Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

Artificial Intelligence in Cancer Imaging: Clinical Challenges and Applications

Institution:
1Department of Neurosurgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
2Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Publisher:
Wiley
Publication Date:
Mar-2019
Journal:
CA Cancer J Clin
Volume Number:
69
Issue Number:
2
Pages:
127-57
Citation:
CA Cancer J Clin. 2019 Mar;69(2):127-57.
PubMed ID:
30720861
PMCID:
PMC6403009
Keywords:
artificial intelligence, cancer imaging, clinical challenges, deep learning, radiomics
Appears in Collections:
NCIGT, SPL
Sponsors:
P41 EB015898/EB/NIBIB/NIH HHS/United States
R01 CA18645/CA/NCI NIH HHS/United States
R01 CA166945/CA/NCI NIH HHS/United States
U01 CA19023/CA/NCI NIH HHS/United States
U01 CA143062/CA/NCI NIH HHS/United States
U01 CA186145/CA/NCI NIH HHS/United States
U01 CA189240/CA/NCI NIH HHS/United States
U01 CA190234/CA/NCI NIH HHS/United States
U01 CA195564/CA/NCI NIH HHS/United States
U01 CA196405/CA/NCI NIH HHS/United States
U01 CA200464/CA/NCI NIH HHS/United States
U01 CA19023/CA/NCI NIH HHS/United States
U24 CA194354/CA/NCI NIH HHS/United States
U24 CA194354/CA/NCI NIH HHS/United States
Generated Citation:
Bi W.L., Hosny A., Schabath M.B., Giger M.L., Birkbak N.J., Mehrtash A., Allison T., Arnaout O., Abbosh C., Dunn I.F., Mak R.H., Tamimi R.M., Tempany C.M., Swanton C., Hoffmann U., Schwartz L.H., Gillies R.J., Huang R.Y., Aerts H.J.W.L. Artificial Intelligence in Cancer Imaging: Clinical Challenges and Applications. CA Cancer J Clin. 2019 Mar;69(2):127-57. PMID: 30720861. PMCID: PMC6403009.
Downloaded: 68 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.