TY - JOUR
T1 - The Use of Quantitative Imaging in Radiation Oncology
T2 - A Quantitative Imaging Network (QIN) Perspective
AU - Press, Robert H.
AU - Shu, Hui Kuo G.
AU - Shim, Hyunsuk
AU - Mountz, James M.
AU - Kurland, Brenda F.
AU - Wahl, Richard L.
AU - Jones, Ella F.
AU - Hylton, Nola M.
AU - Gerstner, Elizabeth R.
AU - Nordstrom, Robert J.
AU - Henderson, Lori
AU - Kurdziel, Karen A.
AU - Vikram, Bhadrasain
AU - Jacobs, Michael A.
AU - Holdhoff, Matthias
AU - Taylor, Edward
AU - Jaffray, David A.
AU - Schwartz, Lawrence H.
AU - Mankoff, David A.
AU - Kinahan, Paul E.
AU - Linden, Hannah M.
AU - Lambin, Philippe
AU - Dilling, Thomas J.
AU - Rubin, Daniel L.
AU - Hadjiiski, Lubomir
AU - Buatti, John M.
N1 - Funding Information:
The National Cancer Institute (NCI) has recognized the importance of QI by funding the Quantitative Imaging Network (QIN) since 2008 under the Cancer Imaging Program (6). The QIN supports use of QI for clinical decision-making in oncology through the development and validation of tools for standardizing image acquisition, processing, and analysis. These tools use analytical algorithms for data quantification to enable personalized treatment for individual patients and the prediction and monitoring of response to drugs or RT (7).
Funding Information:
Conflict of interest: H-K.G.S. reports grant nos. R01CA214557 and U01CA172027. H.S. reports grant nos. R01CA214557 and U01CA172027. N.H. reports grant nos. P01CA210961-01A1 and R01CA132870. E.G. reports grant nos. R01CA211238-01, K23CA169021-04, and U01CA15460. M.J. reports grant nos. U01CA140204 and 1R01CA190299. J.M. reports grant no. U01CA140230. B.K. reports grant nos. U01CA148131, U01CA140230, and P30CA047904. D.J. reports Canadian Institutes of Health Research (CIHR) funding reference number 137992 . M.H. reports grant no. U01CA172027 and serving as a compensated member of the Abbvie scientific advisory board and Celgene scientific advisory board. L.S. reports grant nos. U01CA211205-01 and R01CA194783-03. D.M. reports grant nos. P30CA016520-41, R01CA211337-01, R33CA225310-01, and P30CA016520. P.K. reports grant no. U01CA148131. H.L. reports grant no. U01CA148131. D.R. reports grant nos. U01CA190214 and U01CA187947. L.H. reports grant no. U01CA179106. J.B. reports grant no. U01CA140206.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/11/15
Y1 - 2018/11/15
N2 - Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.
AB - Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.
UR - http://www.scopus.com/inward/record.url?scp=85054151431&partnerID=8YFLogxK
U2 - 10.1016/j.ijrobp.2018.06.023
DO - 10.1016/j.ijrobp.2018.06.023
M3 - Article
C2 - 29966725
AN - SCOPUS:85054151431
VL - 102
SP - 1219
EP - 1235
JO - International Journal of Radiation Oncology Biology Physics
JF - International Journal of Radiation Oncology Biology Physics
SN - 0360-3016
IS - 4
ER -