Principal component analysis modeling of Head-and-Neck anatomy using daily Cone Beam-CT images.
Tsiamas P, Bagher-Ebadian H, Siddiqui F, Liu C, Hvid CA, Kim JP, Brown SL, Movsas B, and Chetty IJ. Principal component analysis (pca) modeling of head-and-neck anatomy using daily cbct images. Med Phys 2018;Dec;45(12):5366-5375.
PURPOSE: To model Head-and-Neck anatomy from daily Cone Beam-CT (CBCT) images over the course of fractionated radiotherapy using principal component analysis (PCA).
METHODS AND MATERIALS: Eighteen oropharyngeal Head-and-Neck cancer patients, treated with volumetric modulated arc therapy (VMAT), were included in this retrospective study. Normal organs, including the parotid and submandibular glands, mandible, pharyngeal constrictor muscles (PCMs), and spinal cord were contoured using daily CBCT image datasets. PCA models for each organ were developed for individual patients (IP) and the entire patient cohort/population (PP). The first 10 principal components (PCs) were extracted for all models. Analysis included cumulative and individual PCs for each organ and patient, as well as the aggregate organ/patient population; comparisons were made using the root-mean-square (RMS) of the percentage predicted spatial displacement for each PC.
RESULTS: Overall, spatial displacement prediction was achieved at the 95% confidence level (CL) for the first three to four PCs for all organs, based on IP models. For PP models, the first four PCs predicted spatial displacement at the 80%-89% CL. Differences in percentage predicted spatial displacement between mean IP models for each organ ranged from 2.8% ± 1.8% (1st PC) to 0.6% ± 0.4% (4th PC). Differences in percentage predicted spatial displacement between IP models vs the mean IP model for each organ based on the 1st PC were
CONCLUSION: Tissue changes during fractionated radiotherapy observed on daily CBCT in patients with Head-and-Neck cancers, were modeled using PCA. In general, spatial displacement for organs-at-risk was predicted for the first 4 principal components at the 95% confidence levels (CL), for individual patient (IP) models, and at the 80%-89% CL for population-based patient (PP) models. The IP and PP models were most predictive of changes in glandular organs and pharyngeal constrictor muscles, respectively.
Medical Subject Headings
Cone-Beam Computed Tomography; Head and Neck Neoplasms; Humans; Image Processing, Computer-Assisted; Principal Component Analysis