Automated gross tumour volume contour generation for large-scale analysis of early stage lung cancer patients planned with 4D-CT

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Early stage lung cancer patients undergoing stereotactic ablative radiotherapy receive four-dimensional computed tomography (4D-CT) for treatment planning. Often, an internal gross target volume (iGTV), which approximates the motion envelope of a tumour over the breathing cycle, is delineated without defining a gross tumour volume (GTV). However, the GTV volume and shape are important parameters for prognostic and dose modelling, and there is interest in radiomic features extracted from the GTV and surrounding tissue. We demonstrate and validate a method to generate the GTV from an iGTV contour to aid retrospective analysis on routine data.
It is possible to reconstruct the geometry of a tumour with knowledge of tumour motion and the motion envelope formed during respiration. To demonstrate this, tumour motion path was estimated with local rigid registration, and the iGTV positioned incrementally at stations along the reverse path. It is shown that tumour volume is the largest set common to the intersection of the iGTV at the different positions, so hence can be derived. This was implemented for 521 lung lesions on 4D-CT. Eleven patients with a GTV delineation performed by a radiation oncologist on a reference phase (50%) were used for validation. The generated GTV was compared to that delineated by expert using distance-to-agreement, volume, and distance between centres of mass. An overall success rate was determined by detecting registration inaccuracy and performing a quality check on the routine iGTV. For successfully generated contours, GTV volume was compared to iGTV volume in a prognostic model for overall survival.
For the validation dataset, distance-to-agreement mean (0.79-1.55mm) and standard deviation (0.68-1.51mm) was comparable to expected observer variation. Difference in volume was less than 5cm3, and average difference in position was 1.21mm. Deviations in shape and position were mainly caused by delineation interpretation differences between iGTV and GTV as opposed to algorithm performance. For the complete dataset, an acceptable contour was generated for 94% of patients using statistical and visual assessment to detect failures. Generated GTV volumes improved prognostic model performance over iGTV volumes.
A method to generate a GTV from an iGTV and 4D-CT dataset was developed. This method facilitates data analysis of early stage lung cancer patients treated in the routine setting i.e. data mining, prognostic modelling, and radiomics. Generation failure detection removes the need for visual assessment of all contours, reducing a time-consuming aspect of big-data analysis. Favourable prognostic performance of generated GTV volumes over iGTV ones demonstrates opportunities to use this methodology for future study.
Original languageEnglish
JournalMedical Physics
Early online date8 Dec 2020
Publication statusE-pub ahead of print - 8 Dec 2020


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