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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 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.
1 . Workstation Setup for Machine Learning
2 . Feature Extraction from Plaintext Clinical Notes and Structured Clinical Data
3 . Feature Extraction from Medical Images
NOTE: See Step 3 Supplementary Materials for Code Examples.
4. Feature Aggregation and Reduction
NOTE: See Step 4 Supplementary Materials for Code Examples.
Patient | Age > 60 | Male Sex | Albumin < 3.5 | Presence of Cirrhosis | Hepatitis C Present | mean liver enhancement > 50 | liver volume > 20000 |
1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 |
2 | 1 | 1 | 1 | 0 | 0 | 0 | 0 |
3 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
Patient | Age > 60 | Male Sex | Albumin < 3.5 | Presence of Cirrhosis | Hepatitis C Present | mean liver enhancement > 50 | liver volume > 20000 | qEASL Responder |
1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 |
2 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 |
3 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 |
Patient | Age > 60 | Hepatitis C Present | mean liver enhancement > 50 | qEASL Responder |
1 | 0 | 0 | 1 | 1 |
2 | 1 | 0 | 0 | 1 |
3 | 0 | 1 | 0 | 0 |
Patient | Hepatitis C Present | mean liver enhancement > 50 | qEASL Responder |
1 | 0 | 1 | 1 |
2 | 0 | 0 | 1 |
3 | 1 | 0 | 0 |
5 . Model Training and Testing
See Step 5 Supplementary Materials for Code Examples
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...
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...
A.A. works as a software consult for Health Fidelity, Inc. that employs similar machine learning techniques on clinical notes for optimizing medical reimbursement.
J.F.G. receives personal fees from Guerbet Healthcare, BTG, Threshold Pharmaceuticals (San Francisco, California), Boston Scientific, and Terumo (Elkton, Maryland); and has a paid consultancy for Prescience Labs (Westport, Connecticut).
None of the other authors have identified a conflict of interest.
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)
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. |
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