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Summary

Abstract

Introduction

Protocol

Representative Results

Discussion

Acknowledgements

Materials

References

Medicine

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

Published: October 10th, 2018

DOI:

10.3791/58382

1Department of Radiology and Biomedical Imaging, Yale School of Medicine, 2Department of Diagnostic and Interventional Radiology, Universitätsmedizin Charité Berlin, 3Department of Biomedical Engineering, Yale School of Engineering and Applied Science, 4Philips Research North America, 5Prescience Labs

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 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.

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 a....

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1 . Workstation Setup for Machine Learning

  1. 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
  2. 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.......

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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.......

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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.......

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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 Ins....

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Name Company Catalog Number Comments
Computer workstation N/A N/A 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
Anaconda Python 3 Anaconda, Inc. Version 3.6 Python 3 system and libraries packaged for scientists and researchers
DICOM to NIfTI NeuroImaging Tools & Resources Collaboratory Version 1.0 (4/4/2018 release) Standalone program for converting DICOM imaging files to NIfTI format
Sublime Text Editor Sublime HQ Pty Ltd Version 3 (Build 3143) Text-editor for writing Python code
Required Python Libraries N/A Version 3.2.25 (nltk)
Version 0.19.1 (scikit-learn)
Natural Language Toolkit (nltk)
Scikit-learn
ITK-SNAP N/A Version 3.6.0 Optional toolkit for performing segmentation of organ systems in medical images.

  1. Benson, A., et al. NCCN clinical practice guidelines in oncology: hepatobiliary cancers. J National Comprehensive Cancer Network. 7 (4), 350-391 (2009).
  2. Siegel, R., Miller, K., Jemal, A. Cancer statistics, 2016. CA Cancer J Clin. 66 (1), 7-30 (2016).
  3. Bruix, J., et al. Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 European Association for the Study of the Liver conference. Journal of Hepatology. 35 (3), 421-430 (2001).
  4. Eisenhauer, E., et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). European Journal of Cancer. 45 (2), 228-247 (2009).
  5. Gillmore, R., et al. EASL and mRECIST responses are independent prognostic factors for survival in hepatocellular cancer patients treated with transarterial embolization. Journal of Hepatology. 55 (6), 1309-1316 (2011).
  6. Lin, M., et al. 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. Journal of Vascular and Interventional Radiology. 23 (12), 1629-1637 (2012).
  7. Tacher, V., et al. Comparison of Existing Response Criteria in Patients with Hepatocellular Carcinoma Treated with Transarterial Chemoembolization Using a 3D Quantitative Approach. Radiology. 278 (1), 275-284 (2016).
  8. Pedregosa, F., et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 12, 2825-2830 (2011).
  9. Bishop, C. . Pattern recognition and machine learning. , 738 (2006).
  10. Alpaydin, E. . Introduction to machine learning. Third edition. , 613 (2014).
  11. Kim, S., Cho, K., Oh, S. Development of machine learning models for diagnosis of glaucoma. PLoS One. 12 (5), (2017).
  12. Son, Y., Kim, H., Kim, E., Choi, S., Lee, S. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthcare Informatics Research. 16 (4), 253-259 (2010).
  13. Wang, S., Summers, R. Machine learning and radiology. Medical Image Analysis. 16 (5), 933-951 (2012).
  14. Abajian, A., et al. Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept. Journal of Vascular and Interventional Radiology. , (2018).

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