User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

Mattea L. Welch*, Chris McIntosh, Andrea McNiven, Shao Hui Huang, Bei-Bei Zhang, Leonard Wee, Alberto Traverso, Brian O'Sullivan, Frank Hoebers, Andre Dekker, David A. Jaffray

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Web of Science)
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Abstract

Purpose: Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied levels of human interaction. In this work we present two pipelines designed to combine clinical, radiomic (quantified imaging), and RTx-omic (quantified radiation therapy (RT) plan) information for prediction of locoregional failure (LRF) in head and neck cancer (H&N).

Methods: Pipelines were designed to extract information and model patient outcomes based on clinical features, computed tomography (CT) imaging, and planned RT dose volumes. We predict H&N LRF using: 1) a highly user-driven pipeline that leverages modular design and machine learning for feature extraction and model development; and 2) a pipeline with minimal user input that utilizes deep learning convolutional neural networks to extract and combine CT imaging, RT dose and clinical features for model development.

Results: Clinical features with logistic regression in our highly user-driven pipeline had the highest precision recall area under the curve (PR-AUC) of 0.66 (0.33-0.93), where a PR-AUC = 0.11 is considered random.

CONCLUSIONS: Our work demonstrates the potential to aggregate features from multiple specialties for conditional-outcome predictions using pipelines with varied levels of human interaction. Most importantly, our results provide insights into the importance of data curation and quality, as well as user, data and methodology bias awareness as it pertains to result interpretation in user controlled pipelines.

Original languageEnglish
Pages (from-to)145-152
Number of pages8
JournalPhysica Medica: European journal of medical physics
Volume70
DOIs
Publication statusPublished - Feb 2020
EventInternational Conference on the Use of Computers in Radiation Therapy (ICCR) / International Conference on Monte Carlo Techniques for Medical Applications (MCMA) - Montreal, Canada
Duration: 17 Jun 201921 Jun 2019

Keywords

  • Head and neck
  • Outcome prediction
  • Deep learning
  • Machine learning
  • User-controlled
  • Bias
  • PROGNOSTIC-FACTORS
  • RADIOTHERAPY
  • CANCER
  • RADIOMICS
  • CARCINOMA
  • SURVIVAL
  • IMPACT
  • RISK

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