Challenges in the target volume definition of lung cancer radiotherapy.

Radiotherapy, with or without systemic treatment has an important role in the management of lung cancer. In order to deliver the treatment accurately, the clinician must precisely outline the gross tumour volume (GTV), mostly on computed tomography (CT) images. However, due to the limited contrast between tumour and non-malignant changes in the lung tissue, it can be difficult to distinguish the tumour boundaries on CT images leading to large interobserver variation and differences in interpretation. Therefore the definition of the GTV has often been described as the weakest link in radiotherapy with its inaccuracy potentially leading to missing the tumour or unnecessarily irradiating normal tissue. In this article, we review the various techniques that can be used to reduce delineation uncertainties in lung cancer.

The impact of training and professional collaboration on the interobserver variation of lung cancer delineations: a multi-institutional study.

BACKGROUND: To assess the impact of training and interprofessional collaboration on the interobserver variation in the delineation of the lung gross tumor volume (GTVp) and lymph node (GTVln). MATERIAL AND METHODS: Eight target volume delineations courses were organized between 2008 and 2013. Specialists and trainees in radiation oncology were asked to delineate the GTVp and GTVln on four representative CT images of a patient diagnosed with lung cancer individually prior each course (baseline), together as group (interprofessional collaboration) and post-training. The mean delineated volume and local standard deviation (local SD) between the contours for each course group were calculated and compared with the expert delineations. RESULTS: A total 410 delineations were evaluated. The average local SD was lowest for the interprofessional collaboration (GTVp = 0.194 cm, GTVln = 0.371 cm) followed by the post-training (GTVp = 0.244 cm, GTVln = 0.607 cm) and baseline delineations (GTVp = 0.274 cm, GTVln: 0.718 cm). The mean delineated volume was smallest for the interprofessional (GTVp = 4.93 cm3, GTVln = 4.34 cm3) followed by the post-training (GTVp = 5.68 cm3, GTVln = 5.47 cm3) and baseline delineations (GTVp = 6.65 cm3, GTVln = 6.93 cm3). All delineations were larger than the expert for both GTVp and GTVln (p < .001). CONCLUSION: Our findings indicate that image interpretational differences can lead to large interobserver variation particularly when delineating the GTVln. Interprofessional collaboration was found to have the greatest impact on reducing interobserver variation in the delineation of the GTVln. This highlights the need to develop a clinical workflow so as to ensure that difficult cases are reviewed routinely by a second radiation oncologist or radiologist so as to minimize the risk of geographical tumor miss and unnecessary irradiation to normal tissue.

Comparison of SUVmax and SUVpeak based segmentation to determine primary lung tumour volume on FDG PET-CT correlated with pathology data.

PURPOSE: The aim of the study was to compare simple SUVmax and SUVpeak based segmentation methods for calculating the lung tumour volume, compared to a pathology ground truth. METHODS: Thirty patients diagnosed with early stage Non-Small Cell lung cancer (NSCLC) underwent surgical resection in the Netherlands between 2006 and 2008. FDG PET-CT scans for these patients were acquired within a median of 20 days before surgery. The tumour volume for each percentage SUVmax and SUVpeak threshold, with and without background correction, was calculated for each patient. The percentage threshold that provided the tumour volume that corresponded best with the pathology volume was considered to be the optimal threshold. The optimal thresholds were plotted as a function of tumour volume using a power law function and cross validated using the leave-one-out technique. RESULTS: The mean optimal percentage threshold was 50% ±â€¯10% and 62% ±â€¯15% for the SUVmax and SUVpeak without background correction respectively and 47% ±â€¯10% and 60 ±â€¯15% for the SUVmax and SUVpeak with background correction respectively. The optimal threshold curves could be fitted well with power law function. After cross validation the correlation between the effective tumour diameter in pathology and autosegmentation was 0.900 and 0.905 for the SUVmax and SUVpeak without background correction respectively and 0.913 and 0.908 for the SUVmax and SUVpeak with background correction respectively. CONCLUSION: No benefit was shown on clinical data for the SUVpeak based segmentation method over a SUVmax based one. Both methods can be used to determine the tumour volumes in resected NSCLC tumours.

Interobserver variability in the delineation of the primary lung cancer and lymph nodes on different four-dimensional computed tomography reconstructions.

PURPOSE: The study compared interobserver variation in the delineation of the primary tumour (GTVp) and lymph nodes (GTVln) between three different 4DCT reconstruction types; Maximum Intensity Projection (MIP), Mid-Ventilation (Mid-V) and Mid-Position (Mid-P). MATERIAL AND METHODS: Seven radiation oncologists delineated the GTVp and GTVln on the MIP, Mid-V and Mid-P 4DCT image reconstructions of 10 lung cancer patients. The volumes, the mean standard deviation (SD) and distribution of SD (SD/area) over the median surface contour were compared for different tumour regions. RESULTS: The overall mean delineated volume on the MIP was significantly larger (p < 0.001) than the Mid-V and Mid-P. For the GTVp the Mid-P had the lowest interobserver variation (SD = 0.261 cm), followed by Mid-V (SD = 0.314 cm) and MIP (SD = 0.330 cm) For GTVln the Mid-V had the lowest interobserver variation (SD = 0.425 cm) followed by the MIP (SD = 0.477 cm) and Mid-P (SD = 0.543 cm). The SD/area distribution showed a statistically significant difference between the MIP versus Mid-P and Mid-P versus Mid-V for both GTVp and GTVln (p < 0.001), with outliers indicating interpretation differences for GTVp located close to the mediastinum and GTVln. CONCLUSION: The Mid-P reduced the interobserver variation for the GTVp. Delineation protocols must be improved to benefit from the improved image quality of Mid-P for the GTVln.