Department of Clinical Radiology, Lincoln Wing, Leeds Teaching Hospitals NHS Trust, St James's University Hospital, Beckett Street, Leeds, LS9 7TF, UK.
Radiotherapy Research Group, Leeds Institute of Medical Research at St James's, Faculty of Medicine & Health, University of Leeds, Leeds, UK.
Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2790-2799. doi: 10.1007/s00259-019-04495-1. Epub 2019 Sep 4.
Incidence of anal squamous cell carcinoma (ASCC) is increasing, with curative chemoradiotherapy (CRT) as the primary treatment of non-metastatic disease. A significant proportion of patients have locoregional treatment failure (LRF), but distant relapse is uncommon. Accurate prognostication of progression-free survival (PFS) would help personalisation of CRT regimens. The study aim was to evaluate novel imaging pre-treatment features, to prognosticate for PFS in ASCC.
Consecutive patients with ASCC treated with curative intent at a large tertiary referral centre who underwent pre-treatment FDG-PET/CT were included. Radiomic feature extraction was performed using LIFEx software on baseline FDG-PET/CT. Outcome data (PFS) was collated from electronic patient records. Elastic net regularisation and feature selection were used for logistic regression model generation on a randomly selected training cohort and applied to a validation cohort using TRIPOD guidelines. ROC-AUC analysis was used to compare performance of a regression model encompassing standard clinical prognostic factors (age, sex, tumour and nodal stage-model A), a radiomic feature model (model B) and a combined radiomic/clinical model (model C).
A total of 189 patients were included in the study, with 145 in the training cohort and 44 in the validation cohort. Median follow-up was 35.1 and 37. 9 months, respectively for each cohort, with 70.3% and 68.2% reaching this time-point with PFS. GLCM entropy (a measure of randomness of distribution of co-occurring pixel grey-levels), NGLDM busyness (a measure of spatial frequency of changes in intensity between nearby voxels of different grey-level), minimum CT value (lowest HU within the lesion) and SMTV (a standardized version of MTV) were selected for inclusion in the prognostic model, alongside tumour and nodal stage. AUCs for performance of model A (clinical), B (radiomic) and C (radiomic/clinical) were 0.6355, 0.7403, 0.7412 in the training cohort and 0.6024, 0.6595, 0.7381 in the validation cohort.
Radiomic features extracted from pre-treatment FDG-PET/CT in patients with ASCC may provide better PFS prognosis than conventional staging parameters. With external validation, this might be useful to help personalise CRT regimens in the future.
肛门鳞状细胞癌（ASCC）的发病率正在上升，对于非转移性疾病，以放化疗（CRT）作为主要治疗方法。相当一部分患者存在局部区域治疗失败（LRF），但远处复发并不常见。准确预测无进展生存期（PFS）有助于 CRT 方案的个体化。本研究旨在评估新的影像学治疗前特征，以预测 ASCC 的 PFS。
连续纳入在一家大型三级转诊中心接受根治性治疗的 ASCC 患者，所有患者均接受治疗前 FDG-PET/CT 检查。使用 LIFEx 软件对基线 FDG-PET/CT 进行放射组学特征提取。通过电子病历收集预后数据（PFS）。使用弹性网络正则化和特征选择在随机选择的训练队列中生成逻辑回归模型，并根据 TRIPOD 指南应用于验证队列。使用 ROC-AUC 分析比较包含标准临床预后因素（年龄、性别、肿瘤和淋巴结分期-模型 A）、放射组学特征模型（模型 B）和放射组学/临床综合模型（模型 C）的回归模型的性能。
共纳入 189 例患者，其中 145 例患者在训练队列中，44 例患者在验证队列中。每个队列的中位随访时间分别为 35.1 个月和 37.9 个月，分别有 70.3%和 68.2%的患者达到 PFS 时间点。GLCM 熵（衡量分布中灰度级共现像素的随机性的度量）、NGLDM 忙碌度（衡量不同灰度级的相邻体素之间强度变化的空间频率的度量）、最小 CT 值（病变内最低 HU）和 SMTV（MTV 的标准化版本）被选为预后模型的纳入因素，以及肿瘤和淋巴结分期。在训练队列中，模型 A（临床）、B（放射组学）和 C（放射组学/临床）的 AUC 分别为 0.6355、0.7403、0.7412，在验证队列中，AUC 分别为 0.6024、0.6595、0.7381。
从接受 ASCC 治疗前 FDG-PET/CT 中提取的放射组学特征可能比传统分期参数提供更好的 PFS 预后。经过外部验证，这可能有助于未来帮助 CRT 方案个体化。
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