Abstract
Purpose: Positron emission tomography/computed tomography (PET/CT) is increasingly used for delineating gross tumor volume (GTV) in non-small-cell lung cancer (NSCLC). The methodology for contouring tumor margins remains controversial. We developed a rigorous visual protocol for contouring GTV that uses all available clinical information and studied its reproducibility in patients from a prospective PET/CT planning trial. Methods and Materials: Planning PET/CT scans from 6 consecutive patients were selected. Six "observers" (two radiation oncologists, two nuclear medicine physicians, and two radiologists) contoured GTVs for each patient using a predefined protocol and subsequently recontoured 2 patients. For the estimated GTVs and axial distances, least-squares means for each observer and for each case were calculated and compared, using the F test and pairwise t-tests. In five cases, tumor margins were also autocontoured using standardized uptake value (SUV) cutoffs of 2.5 and 3.5 and 40% SUVmax. Results: The magnitude of variation between observers was small relative to the mean (coefficient of variation [CV] = 3%), and the total variation (intraclass correlation coefficient [ICC] = 3%). For estimation of superior/inferior (SI), left/right (LR), and anterior/posterior (AP) borders of the GTV, differences between observers were also small (AP, CV = 2%, ICC = 0.4%; LR, CV = 6%, ICC = 2%; SI, CV 4%, ICC = 2%). GTVs autocontoured generated using SUV 2.5, 3.5, and 40% SUVmax differed widely in each case. An SUV contour of 2.5 was most closely correlated with the mean GTV defined by the human observers. Conclusions: Observer variation contributed little to total variation in the GTV and axial distances. A visual contouring protocol gave reproducible results for contouring GTV in NSCLC.
| Original language | English |
|---|---|
| Pages (from-to) | 1151-1157 |
| Number of pages | 7 |
| Journal | International Journal of Radiation Oncology Biology Physics |
| Volume | 77 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 15 Jul 2010 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Computed tomography
- Non-small-cell lung cancer
- Positron emission tomography
- Radiation therapy
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