Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss
CoRR(2023)
摘要
Brain tumor growth is unique to each patient and extends beyond what is
visible in imaging scans, infiltrating surrounding brain tissue. Understanding
these hidden patient-specific progressions is essential for effective
therapies. Current treatment plans for brain tumors, such as radiotherapy,
typically involve delineating a uniform margin around the visible tumor on
pre-treatment scans to target this invisible tumor growth. This "one size fits
all" approach is derived from population studies and often fails to account for
the nuances of individual patient conditions. We present the framework GliODIL
which infers the full spatial distribution of tumor cell concentration from
available multi-modal imaging. This is achieved through the newly introduced
method of Optimizing the Discrete Loss (ODIL), where both data and
physics-based constraints are softly assimilated into the solution. Our test
dataset comprises 152 glioblastoma patients with pre-treatment imaging and
post-treatment follow-ups for tumor recurrence monitoring. By blending
data-driven techniques with physics-based constraints adapted for complex
cases, GliODIL enhances recurrence prediction in radiotherapy planning,
offering a superior alternative to traditional uniform margins and strict PDE
adherence.
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