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Principal component analysis modeling of Head‐and‐Neck anatomy using daily Cone Beam‐CT images
Author(s) -
Tsiamas Panagiotis,
BagherEbadian Hassan,
Siddiqui Farzan,
Liu Chang,
Hvid Christian A.,
Kim Joshua P.,
Brown Stephen L.,
Movsas Benjamin,
Chetty Indrin J.
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13233
Subject(s) - cone beam computed tomography , head and neck , cone beam ct , medical imaging , principal component analysis , nuclear medicine , component (thermodynamics) , head (geology) , medical physics , medicine , anatomy , physics , computed tomography , radiology , computer science , artificial intelligence , geology , surgery , geomorphology , thermodynamics
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 ( PCM s), 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 ( PC s) were extracted for all models. Analysis included cumulative and individual PC s 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 PC s for all organs, based on IP models. For PP models, the first four PC s 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 <12.9% ± 6.9% for all organs. Differences in percentage predicted spatial displacement between IP and PP models based on all organs and patients for the 1st and 2nd PC were <11.7% ± 2.2%. 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.