Prediction of lung tumour position based on spirometry and on abdominal displacement: accuracy and reproducibility.
OBJECTIVE
A simulation investigating the accuracy and reproducibility of a tumour motion prediction model over clinical time frames is presented. The model is formed from surrogate and tumour motion measurements, and used to predict the future position of the tumour from surrogate measurements alone.


METHODS
Data were acquired from five non-small cell lung cancer patients, on 3 days. Measurements of respiratory volume by spirometry and abdominal displacement by a real-time position tracking system were acquired simultaneously with X-ray fluoroscopy measurements of superior-inferior tumour displacement. A model of tumour motion was established and used to predict future tumour position, based on surrogate input data. The calculated position was compared against true tumour motion as seen on fluoroscopy. Three different imaging strategies, pre-treatment, pre-fraction and intrafractional imaging, were employed in establishing the fitting parameters of the prediction model. The impact of each imaging strategy upon accuracy and reproducibility was quantified.


RESULTS
When establishing the predictive model using pre-treatment imaging, four of five patients exhibited poor interfractional reproducibility for either surrogate in subsequent sessions. Simulating the formulation of the predictive model prior to each fraction resulted in improved interfractional reproducibility. The accuracy of the prediction model was only improved in one of five patients when intrafractional imaging was used.


CONCLUSIONS
Employing a prediction model established from measurements acquired at planning resulted in localization errors. Pre-fractional imaging improved the accuracy and reproducibility of the prediction model. Intrafractional imaging was of less value, suggesting that the accuracy limit of a surrogate-based prediction model is reached with once-daily imaging.
Department of Medical Biophysics, University of Toronto, and Sunnybrook and Women's Health Sciences Centre, Toronto, Canada.
Jeremy D P
JD
Hoisak
Katharina E
KE
Sixel
Romeo
R
Tirona
Patrick C F
PC
Cheung
Jean-Philippe
JP
Pignol
D000005:Abdomen / Q000503:physiopathology; D000368:Aged; D000369:Aged, 80 and over; D016477:Artifacts; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000503:physiopathology / Q000532:radiotherapy*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009068:Movement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods; D011874:Radiometry / Q000379:methods; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods; D015203:Reproducibility of Results; D015656:Respiratory Mechanics; D012680:Sensitivity and Specificity; D013147:Spirometry / Q000379:methods*