Name: machine learning algorithms for early detection of bone metastases in an experimental rat model
Uploaded: 2020-08-16T13:00:00
Description: Watch this Scientific Journal Video about Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model at JoVE.com

This work was supported by the German Research Foundation (DFG, Collaborative Research Centre CRC 1181, subproject Z02; Priority Programme &#956;Bone, projects BA 4027/10-1 and BO 3811), including additional support for the scanning devices (INST&#160;410/77-1&#160;FUGG and INST&#160;410/93-1&#160;FUGG), and by the Emerging Fields Initiative (EFI) &#8220;Big Thera&#8221; of the Friedrich-Alexander-University Erlangen-N&#252;rnberg.

All care and experimental procedures were performed in accordance with national and regional legislation on animal protection, and all animal procedures were approved by the State Government of Franconia, Germany (reference number 55.2 DMS-2532-2-228).
1. Induction of breast cancer bone metastases in the right hind leg of nude rats
NOTE: A detailed description of the induction of breast cancer bone metastases in nude rats has been published elsewhere...

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ML algorithms are powerful tools used to integrate several predictive features into a combined model and obtain an accuracy that exceeds that of its separate constituents when used alone. Nonetheless, the actual result depends on several critical steps. First, the ML algorithm used is a crucial factor, because different ML algorithms yield different results. The algorithm used in this protocol is an avNNet, but other promising algorithms include Extreme Gradient Boosting21 or Random Forests. The c...

The purpose of this protocol is to integrate several functional imaging parameters from MRI and PET/CT into a model-averaged neural network (avNNet) ML algorithm. This algorithm predicts the growth of macrometastases in a rat model of breast cancer bone metastases at an early timepoint, when macroscopic changes within the bone are not yet visible.
Prior to the growth of macrometastases, a bone marrow invasion of disseminated tumor cells occurs, commonly referred to as micrometastatic disease1,2. This initial invasion can be considered an early step in metastatic disease, but is typically missed during conventional staging examinations3,4. Although the currently available imaging modalities cannot detect bone marrow microinvasion when used alone, a combination of imaging parameters yielding information on vascularization and metabolic activity has been shown to perform better5. This complementary benefit is achieved by combining different imaging parameters into an avNNet, which is an ML algorithm. Such an avNNet allows for the reliable prediction of bone macrometastases formation before any visible metastases are present. Therefore, integrating imaging biomarkers into an avNNet could serve as a surrogate parameter for bone marrow microinvasion and early metastatic disease.
To develop the protocol, a previously described model of breast cancer bone metastases in nude rats was used6,7,8. The advantage of this model is its site-specificity, meaning that the animals develop bony metastases exclusively in their right hind leg. However, the tumor-take rate of this approach is 60%&#8211;80%, so a considerable number of the animals do not develop any metastases during the study. Using imaging modalities such as MRI and PET/CT, the presence of metastases is detectable from day 30 postinjection (PI). At earlier time points (e.g., 10 PI) imaging does not distinguish between animals that will develop metastatic disease and those will not (Figure 1).
An avNNet trained on functional imaging parameters acquired on day 10 PI, as described in the following protocol, reliably predicts or excludes the growth of macrometastases within the following ~3 weeks. Neural Networks combine artificial nodes within different layers. In the study protocol, the functional imaging parameters for bone marrow blood supply and metabolic activity represent the bottom layer, while the prediction of malignancy represents the top layer. An additional intermediate layer contains hidden nodes that are connected to both the top and the bottom layer. The strength of the connections between the different nodes is updated during the training of the network to perform the respective classification task with high accuracy9. The accuracy of such a neural network can be further increased by averaging the outputs of several models, resulting in an avNNet10.

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			<td height="21" style="height:21px;width:241px;">Binocular Operating Microscope</td>
			<td style="width:239px;">Leica</td>
			<td style="width:154px;">NA</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">ClinScan MR System</td>
			<td style="width:239px;">Bruker</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">DICOM Viewer</td>
			<td style="width:239px;">Horos</td>
			<td style="width:154px;">NA</td>
			<td style="width:167px;">www.horosproject.org</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Excel: Spreadsheet</td>
			<td style="width:239px;">Microsoft</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">FCS</td>
			<td style="width:239px;">Sigma</td>
			<td style="width:154px;">F2442-500ML</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Gadovist</td>
			<td style="width:239px;">Bayer-Schering</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
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			<td height="21" style="height:21px;width:241px;">Inveon PET/CT</td>
			<td style="width:239px;">Siemens</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
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			<td height="42" style="height:42px;width:241px;">Inveon Research Workplace Software</td>
			<td style="width:239px;">Siemens Healthcare GmbH</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
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			<td height="21" style="height:21px;width:241px;">IVIS Spectrum</td>
			<td style="width:239px;">PerkinElmer</td>
			<td style="width:154px;">NA</td>
			<td></td>
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			<td height="42" style="height:42px;width:241px;">MDA-MB-231 human breast cancer cells</td>
			<td style="width:239px;">American Type Culture Collection</td>
			<td style="width:154px;">N/A</td>
			<td></td>
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		<tr height="63">
			<td height="63" style="height:63px;width:241px;">Open-source data visualization, machine learning and data mining toolkit.</td>
			<td style="width:239px;">Orange3, University of Ljubljana</td>
			<td style="width:154px;">NA</td>
			<td>https://orange.biolab.si/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">RPMI-1640</td>
			<td>Invitrogen/ThermoFisher</td>
			<td align="right" style="width:154px;">11875093</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Trypsin</td>
			<td style="width:239px;">Sigma</td>
			<td style="width:154px;">9002-07-7</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:241px;">Vevo 3100</td>
			<td style="width:239px;">VisualSonics</td>
			<td style="width:154px;">NA</td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

The rats recovered quickly from the surgery and injection of the MDA-MB-231 breast cancer cells and were then subjected to MR- and PET/CT imaging on days 10 and 30 PI (Figure 1). A representative DCE analysis of a rat&#8217;s right proximal tibia is presented in Figure 2A. The DCE raw measurements were saved by selecting the &#8220;Export&#8221; button and choosing &#8220;DCEraw.txt&#8221; as the file name.
Subsequent...

Watch this Scientific Journal Video about Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model at JoVE.com

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

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 ...

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