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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Representative Results
  • Discussion
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

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.

Abstract

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.

Introduction

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....

Protocol

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.......

Representative Results

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.......

Discussion

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.......

Acknowledgements

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....

Materials

NameCompanyCatalog NumberComments
Computer workstationN/AN/AIntel 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 3Anaconda, Inc.Version 3.6Python 3 system and libraries packaged for scientists and researchers
DICOM to NIfTINeuroImaging Tools & Resources CollaboratoryVersion 1.0 (4/4/2018 release)Standalone program for converting DICOM imaging files to NIfTI format
Sublime Text EditorSublime HQ Pty LtdVersion 3 (Build 3143)Text-editor for writing Python code
Required Python LibrariesN/AVersion 3.2.25 (nltk)
Version 0.19.1 (scikit-learn)
Natural Language Toolkit (nltk)
Scikit-learn
ITK-SNAPN/AVersion 3.6.0Optional toolkit for performing segmentation of organ systems in medical images.

References

  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 C....

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Machine LearningImage guided TherapyHepatocellular CarcinomaTrans arterial TreatmentFeature ExtractionBinarizationModel TrainingOutcome Prediction

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