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

Summary

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.

Abstract

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’s tumor-take rate is 60%–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.

Introduction

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 disease^1,2. This initial invasion can be considered an early step in metastatic disease, but is typi....

Protocol

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^6,8. The most relevant steps are presented below.

1. Culture MDA-MB-231 human breast cancer cells ....

Results

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’s right proximal tibia is presented in Figure 2A. The DCE raw measurements were saved by selecting the “Export” button and choosing “DCEraw.txt” as the file name.

Subsequent calculations of the dynamic p.......

Discussion

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.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Binocular Operating Microscope	Leica	NA
ClinScan MR System	Bruker	NA
DICOM Viewer	Horos	NA	www.horosproject.org
Excel: Spreadsheet	Microsoft	NA
FCS	Sigma	F2442-500ML
Gadovist	Bayer-Schering	NA
Inveon PET/CT	Siemens	NA
Inveon Research Workplace Software	Siemens Healthcare GmbH	NA
IVIS Spectrum	PerkinElmer	NA
MDA-MB-231 human breast cancer cells	American Type Culture Collection	N/A
Open-source data visualization, machine learning and data mining toolkit.	Orange3, University of Ljubljana	NA	https://orange.biolab.si/
RPMI-1640	Invitrogen/ThermoFisher	11875093
Trypsin	Sigma	9002-07-7
Vevo 3100	VisualSonics	NA