The transformational potential of molecular radiomics

Geoffrey Currie, K. Elizabeth Hawk, Eric Rohren

Research output: Contribution to journalReview articlepeer-review

Abstract

Conventional radiomics in nuclear medicine involve hand-crafted and computer-assisted regions of interest. Recent developments in artificial intelligence (AI) have seen the emergence of AI-augmented segmentation and extraction of lower order traditional radiomic features. Deep learning (DL) affords the opportunity to extract abstract radiomic features directly from input tensors (images) without the need for segmentation. These fourth-order, high dimensional radiomics produce deep radiomics and are well suited to the data density associated with the molecular nature of hybrid imaging. Molecular radiomics and deep molecular radiomics provide insights beyond images and quantitation typical of semantic reporting. While the application of molecular radiomics using hand-crafted and computer-generated features is integrated into decision-making in nuclear medicine, the acceptance of deep molecular radiomics is less universal. This manuscript aims to provide an understanding of the language and principles associated with radiomics and deep radiomics in nuclear medicine.

Original languageEnglish (US)
JournalJournal of Medical Radiation Sciences
DOIs
StateAccepted/In press - 2022

Keywords

  • Artificial intelligence
  • artificial neural network
  • convolutional neural network
  • deep learning
  • deep radiomics
  • molecular imaging
  • nuclear medicine
  • radiomics

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'The transformational potential of molecular radiomics'. Together they form a unique fingerprint.

Cite this