Artificial intelligence in oncologic imaging

Abstract

Radiology is integral to cancer care. Compared to molecular assays, imaging has its advantages. Imaging as a noninvasive tool can assess the entirety of tumor unbiased by sampling error and is routinely acquired at multiple time points in oncological practice. Imaging data can be digitally post-processed for quantitative assessment. The ever-increasing application of Artificial intelligence (AI) to clinical imaging is challenging radiology to become a discipline with competence in data science, which plays an important role in modern oncology. Beyond streamlining certain clinical tasks, the power of AI lies in its ability to reveal previously undetected or even imperceptible radiographic patterns that may be difficult to ascertain by the human sensory system. Here, we provide a narrative review of the emerging AI applications relevant to the oncological imaging spectrum and elaborate on emerging paradigms and opportunities. We envision that these technical advances will change radiology in the coming years, leading to the optimization of imaging acquisition and discovery of clinically relevant biomarkers for cancer diagnosis, staging, and treatment monitoring. Together, they pave the road for future clinical translation in precision oncology.

Fig. 1

Emerging AI applications in oncologic imaging are seen in four broad categories: Acquisition optimization, cancer screening, tumor response assessment and treatment planning.

Fig. 2

AI has the potential to address unmet gaps in personalized cancer therapy. ML radiomics can extract and analyze quantitative data to provide tumor characterizations that can help guide therapy. Habitat imaging is a proposed ML analysis that explicitly segments whole tumors in intrinsic subregions of similar radiographic patterns to help refine delivery of radiation therapy to different parts of tumors.

Fig. 3

FDA-Approved AI software related to oncologic imaging from the ACR Data Science Institute Database.