Encaoua J, Abgral R, Leleu C, El Kabbaj O, Caradec P, Bourhis D, Pradier O, Schick U
Service de radiothérapie, institut de cancérologie et d'hématologie, CHRU Morvan, 2, avenue Foch, 29200 Brest, France.
Service de médecine nucléaire, CHRU Morvan, 2, avenue Foch, 29200 Brest, France.
Cancer Radiother. 2017 Jun;21(4):267-275. doi: 10.1016/j.canrad.2016.12.004. Epub 2017 May 9.
To study the impact on radiotherapy planning of an automatically segmented target volume delineation based on (F)-fluorodeoxy-D-glucose (FDG)-hybrid positron emission tomography-computed tomography (PET-CT) compared to a manually delineation based on computed tomography (CT) in oesophageal carcinoma patients.
Fifty-eight patients diagnosed with oesophageal cancer between September 2009 and November 2014 were included. The majority had squamous cell carcinoma (84.5 %), and advanced stage (37.9 % were stade IIIA) and 44.8 % had middle oesophageal lesion. Gross tumour volumes were retrospectively defined based either manually on CT or automatically on coregistered PET/CT images using three different threshold methods: standard-uptake value (SUV) of 2.5, 40 % of maximum intensity and signal-to-background ratio. Target volumes were compared in length, volume and using the index of conformality. Radiotherapy plans to the dose of 50Gy and 66Gy using intensity-modulated radiotherapy were generated and compared for both data sets. Planification target volume coverage and doses delivered to organs at risk (heart, lung and spinal cord) were compared.
The gross tumour volume based manually on CT was significantly longer than that automatically based on signal-to-background ratio (6.4cm versus 5.3cm; P<0.008). Doses to the lungs (V20, D), heart (V40), and spinal cord (D) were significantly lower on plans using the PTV. The PTV coverage was statistically better than the PTV coverage on both plans. (50Gy: P<0.0004 and 66Gy: P<0.0006).
The automatic PET segmentation algorithm based on the signal-to-background ratio method for the delineation of oesophageal tumours is interesting, and results in better target volume coverage and decreased dose to organs at risk. This may allow dose escalation up to 66Gy to the gross tumour volume.
研究在食管癌患者中,基于(F)-氟脱氧-D-葡萄糖(FDG)-混合正电子发射断层扫描-计算机断层扫描(PET-CT)自动分割靶区体积与基于计算机断层扫描(CT)手动分割相比,对放射治疗计划的影响。
纳入2009年9月至2014年11月期间诊断为食管癌的58例患者。大多数为鳞状细胞癌(84.5%),处于晚期(37.9%为ⅢA期),44.8%有食管中段病变。大体肿瘤体积通过以下两种方式回顾性定义:一是基于CT手动定义,二是基于配准后的PET/CT图像使用三种不同阈值方法自动定义:标准摄取值(SUV)为2.5、最大强度的40%以及信号与背景比值。比较靶区体积的长度、体积以及适形指数。针对两组数据集生成并比较采用调强放疗、剂量分别为50Gy和66Gy的放射治疗计划。比较计划靶区体积覆盖率以及危及器官(心脏、肺和脊髓)所接受的剂量。
基于CT手动定义的大体肿瘤体积显著长于基于信号与背景比值自动定义的体积(6.4厘米对5.3厘米;P<0.008)。使用计划靶区(PTV)的计划中,肺(V20、D)、心脏(V40)和脊髓(D)所接受的剂量显著更低。PTV覆盖率在统计学上优于两个计划中的计划靶区覆盖率。(50Gy:P<0.0004;66Gy:P<0.0006)。
基于信号与背景比值法的食管肿瘤自动PET分割算法很有意义,可实现更好的靶区体积覆盖,并降低危及器官的剂量。这可能允许将大体肿瘤体积的剂量提升至66Gy。