TY - GEN
T1 - Deep 3D dose analysis for prediction of outcomes after liver stereotactic body radiation therapy
AU - Ibragimov, Bulat
AU - Toesca, Diego A.S.
AU - Yuan, Yixuan
AU - Koong, Albert C.
AU - Chang, Daniel T.
AU - Xing, Lei
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Accurate and precise dose delivery is the key factor for radiation therapy (RT) success. Currently, RT planning is based on optimization of oversimplified dose-volume metrics that consider all human organs to be homogeneous. The limitations of such an approach result in suboptimal treatments with poor outcomes: short survival, early cancer recurrence and radiation-induced toxicities of healthy organs. This paper pioneers the concept of deep 3D dose analysis for outcome prediction after liver stereotactic body RT (SBRT). The presented work develops tools for unification of dose plans into the same anatomy space, classifies dose plan using convolutional neural networks with transfer learning form anatomy images, and assembles the first volumetric liver atlas of the critical-to-spare liver regions. The concept is validated on prediction of post-SBRT survival and local cancer progression using a clinical database of primary and metastatic liver SBRTs. The risks of negative SBRT outcomes are quantitatively estimated for individual liver segments.
AB - Accurate and precise dose delivery is the key factor for radiation therapy (RT) success. Currently, RT planning is based on optimization of oversimplified dose-volume metrics that consider all human organs to be homogeneous. The limitations of such an approach result in suboptimal treatments with poor outcomes: short survival, early cancer recurrence and radiation-induced toxicities of healthy organs. This paper pioneers the concept of deep 3D dose analysis for outcome prediction after liver stereotactic body RT (SBRT). The presented work develops tools for unification of dose plans into the same anatomy space, classifies dose plan using convolutional neural networks with transfer learning form anatomy images, and assembles the first volumetric liver atlas of the critical-to-spare liver regions. The concept is validated on prediction of post-SBRT survival and local cancer progression using a clinical database of primary and metastatic liver SBRTs. The risks of negative SBRT outcomes are quantitatively estimated for individual liver segments.
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U2 - 10.1007/978-3-030-00934-2_76
DO - 10.1007/978-3-030-00934-2_76
M3 - Conference contribution
AN - SCOPUS:85054050078
SN - 9783030009335
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 684
EP - 692
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Fichtinger, Gabor
A2 - Davatzikos, Christos
A2 - Alberola-López, Carlos
A2 - Frangi, Alejandro F.
A2 - Schnabel, Julia A.
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -