Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy

Abstract

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.