FeAture Explorer (FAE): A tool for developing and comparing radiomics models

PLoS One. 2020 Aug 17;15(8):e0237587. doi: 10.1371/journal.pone.0237587. eCollection 2020.


In radiomics studies, researchers usually need to develop a supervised machine learning model to map image features onto the clinical conclusion. A classical machine learning pipeline consists of several steps, including normalization, feature selection, and classification. It is often tedious to find an optimal pipeline with appropriate combinations. We designed an open-source software package named FeAture Explorer (FAE). It was programmed with Python and used NumPy, pandas, and scikit-learning modules. FAE can be used to extract image features, preprocess the feature matrix, develop different models automatically, and evaluate them with common clinical statistics. FAE features a user-friendly graphical user interface that can be used by radiologists and researchers to build many different pipelines, and to compare their results visually. To prove the effectiveness of FAE, we developed a candidate model to classify the clinical-significant prostate cancer (CS PCa) and non-CS PCa using the PROSTATEx dataset. We used FAE to try out different combinations of feature selectors and classifiers, compare the area under the receiver operating characteristic curve of different models on the validation dataset, and evaluate the model using independent test data. The final model with the analysis of variance as the feature selector and linear discriminate analysis as the classifier was selected and evaluated conveniently by FAE. The area under the receiver operating characteristic curve on the training, validation, and test dataset achieved results of 0.838, 0.814, and 0.824, respectively. FAE allows researchers to build radiomics models and evaluate them using an independent testing dataset. It also provides easy model comparison and result visualization. We believe FAE can be a convenient tool for radiomics studies and other medical studies involving supervised machine learning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Male
  • Multiparametric Magnetic Resonance Imaging
  • Prostatic Neoplasms / diagnostic imaging*
  • ROC Curve
  • Software
  • Supervised Machine Learning

Associated data

  • figshare/10.6084/m9.figshare.12751391.v1

Grants and funding

This study was supported in part by the National Key Research and Development Program of China (2018YFC1602800), and the Key Project of the National Natural, Science Foundation of China (61731009). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors received no specific funding for this work. Siemens Healthcare provided support in the form of salaries for authors [X. Y.], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.