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

Abstract

Purpose: Patients with early-stage lung cancer 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 tumor over the breathing cycle, is delineated without defining a gross tumor 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. Method: It is possible to reconstruct the geometry of a tumor with knowledge of tumor motion and the motion envelope formed during respiration. To demonstrate this, the tumor motion path was estimated with local rigid registration, and the iGTV positioned incrementally at stations along the reverse path. It is shown that the tumor volume is the largest set common to the intersection of the iGTV at these positions, 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 the expert using distance-to-agreement (DTA), 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. Results: For the validation dataset, DTA mean (0.79-1.55 mm) and standard deviation (0.68-1.51 mm) were comparable to expected observer variation. Difference in volume was < 5 cm 3 , and average difference in position was 1.21 mm. Deviations in shape and position were mainly caused by observer differences in iGTV and GTV interpretation 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. Conclusion: A method to generate a GTV from an iGTV and 4D-CT dataset was developed. This method facilitates data analysis of patients with early-stage lung cancer treated in the routine setting, that is, data mining, prognostic modeling, and radiomics. Generation failure detection removes the need for visual assessment of all contours, reducing a time-consuming aspect of big-data analysis. Favorable prognostic performance of generated GTV volumes over iGTV ones demonstrates opportunities to use this methodology for future study.

Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

“…The counterintuitive result of Region 1 is explained by considering the mean EQD2 dose inside each of the regions identified by the dose parameter (Supplementary Figure 13). In region 1, the median value is 55 Gy with range 74-92 Gy, which is significantly higher than 9 Gy (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) in R2 and 14 Gy (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41) in R3. Considering the difference in dose scales, a large SD in this region would not lead to underdosage of microscopic disease.…”

“…An in-house validated technique was used to segment the GTV on all phases retrospectively ( 20 ). Briefly, local rigid registration was used to estimate the translation required to match the tumour position on each phase to a reference phase (50%).…”

“…From the estimated motion, the GTV was derived and transferred to all 4D phases. All GTVs were visualised by a single observer and approved as they outlined the visible macroscopic tumour ( 20 ). A by-product of GTV generation was two additional clinical variables: GTV volume and the amplitude of tumour motion.…”

Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy

“…Equally as important for future consideration, an automated method was used to generate a GTV on every phase, allowing for large-scale analysis without the need for manual contouring. This methodology could introduce small errors in the shape of the tumour, but it is validated to work well within the limits of observer variation and improve prognostic modelling (Davey et al 2020a ). However, the tumour border largely influences radiomic features (Pavic et al 2018 ), and a potential limitation is that only a single segmentation technique was tested.…”

“…An in-house, validated method was implemented to generate a GTV on a reference phase (50%) from the iGTV for all patients (Davey et al 2020a ). Briefly, local-rigid registration was used to obtain the translation set required to match the tumour position on each phase to the reference i.e.…”