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

Mercieca, Susan; Belderbos, J.; Loon, J. Van; Gilhuijs, Kenneth G. A.; Julyan, P. J.; Herk, Marcel van

doi:10.1016/j.radonc.2018.06.028

Cited by 11 publications

(13 citation statements)

References 35 publications

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“…Most of these previous studies exploited arbitrary values of %SUV max image thresholds, despite the well-established limited accuracy and robustness of such methodology for the segmentation of PET image based functional volumes 13–16 . Indeed, optimal threshold values greatly depend on lesion size and contrast 17 , in addition to being very challenging to determine in case of high intratumor uptake heterogeneity where relationship between maximum intensity and overall contrast can be quite different than for homogeneous lesions. A recent MICCAI challenge further highlighted these limitations, as both implemented fixed thresholds (40 and 50% of the maximum) were amongst the worst ranked methods 18 .…”

Section: Introductionmentioning

confidence: 99%

Revisiting the identification of tumor sub-volumes predictive of residual uptake after (chemo)radiotherapy: influence of segmentation methods on 18F-FDG PET/CT images

Hatt

Tixier

Desseroit

et al. 2019

Sci Rep

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Our aim was to evaluate the impact of the accuracy of image segmentation techniques on establishing an overlap between pre-treatment and post-treatment functional tumour volumes in 18FDG-PET/CT imaging. Simulated images and a clinical cohort were considered. Three different configurations (large, small or non-existent overlap) of a single simulated example was used to elucidate the behaviour of each approach. Fifty-four oesophageal and head and neck (H&N) cancer patients treated with radiochemotherapy with both pre- and post-treatment PET/CT scans were retrospectively analysed. Images were registered and volumes were determined using combinations of thresholds and the fuzzy locally adaptive Bayesian (FLAB) algorithm. Four overlap metrics were calculated. The simulations showed that thresholds lead to biased overlap estimation and that accurate metrics are obtained despite spatially inaccurate volumes. In the clinical dataset, only 17 patients exhibited residual uptake smaller than the pre-treatment volume. Overlaps obtained with FLAB were consistently moderate for esophageal and low for H&N cases across all metrics. Overlaps obtained using threshold combinations varied greatly depending on thresholds and metrics. In both cases overlaps were variable across patients. Our findings do not support optimisation of radiotherapy planning based on pre-treatment 18FDG-PET/CT image definition of high-uptake sub-volumes. Combinations of thresholds may have led to overestimation of overlaps in previous studies.

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Section: Introductionmentioning

confidence: 99%

Revisiting the identification of tumor sub-volumes predictive of residual uptake after (chemo)radiotherapy: influence of segmentation methods on 18F-FDG PET/CT images

Hatt

Tixier

Desseroit

et al. 2019

Sci Rep

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“…Mercieca et al ., in a study of 30 patients, demonstrated that the mean optimal percentage threshold of 47% ± 10% of SUV max based tumor volume correlated best with the pathological tumor volume. [ 17 ] The optimal threshold of the above two studies closely matches our optimal threshold of 36%, and the difference could possibly be because we used the largest tumor dimension rather than tumor volume. van Loon et al .…”

Section: Discussionmentioning

confidence: 75%

“…[ 4 15 ] However, very few published studies compared auto delineated maximum tumor dimension with the pathological tumor size of surgical specimens, which is considered to be the gold standard. [ 4 14 17 ] PET/CT based tumor volume corresponds more accurately to the pathological tumor volume as compared to the CT based tumor. [ 4 ] Furthermore, tumor delineation using PET/CT improves precision, especially in poorly defined tumors and reduces interobserver variation.…”

Section: Discussionmentioning

confidence: 99%

“…There are limitations of using single PET threshold for auto delineation of tumor as reported by some studies. [ 16 17 22 23 ] The optimal percentage threshold for SUV max ranged from 15% to 60% in some studies, with 42% SUV max being commonly used. [ 4 11 12 17 ] Uniformity of SUV within the tumor is crucial for deciding single threshold.…”

Section: Discussionmentioning

confidence: 99%

“…[ 16 17 22 23 ] The optimal percentage threshold for SUV max ranged from 15% to 60% in some studies, with 42% SUV max being commonly used. [ 4 11 12 17 ] Uniformity of SUV within the tumor is crucial for deciding single threshold. Because of factors such as tumor size, hypoxia, and necrosis, which are more likely to occur in larger tumors there appears to be a lack of uniformity of 18 F-FDG concentration within the tumor.…”

Section: Discussionmentioning

confidence: 99%

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Optimal standardized uptake value threshold for auto contouring of gross tumor volume using positron emission tomography/computed tomography in patients with operable nonsmall-cell lung cancer: Comparison with pathological tumor size

et al. 2021

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Purpose: Incorporating 18 F-fluorodeoxyglucose positron emission tomography-computed tomography ( 18 F-FDG-PET/CT) for gross tumor volume (GTV) delineation is challenging due to varying tumor edge based on the set threshold of the standardized uptake value (SUV). This study aims to determine an optimal SUV threshold that correlates best with the pathological tumor size. Materials and Methods: From January 2013 to July 2014, 25 consecutive patients of operable nonsmall-cell lung cancer (NSCLC) who underwent staging 18 F-FDG-PET/CT before surgical resection were included in the test cohort and 12 patients in the validation cohort. GTVs were delineated on the staging PET/CT by automatic delineation using various percentage threshold of maximum SUV (SUVmax) and absolute SUV. The maximum pathological tumor diameter was then matched with the maximum auto-delineated tumor diameter with varying SUV thresholds. First-order linear regression and Bland–Altman plots were used to obtain an optimal SUV threshold for each patient. Three radiation oncologists with varying degrees of experiences also delineated GTVs with the visual aid of PET/CT to assess interobserver variation in delineation. Results: In the test set, the mean optimal percentage threshold for GTV was SUVmax of 35.6%±18.6% and absolute SUV of 4.35 ± 1.7. In the validation set, the mean optimal percentage threshold SUV and absolute SUV were 36.9 ± 16.9 and 4.1 ± 1.6, respectively. After a combined analysis of all 37 patients, the mean optimal threshold was 36% ± 17.9% and 4.27 ± 1.7, respectively. Using Bland–Altman plots, auto-contouring with 40% SUVmax and SUV 4 was in greater agreement with the pathological tumor diameter. Conclusion: Automatic GTV delineation on PETCT in NSCLC with percentage threshold SUV of 40% and absolute SUV of 4 correlated best with pathological tumor size. Auto-contouring using these thresholds will increase the precision of radiotherapy contouring of GTV and will save time.

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An improved supervoxel 3D region growing method based on PET/CT multimodal data for segmentation and reconstruction of GGNs

Dong

Yang

Wang

et al. 2019

Multimed Tools Appl

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

Abstract: 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.

Cited by 11 publications

References 35 publications

Revisiting the identification of tumor sub-volumes predictive of residual uptake after (chemo)radiotherapy: influence of segmentation methods on 18F-FDG PET/CT images

Revisiting the identification of tumor sub-volumes predictive of residual uptake after (chemo)radiotherapy: influence of segmentation methods on 18F-FDG PET/CT images

Optimal standardized uptake value threshold for auto contouring of gross tumor volume using positron emission tomography/computed tomography in patients with operable nonsmall-cell lung cancer: Comparison with pathological tumor size

An improved supervoxel 3D region growing method based on PET/CT multimodal data for segmentation and reconstruction of GGNs

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