Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics

William T. Tran, Katarzyna Jerzak, Fang I. Lu, Jonathan Klein, Sami Tabbarah, Andrew Lagree, Tina Wu, Ivan Rosado-Mendez, Ethan Law, Khadijeh Saednia, Ali Sadeghi-Naini

Research output: Contribution to journalComment/debatepeer-review

35 Scopus citations


Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining “big data”). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven computer vision and machine learning are opening new pathways that can potentially impact patient outcomes through response-guided adaptive treatments and targeted therapies based on radiomic and pathomic analysis.

Original languageEnglish (US)
Pages (from-to)S32-S41
JournalJournal of Medical Imaging and Radiation Sciences
Issue number4
StatePublished - Dec 2019


  • Breast cancer
  • digital pathology
  • informatics
  • pathology
  • pathomics
  • radiomics

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


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