| Literature DB >> 24479954 |
Zachary D Horne, David A Clump, John A Vargo, Samir Shah, Sushil Beriwal, Steven A Burton, Annette E Quinn, Matthew J Schuchert, Rodney J Landreneau, Neil A Christie, James D Luketich, Dwight E Heron1.
Abstract
BACKGROUND: This retrospective study aims to assess the usefulness of SUV(max) from FDG-PET imaging as a prognosticator for primary biopsy-proven stage I NSCLC treated with SBRT.Entities:
Mesh:
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Year: 2014 PMID: 24479954 PMCID: PMC3922961 DOI: 10.1186/1748-717X-9-41
Source DB: PubMed Journal: Radiat Oncol ISSN: 1748-717X Impact factor: 3.481
Patient characteristics
| Age | 77 (48-91) years |
| Sex | |
| Male | 49 (51.6%) |
| Female | 46 (48.4%) |
| Operable | 14 (14.7%) |
| Inoperable | 81 (85.3%) |
| KPS | |
| 80-100 | 63 (66.3%) |
| <70 | 32 (32.7%) |
| Clinical follow-up | 16.33 (1.13-64.2) months |
Imaging and tumor characteristics with percentage distribution per SUV category
| | ||||
|---|---|---|---|---|
| All patients | 95 | 40 | 55 | ns |
| Histology | | | | |
| Squamous | 38 (40%) | 8 (20%) | 30 (54.5%) | |
| Adenocarcinoma | 33 (34.7%) | 21 (52.5%) | 12 (21.8%) | |
| NSCLC NOS | 24 (25.3%) | 11 (27.5%) | 13 (23.7%) | |
| Tumor Size [median (range)] | 2.15 (0.8-5.0) cm | 1.95 (0.9-5.0) cm | 2.4 (0.8-4.8) cm | |
| T Stage | | | | |
| 1a | 46 (48.4%) | 27 (67.5%) | 19 (34.5%) | |
| 1b | 30 (31.6%) | 7 (17.5%) | 23 (41.8%) | |
| 2a | 19 (20%) | 6 (15%) | 13 (23.7%) | |
Tumor demographics and SUVmax distributions showing tendency for squamous cell histology and larger size to be of increasing SUVmax.
Overall outcomes from treatment with 2-year event rates showing differences between SUV categories
| | | | | ||
|---|---|---|---|---|---|
| Local failure | 93.7 | 8 (8.4) | 97 | 86 | .256 |
| Regional failure | 90.5 | 10 (10.5) | 94 | 82 | .131 |
| Distant failure | 86.3 | 15 (15.8) | 91 | 78 | .371 |
| Any progression | 93.7 | 25 (26.3) | 88 | 62 | |
| Death | 64.2 | 48 (50.5) | 72 | 49 |
Comparison of SUVmax categories shows a statistically significant difference in progression-free and overall survivals.
Figure 1Overall and progression-free survivals as differentiated by SUV. A: Overall survival differences between SUVmax categories, p= 0.024; B: Progression-free survival differences between SUVmax categories, p= 0.024.
Figure 2Local, regional, and distant control rates as differentiated by SUV. A: Local control differences between SUVmax categories, p= 0.256; B: Regional control differences between SUVmax categories, p= 0.131; C: Distant control differences between SUVmax categories, p= 0.371.
Univariate and multivariate Cox proportional hazards regression analysis with SUV as a dichotomous and continuous variable
| | ||||
|---|---|---|---|---|
| Local control | -- | NS | 1.124 (1.002 – 1.260) | |
| Distant control | -- | NS | -- | .059 |
| Progression-free survival | 0.359 (0.143 – 0.905) | 1.098 (1.033 – 1.168) | ||
| Overall survival | 0.478 (0.249 – 0.920) | 1.061 (1.005 – 1.119) | ||
| Multivariate Cox proportional hazards regression analysis | ||||
| Local control | -- | NS | -- | .057 |
| Distant control | -- | NS | -- | .092 |
| Progression-free survival | -- | .105 | 1.111 (1.027 – 1.201) | |
Univariate Cox hazards regression analysis of SUVmax as a dichotomous variable shows significance for progression-free and overall survivals. As a continuous variable, SUVmax significantly predicts for local control, progression-free and overall survivals. In multivariate Cox analysis, SUVmax remains a significant predictor of progression-free survival.