Principal component analysis modeling of Head-and-Neck anatomy using daily Cone Beam-CT images.

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 <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.

Cone-beam computed tomography (CBCT) for adaptive image guided head and neck radiation therapy.

BACKGROUND: Adaptive (ART) and image guided radiation therapy (IGRT) can improve target coverage and reduce unnecessary irradiation of organs at risk (OAR). The purpose of this study is to report the results of using mid-course imaging and a novel workflow with cone-beam computed tomography (CBCT) surveillance of dose to OAR to trigger adaptive replanning in head and neck radiotherapy. MATERIAL AND METHODS: Impact of radiation therapist (RTT) managed match protocol and mid-course imaging was assessed in two cohorts of consecutive patients receiving RT to the head and neck region, using computed tomography (CT) and CBCT-based dose verification respectively. In the CBCT cohort, patients at high risk of xerostomia received weekly dose surveillance, while low-risk patients received a mid-course CBCT review. For weekly surveillance, predicted total doses to parotid glands, spinal cord and brainstem were calculated. If predicted mean dose to parotid glands increased by >2 Gy or constraints to brainstem or spinal cord were exceeded, replanning was performed. RESULTS: None had replanning triggered by mid-course imaging. In the CBCT cohort, weekly surveillance of 40 patients yielded minimal reduction in mean dose to parotid glands of 0.65 Gy (range0.4-1 Gy ) for three patients. Patients were surveilled averagely 4.5 times during treatment. Time consumption per CBCT/week was 22 min (range 17-38). Number of patients needed to see to achieve any dose reduction to parotid glands was 13 or the equivalent of 22 working-hours. CONCLUSION: The tested dose surveillance algorithm resulted in a minimal dose reduction ( ≤1 Gy) to parotid glands for three of 40 patients. The proposed algorithm and workflow is thus not sustainable. Mid-course dose verification did not provide added benefit and can be safely omitted in the presence of closely monitored daily IGRT. Daily image guidance and match protocol is a safe and efficient method for identifying patients requiring adaptive replanning.

Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy.

BACKGROUND: Autocontouring improves workflow in computed tomography (CT)-based dose planning, but could also potentially play a role for optimal use of daily cone beam CT (CBCT) in adaptive radiotherapy. This study aims to determine the accuracy of a deformable image registration (DIR) algorithm for organs at risk (OAR) in the neck region, when applied to CBCT. MATERIAL AND METHODS: For 30 head and neck cancer (HNC) patients 14 OARs including parotid glands, swallowing structures and spinal cord were delineated. Contours were propagated by DIR from CT to the CBCTs of the first and last treatment fraction. An indirect approach, propagating contours to the first CBCT and from there to the last CBCT was also tested. Propagated contours were compared to manually corrected contours by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Dose was recalculated on CBCTs and dosimetric consequences of uncertainties in DIR were reviewed. RESULTS: Mean DSC values of ≥0.8 were considered adequate and were achieved in tongue base (0.91), esophagus (0.85), glottic (0.81) and supraglottic larynx (0.83), inferior pharyngeal constrictor muscle (0.84), spinal cord (0.89) and all salivary glands in the first CBCT. For the last CBCT by direct propagation, adequate DSC values were achieved for tongue base (0.85), esophagus (0.84), spinal cord (0.87) and all salivary glands. Using indirect propagation only tongue base (0.80) and parotid glands (0.87) were ≥0.8. Mean relative dose difference between automated and corrected contours was within ±2.5% of planed dose except for esophagus inlet (-4.5%) and esophagus (5.0%) for the last CBCT using indirect propagation. CONCLUSION: Compared to manually corrected contours, the DIR algorithm was accurate for use in CBCT images of HNC patients and the minor inaccuracies had little consequence for mean dose in most clinically relevant OAR. The method can thus enable a more automated segmentation of CBCT for use in adaptive radiotherapy.