Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type

Radiology. 2019 Feb;290(2):479-487. doi: 10.1148/radiol.2018180946. Epub 2018 Dec 11.

Abstract

Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis. Materials and methods This single-center retrospective analysis included radiomic features of 658 brain metastases from T1-weighted contrast material-enhanced, T1-weighted nonenhanced, and fluid-attenuated inversion recovery (FLAIR) images in 189 patients (101 women, 88 men; mean age, 61 years; age range, 32-85 years). Images were acquired over a 9-year period (from September 2007 through December 2016) with different MRI units, reflecting heterogeneous image data. Included metastases originated from breast cancer (n = 143), small cell lung cancer (n = 151), non-small cell lung cancer (n = 225), gastrointestinal cancer (n = 50), and melanoma (n = 89). A total of 1423 quantitative image features and basic clinical data were evaluated by using random forest machine learning algorithms. Validation was performed with model-external fivefold cross validation. Comparative analysis of 10 randomly drawn cross-validation sets verified the stability of the results. The classifier performance was compared with predictions from a respective conventional reading by two radiologists. Results Areas under the receiver operating characteristic curve of the five-class problem ranged between 0.64 (for non-small cell lung cancer) and 0.82 (for melanoma); all P values were less than .01. Prediction performance of the classifier was superior to the radiologists' readings. Highest differences were observed for melanoma, with a 17-percentage-point gain in sensitivity compared with the sensitivity of both readers; P values were less than .02. Conclusion Quantitative features of routine brain MR images used in a machine learning classifier provided high discriminatory accuracy in predicting the tumor type of brain metastases. © RSNA, 2018 Online supplemental material is available for this article.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Brain Neoplasms / classification
  • Brain Neoplasms / diagnostic imaging*
  • Brain Neoplasms / epidemiology
  • Brain Neoplasms / secondary*
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Machine Learning
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Neoplasms / pathology
  • Retrospective Studies