Prediction of Head and Neck Radiotherapy Toxicity Using a Deformable 3D CNN on Longitudinal Daily CBCT Acquisitions

Le, William Trung; Bang, Chulmin; Cordelle, Philippine; Markel, Daniel; Nguyen-Tan, Phuc Felix; Bahig, Houda; Kadoury, Samuel

doi:10.1109/isbi53787.2023.10230386

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment but also allows us to assess tumor response during the entire treatment course. The CBCT-based radiomic provides a large number of potential features, as well as feature changes in those features, thus offering additional predictive values of tumor response [19][20][21][22]. Many researchers have begun to pay attention to daily images during treatment, and many publications have proved the values of delta-radiomic research during treatment [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT

Li,

Raldow,

Weidhaas

et al. 2023

Cancers

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Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

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