scholarly journals Simple calculation using anatomical features on pre-treatment verification CT for bladder volume estimation during radiation therapy for rectal cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Nalee Kim ◽  
Hong In Yoon ◽  
Jin Sung Kim ◽  
Woong Sub Koom ◽  
Jee Suk Chang ◽  
...  

Abstract Background Despite detailed instruction for full bladder, patients are unable to maintain consistent bladder filling during a 5-week pelvic radiation therapy (RT) course. We investigated the best bladder volume estimation procedure for verifying consistent bladder volume. Methods We reviewed 462 patients who underwent pelvic RT. Biofeedback using a bladder scanner was conducted before simulation and during treatment. Exact bladder volume was calculated by bladder inner wall contour based on CT images (Vctsim). Bladder volume was estimated either by bladder scanner (Vscan) or anatomical features from the presacral promontory to the bladder base and dome in the sagittal plane of CT (Vratio). The feasibility of Vratio was validated using daily megavoltage or kV cone-beam CT before treatment. Results Mean Vctsim was 335.6 ± 147.5 cc. Despite a positive correlation between Vctsim and Vscan (R2 = 0.278) and between Vctsim and Vratio (R2 = 0.424), Vratio yielded more consistent results than Vscan, with a mean percentage error of 26.3 (SD 19.6, p < 0.001). The correlation between Vratio and Vctsim was stronger than that between Vscan and Vctsim (Z-score: − 7.782, p < 0.001). An accuracy of Vratio was consistent in megavoltage or kV cone-beam CT during treatment. In a representative case, we can dichotomize for clinical scenarios with or without bowel displacement, using a ratio of 0.8 resulting in significant changes in bowel volume exposed to low radiation doses. Conclusions Bladder volume estimation using personalized anatomical features based on pre-treatment verification CT images was useful and more accurate than physician-dependent bladder scanners. Trial registration Retrospectively registered.

2016 ◽  
Vol 61 (15) ◽  
pp. 5781-5802 ◽  
Author(s):  
Rune Slot Thing ◽  
Uffe Bernchou ◽  
Ernesto Mainegra-Hing ◽  
Olfred Hansen ◽  
Carsten Brink

2012 ◽  
Vol 195-196 ◽  
pp. 583-588
Author(s):  
Deng Wang Li ◽  
Hong Jun Wang ◽  
Da Chen ◽  
Yong Yin

Cone beam CT based image guided radiation therapy can be used to measure and correct positional errors for target and critical structures immediately prior to or during the treatment delivery. Data correlation between Planning CT images and daily CBCT images is the key issue for adaptive radiation therapy, including image registration and segmentation processing. In this paper, aiming for getting accurate liver contour structures automatically in daily CBCT images which is very low-contrast comparing the planning CT, probabilistic atlas is constructed from 50 high contrast planning CT images with manual delineation by oncologist. The incoming CBCT images are registered with the atlas using the deformable registration algorithm, and the liver contour structures are generated automatically by using the deformation map. The experiment results demonstrate the efficiency of our algorithm.


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