Parkinson Craig, Foley Kieran, Whybra Philip, Hills Robert, Roberts Ashley, Marshall Chris, Staffurth John, Spezi Emiliano
School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff, CF24 3AA, UK.
Division of Cancer and Genetics, School of Medicine, UHW Main Building, Heath Park, Cardiff, CF14 4XN, UK.
EJNMMI Res. 2018 Apr 11;8(1):29. doi: 10.1186/s13550-018-0379-3.
Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated.
Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group.
Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.
食管癌（OC）的预后较差。5年总生存率（OS）约为15%。人们希望个性化医疗能提高5年和10年总生存率。PET的定量分析在预后研究中越来越受到关注，但需要准确界定代谢肿瘤体积。本研究比较了在同一患者队列中使用个体PET分割算法开发的预后模型，并评估其对患者风险分层的影响。最终分析纳入了427例经活检证实为OC的连续患者。所有患者在2010年9月至2016年7月期间接受PET/CT分期。研究了9种自动PET分割方法。对所有肿瘤轮廓进行主观准确性分析，排除准确率<90%的分割方法。计算标准化图像特征，并使用相同的临床数据开发一系列预后模型。计算风险分类组发生变化的患者比例。
在所研究的9种PET分割方法中，纳入聚类均值（KM2）、通用聚类均值（GCM3）、自适应阈值法（AT）和分水岭阈值法（WT）进行分析。已知的临床预后因素（年龄、治疗和分期）在所有开发的预后模型中均具有显著性。AT和KM2分割方法开发出相同的预后模型。患者风险分层取决于用于开发预后模型的分割方法，多达73例患者（17.1%）的风险分层组发生变化。
纳入定量图像特征的预后模型取决于用于勾勒原发肿瘤的方法。这对风险分层有后续影响，患者会因所使用的图像分割方法而改变分组。
