solid+Tumors+measurements:+feasibility+of+a+semiautomated+software+method+to+assess+tumor+response+after+transcatheter+arterial+chemoembolization.">Quantitative and volumetric European Association for the Study of the Liver and Response Evaluation Criteria in Solid Tumors measurements: feasibility of a semiautomated software method to assess tumor response after transcatheter arterial chemoembolization.</a> Journal of Vascular and Interventional Radiology. 23 (12), 1629-1637 (2012).</li><li>Tacher, V., 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=Comparison+of+Existing+Response+Criteria+in+Patients+with+Hepatocellular+Carcinoma+Treated+with+Transarterial+Chemoembolization+Using+a+3D+Quantitative+Approach.">Comparison of Existing Response Criteria in Patients with Hepatocellular Carcinoma Treated with Transarterial Chemoembolization Using a 3D Quantitative Approach.</a> Radiology. 278 (1), 275-284 (2016).</li><li>Pedregosa, F., 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=Scikit-learn:+Machine+Learning+in+Python.">Scikit-learn: Machine Learning in Python.</a> Journal of Machine Learning Research. 12, 2825-2830 (2011).</li><li>Bishop, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Pattern recognition and machine learning. , 738 (2006).</li><li>Alpaydin, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Introduction to machine learning. Third edition. , 613 (2014).</li><li>Kim, S., Cho, K., Oh, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+machine+learning+models+for+diagnosis+of+glaucoma.">Development of machine learning models for diagnosis of glaucoma.</a> PLoS One. 12 (5), (2017).</li><li>Son, Y., Kim, H., Kim, E., Choi, S., Lee, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+support+vector+machine+for+prediction+of+medication+adherence+in+heart+failure+patients.">Application of support vector machine for prediction of medication adherence in heart failure patients.</a> Healthcare Informatics Research. 16 (4), 253-259 (2010).</li><li>Wang, S., Summers, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Machine+learning+and+radiology.">Machine learning and radiology.</a> Medical Image Analysis. 16 (5), 933-951 (2012).</li><li>Abajian, A., 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=Predicting+Treatment+Response+to+Intra-arterial+Therapies+for+Hepatocellular+Carcinoma+with+the+Use+of+Supervised+Machine+Learning-An+Artificial+Intelligence+Concept.">Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.</a> Journal of Vascular and Interventional Radiology. , (2018).</li></ol>

Patients with hepatocellular carcinoma who are not candidates for surgical resection are offered intra-arterial therapies. Few methods exist to determine if a patient will respond pre-treatment. Post-treatment evaluation techniques rely upon changes in tumor size or tumor contrast uptake. These are called response criteria, with the most accurate being the Quantitative European Association for the Study of the Liver (qEASL) criterion. qEASL relies upon both volumetric and enhancement changes following therapy to...

Patients with hepatocellular carcinoma who are not surgical candidates are offered intra-arterial therapies1,2,3. There is no single metric that determines whether a patient will respond to an intra-arterial therapy before the treatment is administered. The objective of this study was to demonstrate a method that predicts treatment response by applying methods from machine learning. Such models provide guidance to practitioners and patients when choosing whether to proceed with a treatment.
The protocol entails a reproducible process for training and updating a model starting from primary patient data (clinical notes, demographics, laboratory data, and imaging). The data is initially parsed for specific features, with each patient represented by a set of binary features and a binary outcome target label. The outcome label is determined using an established imaging-based response criterion for hepatocellular therapy4,5,6,7. The features and target labels are passed to machine learning software that learns the mapping between features and outcomes under a specific learning model (logistic regression or random forest)8,9,10. Similar techniques have been applied in radiology and other areas of cancer research for diagnosis and treatment prediction11,12,13.
The method adapts techniques from computer science to the field of interventional radiology. Traditional significance studies in interventional radiology, and medicine in general, rely upon mono- or oligo- feature analyses. For example, the Model for End-Stage Liver Disease incorporates five clinical metrics to assess the extent of liver disease. The benefit of the proposed method is the ability to add features liberally; twenty-five features are considered in the example analysis. Additional features may be added as desired.
The technique may be applied to other radiographic interventions where pre- and post- intervention imaging data are available. For example, outcomes following percutaneous treatments could be predicted in a similar manner. The main limitation of the study is the need to manual curate features for inclusion in the model. Data curation and feature extraction is time-consuming for the practitioner and may impede clinical adoption of such machine learning models.

<table border="0" cellpadding="0" cellspacing="0" style="width:851px;" width="850">
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	<tbody>
		<tr height="105">
			<td height="105" style="height:105px;width:216px;">Computer workstation</td>
			<td style="width:123px;">N/A</td>
			<td style="width:169px;">N/A</td>
			<td style="width:343px;">Intel Core 2 Duo or higher CPU at 2.0 GHz; 4 GB or more system memory; POSIX-compliant operating system (Linux or Mac OS) or Microsoft Windows 7; User permissions for executing programs and saving files</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Anaconda Python 3</td>
			<td style="width:123px;">Anaconda, Inc.</td>
			<td style="width:169px;">Version 3.6</td>
			<td style="width:343px;">Python 3 system and libraries packaged for scientists and researchers</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">DICOM to NIfTI</td>
			<td style="width:123px;">NeuroImaging Tools &#38; Resources Collaboratory</td>
			<td style="width:169px;">Version 1.0 (4/4/2018 release)</td>
			<td style="width:343px;">Standalone program for converting DICOM imaging files to NIfTI format</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:216px;">Sublime Text Editor</td>
			<td style="width:123px;">Sublime HQ Pty Ltd</td>
			<td style="width:169px;">Version 3 (Build 3143)</td>
			<td style="width:343px;">Text-editor for writing Python code</td>
		</tr>
		<tr height="45">
			<td height="45" style="height:45px;width:216px;">Required Python Libraries</td>
			<td style="width:123px;">N/A</td>
			<td style="width:169px;">Version 3.2.25 (nltk) 
			Version 0.19.1 (scikit-learn)</td>
			<td style="width:343px;">Natural Language Toolkit (nltk) 
			Scikit-learn</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">ITK-SNAP</td>
			<td style="width:123px;">N/A</td>
			<td style="width:169px;">Version 3.6.0</td>
			<td style="width:343px;">Optional toolkit for performing segmentation of organ systems in medical images.</td>
		</tr>
	</tbody>
</table>

predicting treatment response to image-guided therapies using machine learning: an example for trans-arterial treatment of hepatocellular carcinoma

Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. The objective of this study was to develop a method to predict response to intra-arterial treatment prior to intervention.
The method provides a general framework for predicting outcomes prior to intra-arterial therapy. It involves pooling clinical, demographic and imaging data across a cohort of patients and using these data to train a machine learning model. The trained model is applied to new patients in order to predict their likelihood of response to intra-arterial therapy.
The method entails the acquisition and parsing of clinical, demographic and imaging data from N patients who have already undergone trans-arterial therapies. These data are parsed into discrete features (age, sex, cirrhosis, degree of tumor enhancement, etc.) and binarized into true/false values (e.g., age over 60, male gender, tumor enhancement beyond a set threshold, etc.). Low-variance features and features with low univariate associations with the outcome are removed. Each treated patient is labeled according to whether they responded or did not respond to treatment. Each training patient is thus represented by a set of binary features and an outcome label. Machine learning models are trained using N - 1 patients with testing on the left-out patient. This process is repeated for each of the N patients. The N models are averaged to arrive at a final model.
The technique is extensible and enables inclusion of additional features in the future. It is also a generalizable process that may be applied to clinical research questions outside of interventional radiology. The main limitation is the need to derive features manually from each patient. A popular modern form of machine learning called deep learning does not suffer from this limitation, but requires larger datasets.

The proposed method was applied to 36 patients who had undergone trans-arterial therapies for hepatocellular carcinoma. Twenty-five features were identified and binarized using steps 1-5. Five features satisfied both the variance and univariate association filters (see steps 5.1 and 5.2) and were used for model training. Each patient was labeled as either a responder or non-responder under the qEASL response criteria. The features matrix was thus a 36 x 5 array while the target labels vec...

Watch this Scientific Journal Video about Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma at JoVE.co

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. A method for predicting response to these therapies is proposed. The technique uses pre-procedural clinical, demographic, and imaging information to train machine learning models capable of predicting response prior to treatment.

Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. The objective of this ...

This method can help predict response to intra arterial therapies. Machine learning applications in interventional oncology will change the way we treat liver cancer and other diseases with image-guided therapies. This method has the potential to improve the way two important decisions are made.Generally, individuals new to this method will struggle because of the need to generate the features and to perform machine learning and code. Visual demonstration of this method is critical as the feature generation steps entail extracting those features using imaging masks. A well-annotated and clinically representative dataset is necessary to train the model.More patient data and large cohorts will be necessary to further improve such a machine learning algorithm. After deciding which clinical features to include in the model, use an appropriate natural language tool kit to parse the plain-text clinic notes into sentences to allow searching for terms of interest. Then store each patient's features in a file with one feature per line.For non-binary features, obtain the median value of each feature across all of the patients, and binarize each feature as a true or false value based on the median value. To calculate a mean liver enhancement feature from a medical image. After isolating the voxels containing the liver, enter the mean liver enhancement command.To determine the liver volume feature, enter the commands as indicated. Then calculate median values for each imaging feature and binarize the features as demonstrated. For aggregation and reduction of the features, first remove the low-variance features from consideration and read the features in the binary matrix.Next, use the variance threshold model to compute the features appearing in at least 20%of both responders and non-responders, similar to that illustrated in the table. Remove any features with a low univariate association with the outcome and filter only those features that pass the original low variant screening retaining n features where n is the number of patients. Then read in the binary matrix for each feature, similar to as illustrated in the table.After the features have been binarized and filtered, it's time to train the model. Model training is a straightforward process that fits the features to the outcome under study and can be used to make predictions about new patients. In this representative analysis of 36 patients who underwent trans arterial therapies for hepatocellular carcinoma, 25 features were identified and binarized as demonstrated with 5 features satisfying both the variants and the univariate association filters.Each patient was labeled as either a responder or non-responder under the quantitative European Association for the study of the liver response criteria. As illustrated, both the logistic regression and random forest models predicted a trans arterial chemoembolization treatment response with an overall accuracy of 78%Following this method, it is possible to make predictions on new patients by applying the training model. This machine learning technique can be universally applied to any therapy that involves pre-procedural, inter-procedural, and post-procedural imaging.

predicting-treatment-response-to-image-guided-therapies-using-machine

Research

JoVE Journal

Medicine

The Measurement and Treatment of Suppression in Amblyopia

Amblyopia, a developmental disorder of the visual cortex, is one of the leading causes of visual dysfunction in the working age population. Current estimates put the prevalence of amblyopia at approximately 1-3%1-3, the majority of cases being monocular2. Amblyopia is most frequently caused by ocular misalignment (strabismus), blur induced by unequal refractive error (anisometropia), and in some cases by form deprivation.
Although amblyopia is initially caused by abnormal visual input in infancy, once established, the visual deficit often remains when normal visual input has been restored using surgery and/or refractive correction. This is because amblyopia is the result of abnormal visual cortex development rather than a problem with the amblyopic eye itself4,5 . Amblyopia is characterized by both monocular and binocular deficits6,7 which include impaired visual acuity and poor or absent stereopsis respectively. The visual dysfunction in amblyopia is often associated with a strong suppression of the inputs from the amblyopic eye under binocular viewing conditions8. Recent work has indicated that suppression may play a central role in both the monocular and binocular deficits associated with amblyopia9,10 .
Current clinical tests for suppression tend to verify the presence or absence of suppression rather than giving a quantitative measurement of the degree of suppression. Here we describe a technique for measuring amblyopic suppression with a compact, portable device11,12 . The device consists of a laptop computer connected to a pair of virtual reality goggles. The novelty of the technique lies in the way we present visual stimuli to measure suppression. Stimuli are shown to the amblyopic eye at high contrast while the contrast of the stimuli shown to the non-amblyopic eye are varied. Patients perform a simple signal/noise task that allows for a precise measurement of the strength of excitatory binocular interactions. The contrast offset at which neither eye has a performance advantage is a measure of the "balance point" and is a direct measure of suppression. This technique has been validated psychophysically both in control13,14 and patient6,9,11 populations.
In addition to measuring suppression this technique also forms the basis of a novel form of treatment to decrease suppression over time and improve binocular and often monocular function in adult patients with amblyopia12,15,16 . This new treatment approach can be deployed either on the goggle system described above or on a specially modified iPod touch device15.

Amblyopia is a developmental disorder of the visual cortex that is often accompanied by strong suppression of one eye. We present a new technique for measuring and treating interocular suppression in patients with amblyopia that can be deployed using virtual reality goggles or a portable iPod Touch device.

Amblyopia, a developmental disorder of the visual cortex, is one of the leading causes of visual dysfunction in the working age population. Current ...

Generation of Subcutaneous and Intrahepatic Human Hepatocellular Carcinoma Xenografts in Immunodeficient Mice

In vivo experimental models of hepatocellular carcinoma (HCC) that recapitulate the human disease provide a valuable platform for research into disease pathophysiology and for the preclinical evaluation of novel therapies. We present a variety of methods to generate subcutaneous or orthotopic human HCC xenografts in immunodeficient mice that could be utilized in a variety of research applications. With a focus on the use of primary tumor tissue from patients undergoing surgical resection as a starting point, we describe the preparation of cell suspensions or tumor fragments for xenografting. We describe specific techniques to xenograft these tissues i) subcutaneously; or ii) intrahepatically, either by direct implantation of tumor cells or fragments into the liver, or indirectly by injection of cells into the mouse spleen. We also describe the use of partial resection of the native mouse liver at the time of xenografting as a strategy to induce a state of active liver regeneration in the recipient mouse that may facilitate the intrahepatic engraftment of primary human tumor cells. The expected results of these techniques are illustrated. The protocols described have been validated using primary human HCC samples and xenografts, which typically perform less robustly than the well-established human HCC cell lines that are widely used and frequently cited in the literature. In comparison with cell lines, we discuss factors which may contribute to the relatively low chance of primary HCC engraftment in xenotransplantation models and comment on technical issues that may influence the kinetics of xenograft growth. We also suggest methods that should be applied to ensure that xenografts obtained accurately resemble parent HCC tissues.

Human tumor xenografts in immunodeficient mice are valuable tools to study cancer biology. Specific protocols to generate subcutaneous and intrahepatic xenografts from human hepatocellular carcinoma cells or tumor fragments are described. Liver regeneration induced by partial hepatectomy in recipient mice is presented as a strategy to facilitate intrahepatic engraftment.

In  vivo experimental models of hepatocellular carcinoma (HCC) that recapitulate  the human disease provide a valuable platform for research into disease  ...

The Multiple Sclerosis Performance Test (MSPT): An iPad-Based Disability Assessment Tool

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report a new test, the Multiple Sclerosis Performance Test (MSPT), which represents a new approach to quantifying MS related disability. The MSPT takes advantage of advances in computer technology, information technology, biomechanics, and clinical measurement science. The resulting MSPT represents a computer-based platform for precise, valid measurement of MS severity. Based on, but extending the Multiple Sclerosis Functional Composite (MSFC), the MSPT provides precise, quantitative data on walking speed, balance, manual dexterity, visual function, and cognitive processing speed. The MSPT was tested by 51 MS patients and 49 healthy controls (HC). MSPT scores were highly reproducible, correlated strongly with technician-administered test scores, discriminated MS from HC and severe from mild MS, and correlated with patient reported outcomes. Measures of reliability, sensitivity, and clinical meaning for MSPT scores were favorable compared with technician-based testing. The MSPT is a potentially transformative approach for collecting MS disability outcome data for patient care and research. Because the testing is computer-based, test performance can be analyzed in traditional or novel ways and data can be directly entered into research or clinical databases. The MSPT could be widely disseminated to clinicians in practice settings who are not connected to clinical trial performance sites or who are practicing in rural settings, drastically improving access to clinical trials for clinicians and patients. The MSPT could be adapted to out of clinic settings, like the patient&#8217;s home, thereby providing more meaningful real world data. The MSPT represents a new paradigm for neuroperformance testing. This method could have the same transformative effect on clinical care and research in MS as standardized computer-adapted testing has had in the education field, with clear potential to accelerate progress in clinical care and research.

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report methodologic details on a new test, the Multiple Sclerosis Performance Test (MSPT). This new approach to the objective of quantification of MS related disability provides a computer-based platform for precise, valid measurement of MS severity.

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report a new test, the ...

The In Ovo Chick Chorioallantoic Membrane (CAM) Assay as an Efficient Xenograft Model of Hepatocellular Carcinoma

The chick chorioallantoic membrane (CAM) begins to develop by day 7 after fertilization and matures by day 12. The CAM is naturally immunodeficient and highly vascularized, making it an ideal system for tumor implantation. Furthermore, the CAM contains extracellular matrix proteins such as fibronectin, laminin, collagen, integrin alpha(v)beta3, and MMP-2, making it an attractive model to study tumor invasion and metastasis. Scientists have long taken advantage of the physiology of the CAM by using it as a model of angiogenesis. More recently, the CAM assay has been modified to work as an in vivo xenograft model system for various cancers that bridges the gap between basic in vitro work and more complex animal cancer models. The CAM assay allows for the study of tumor growth, anti-tumor therapies, and pro-tumor molecular pathways in a biologically relevant system that is both cost- and time-effective. Here, we describe the development of CAM xenograft model of hepatocellular carcinoma (HCC) with embryonic survival rates of up to 93% and reliable tumor take leading to growth of three-dimensional, vascularized tumors.

The In Ovo Chick Chorioallantoic Membrane CAM Assay as an Efficient Xenograft Model of Hepatocellular Carcinoma

The chick chorioallantoic membrane (CAM) is immunodeficient and highly vascularized, making it a natural in vivo model of tumor growth and angiogenesis. In this protocol, we describe a reliable method of growing three-dimensional, vascularized hepatocellular carcinoma (HCC) tumors using the CAM assay.

The chick chorioallantoic membrane (CAM) begins to develop by day 7 after fertilization and matures by day 12. The CAM is naturally immunodeficient and ...

Evaluation of the Efficacy of the H. pylori Protein HP-NAP as a Therapeutic Tool for Treatment of Bladder Cancer in an Orthotopic Murine Model

Bladder cancer is one of the most common malignancies of the urogenital tract. Intravesical injection of Bacillus Calmette-Gu&#233;rin (BCG) is the gold standard treatment for the high-grade non-muscle invasive bladder cancer (NMIBC). However, since the treatment-related side effects are relevant, newer biological response modifiers with a better benefit/side effects ratio are needed.
The tumour microenvironment can influence both tumour development and therapy efficacy. In order to obtain a good model, it is desirable to implant tumour cells in the organ from which the cancer originates.
In this protocol, we describe a method for establishing a tumour in the bladder cavity of female mice and subsequent delivery of therapeutic agents; the latter are exemplified by our use of Helicobacter pylori neutrophil activating protein (HP-NAP). A preliminary chemical burn of the mucosa, followed by the injection of mouse urothelial carcinoma cell line MB49 via urethral catheterization, enables the cells to attach to the bladder mucosa. After a period, required to allow an initial proliferation of the cells, mice are treated with HP-NAP, administrated again via catheterization. The anti-tumour activity of HP-NAP is evaluated comparing the tumour volume, the extent of necrosis and the degree of vascularization between vehicle- and HP-NAP-treated animals.

Evaluation of the Efficacy of the H. pylori Protein HP-NAP as a Therapeutic Tool for Treatment of Bladder Cancer in an Orthotopic Murine Model

Here the method to establish a syngeneic mouse model of orthotopic bladder tumour to evaluate the anti-tumour efficacy of the bacterial protein HP-NAP is described.

Bladder cancer is one of the most common malignancies of the urogenital tract. Intravesical injection of Bacillus Calmette-Gu&#233;rin (BCG) is the gold ...

A "Patient-Like" Orthotopic Syngeneic Mouse Model of Hepatocellular Carcinoma Metastasis

The majority of cancer-related deaths are caused by the metastasis of the cancer rather than the primary tumor itself. Yet, the underlying mechanisms of cancer metastasis are still unclear. Animal models are essential for elucidating the mechanisms and for evaluating novel strategies for the treatment of metastatic cancers. Here, an in-depth description of a &#8220;patient-like&#8221; orthotopic syngeneic mouse model for exploring the mechanisms of metastasis of solid organ tumors is provided. The survival surgical implantation of BNL 1ME A.7R.1 mouse hepatocellular carcinoma cells directly into the liver (the organ of origin) of the inbred wild-type immune competent laboratory mouse strain, BALB/c is described. The success and reproducibility of this methodology recommends it for widespread use in elucidating the biological mechanisms of solid organ cancer metastasis.

A &quot;Patient-Like&quot; Orthotopic Syngeneic Mouse Model of Hepatocellular Carcinoma Metastasis

The metastatic spread of cancer is the major cause of cancer-related deaths. We provide an in-depth description of our survival surgery methodology for establishing a &#8220;patient-like&#8221; orthotopic syngeneic mouse model system for studying the mechanisms of metastasis in solid organ tumors.

The majority of cancer-related deaths are caused by the metastasis of the cancer rather than the primary tumor itself. Yet, the underlying mechanisms of ...

Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer

Lung cancer is the most lethal cancer in the world. Intensive research is ongoing worldwide to identify new therapies for lung cancer. Several mouse models of lung cancer are being used to study the mechanism of cancer development and to experiment with various therapeutic strategies. However, the absence of a real-time technique to identify the development of tumor nodules in mice lungs and to monitor the changes in their size in response to various experimental and therapeutic interventions hampers the ability to obtain an accurate description of the course of the disease and its timely response to treatments. In this study, a method using a micro-computed tomography (CT) scanner for the detection of the development of lung tumors in a mouse model of lung adenocarcinoma is described. Next, we show that monthly follow-up with micro-CT can identify dynamic changes in the lung tumor, such as the appearance of additional nodules, increase in the size of previously detected nodules, and decrease in the size or complete resolution of nodules in response to treatment. Finally, the accuracy of this real-time assessment method was confirmed with end-point histological quantification. This technique paves the way for planning and conducting more complex experiments on lung cancer animal models, and it enables us to better understand the mechanisms of carcinogenesis and the effects of different treatment modalities while saving time and resources.

We describe a method for the detection of tumor nodule development in the lungs of an adenocarcinoma mouse model using micro-computed tomography and its use for monitoring changes in nodule size over time and in response to treatment. The accuracy of the assessment was confirmed with end-point histological quantification.

Lung cancer is the most lethal cancer in the world. Intensive research is ongoing worldwide to identify new therapies for lung cancer. Several mouse ...

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control seizures. However, a significant minority of patients continues to exhibit post-operative seizures, even in those cases in which the suspected source of seizures has been correctly localized and resected. The protocol presented here combines a clinical procedure routinely employed during the pre-operative evaluation of temporal lobe epilepsy (TLE) patients with a novel technique for network analysis. The method allows for the evaluation of the temporal evolution of mesial network parameters. The bilateral insertion of foramen ovale electrodes (FOE) into the ambient cistern simultaneously records electrocortical activity at several mesial areas in the temporal lobe. Furthermore, network methodology applied to the recorded time series tracks the temporal evolution of the mesial networks both interictally and during the seizures. In this way, the presented protocol offers a unique way to visualize and quantify measures that considers the relationships between several mesial areas instead of a single area.

This protocol describes a procedure to track the evolution of mesial network measures in temporal lobe epilepsy (TLE) patients. It is based on the combination of intracranial recordings with a novel numerical technique for data analysis. Specifically, we present a protocol for network analyses of foramen ovale recordings.

Approximately 30% of epilepsy patients are refractory to antiepileptic drugs. In these cases, surgery is the only alternative to eliminate/control ...

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

The prognosis of patients with metastatic cancers has improved in the past decades due to effective chemotherapy and oligometastatic surgery. For inoperable patients, local ablation therapies, such as stereotactic body radiotherapy (SBRT), can provide effective local tumor control with minimal toxicity. Because of its high precision and accuracy, SBRT delivers a higher radiation dose per fraction, is more effective, and targets smaller irradiation volumes than does conventional radiotherapy. In addition, steep dose gradients from target lesions to surrounding normal tissues are achieved using SBRT; thus, SBRT provides more effective tumor control and exhibits fewer side effects than conventional radiotherapy. The use of SBRT is prevalent for treating intracranial lesions (known as stereotactic radiosurgery); however, it is now also used for treating spinal and adrenal metastases. Because of advancements in image-guided assistance and respiratory motion management, several studies have investigated the use of SBRT for treating lung or liver tumors, which move as a patient breathes. The results of these studies have suggested that SBRT favorably controls tumors in the case of moving lesions.
Four-dimensional computed tomography (4D-CT) with an abdominal compressor (AC) is clinically convenient for effective respiratory motion management. Because this method is noninvasive and allows free breathing, its use reduces complications. Furthermore, patients consider this method convenient. Moreover, it is considered more efficient than other methods of respiratory motion management by physicians and therapists. The use of 4D-CT with an AC for treating pulmonary lesions has also been widely investigated, and the technique is gaining acceptance for treating hepatic lesions. However, the protocols for using 4D-CT with an AC for treating hepatic lesions are different from those used for treating pulmonary lesions. In this article, we describe a new protocol for SBRT with 4D-CT and an AC for treating liver metastases.

Stereotactic body radiotherapy (SBRT) requires rigorous accuracy and precision for delivering high radiation doses per fraction to small treatment volumes to improve tumor control and simultaneously reduce toxicity. Herein, we present a noninvasive and clinically convenient respiratory motion management protocol for SBRT for liver metastases.

The prognosis of patients with metastatic cancers has improved in the past decades due to effective chemotherapy and oligometastatic surgery. For ...

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

In the United States, more than 80% of all abdominal aortic aneurysms are treated by endovascular aortic aneurysm repair (EVAR). The endovascular approach warrants good early results, but adequate follow-up imaging after EVAR is imperative to maintain long-term positive outcomes. Potential graft-related complications are graft migration, infection, fraction, and endoleaks, with the last one being the most common. The most frequently used imaging after EVAR is computed tomography angiography (CTA) and duplex ultrasound. Dynamic, time-resolved computed tomography angiography (d-CTA) is a reasonably new technique to characterize the endoleaks. Multiple scans are done sequentially around the endograft during acquisition that grants good visualization of the contrast passage and graft-related complications. This high diagnostic accuracy of d-CTA can be implemented into therapy via image fusion and reduce additional radiation and contrast material exposure.
This protocol describes the technical aspects of this modality: patient selection, preliminary image review, d-CTA scan acquisition, image processing, qualitative and quantitative endoleak characterization. The steps of integrating dynamic CTA into intra-operative fluoroscopy using 2D-3D fusion-imaging to facilitate targeted embolization are also demonstrated. In conclusion, time-resolved, dynamic CTA is an ideal modality for endoleak characterization with additional quantitative analysis. It can reduce radiation and iodinated contrast material exposure during endoleak treatment by guiding interventions.

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Dynamic computed tomography angiography (CTA) imaging provides additional diagnostic value in characterizing aortic endoleaks. This protocol describes a qualitative and quantitative approach using time-attenuation curve analysis to characterize endoleaks. The technique of integrating dynamic CTA imaging with fluoroscopy using 2D-3D image fusion is illustrated for better image guidance during treatment.

In the United States, more than 80% of all abdominal aortic aneurysms are treated by endovascular aortic aneurysm repair (EVAR). The endovascular approach ...