SUMMARY
Magnetic resonance imaging-guided adaptive radiotherapy (MRgART) allows for safer treatment of otherwise
difficult-to-treat soft-tissue cancers in the abdomen, such as inoperable pancreatic cancers that occur close to
highly mobile and radiosensitive gastrointestinal (GI) organs. MRgART enables daily replanning to compensate
for organ shape variations through improved visualization of the tumor and nearby organs. However, nearby
abdominal organs move considerably between and during treatment fractions and, crucially, accurate tracking
of the dose distribution accumulated in those tissues is currently unavailable. Consequently, tumor prescription
coverage is still often constrained to sub-optimal levels by design to conservatively reduce the risk of radiation
toxicity to GI organs. We hypothesize that accurate estimates of doses to the surrounding mobile healthy organs,
accumulated over all fractions, would enable a less conservative and more effective treatment of the full extent
of the disease. Hence, the key clinical need we will address, to ensure improved local control and to reduce rates
of local tumor progression and morbidity, particularly in the tumors adjacent to luminal GI organs, is the
development of reliably accurate deformable image registration (DIR) methods to estimate the spatial dose
accumulated to the mobile GI luminal organs throughout treatment from previous fractions. This proposal
addresses the key need by developing, rigorously validating, and systematically measuring the gain in target
coverage with an innovative deep learning DIR dose accumulation utilizing a cohort of virtual digital twins. In
Aim 1, We will develop patient-specific virtual digital twin cohorts modeling 21 different temporally varying
realistic GI motions encompassing respiratory and digestive motion. The twins will combine analytical modeling
with the widely used XCAT digital phantoms. In Aim 2, the virtual digital twins will be used to optimize and
rigorously validate our innovative progressive registration-segmentation deep learning network for GI organs.
The key technical novelty of this approach is its ability to perform spatio-temporally varying regularization to
model large deformations, not possible with most DIR methods. In Aim 3, the potential clinical gain of using AI-
DIR dose accumulation compared with the clinical standard with conservative limits to the high dose region will
be systematically simulated with a variety of GI tract motion using the VDT datasets. Potential impact: The
developed and validated AI-DIR techniques, validated for realistic physiologic GI motions, will be applicable
beyond pancreatic tumors and will apply to other GI soft-tissue cancers. Ultimately, the availability of well-
validated dose accumulation techniques could enable clinicians to quantitatively determine the accumulated
radiation dose distribution to luminal GI organs and appropriately account for the spillover radiation, thus leading
to more personalized, safer, and possibly more effective radiation treatments.
