Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Department of Clinical Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Clin Radiol. 2021 Jan;76(1):78.e9-78.e17. doi: 10.1016/j.crad.2020.08.030. Epub 2020 Oct 6.
To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally advanced larynx and hypopharynx squamous cell carcinoma (SCC) receiving (chemo)radiotherapy.
Patients with larynx and hypopharynx SCC treated with definitive (chemo)radiotherapy at a specialist cancer centre undergoing pre-treatment PET-CT between 2008 and 2017 were included. Tumour segmentation and radiomic analysis was performed using LIFEx software (University of Paris-Saclay, France). Data were assigned into training (80%) and validation (20%) cohorts adhering to TRIPOD guidelines. A random forest classifier was created for four predictive models using features determined by recursive feature elimination: (A) PET, (B) CT, (C) clinical, and (D) combined PET-CT parameters. Model performance was assessed using area under the curve (AUC) receiver operating characteristic (ROC) analysis.
Seventy-two patients (40 hypopharynx 32 larynx tumours) were included, mean age 61 (range 41-77) years, 50 (69%) were men. Forty-five (62.5%) had chemoradiotherapy, 27 (37.5%) had radiotherapy alone. Median follow-up 26 months (range 12-105 months). Twenty-seven (37.5%) patients progressed within 12 months. ROC AUC for models A, B, C, and D were 0.91, 0.94, 0.88, and 0.93 in training and 0.82, 0.72, 0.70, and 0.94 in validation cohorts. Parameters in model D were metabolic tumour volume (MTV), maximum CT value, minimum standardized uptake value (SUVmin), grey-level zone length matrix (GLZLM) small-zone low grey-level emphasis (SZLGE) and histogram kurtosis.
FDG PET-CT derived radiomic features are potential predictors of early disease progression in patients with locally advanced larynx and hypopharynx SCC.
确定基于机器学习的基线整合 2-[F]-氟-2-脱氧-d-葡萄糖（FDG）正电子发射断层扫描（PET）计算机断层扫描（CT）放射组学特征分析是否可预测接受放化疗的局部晚期喉和下咽鳞状细胞癌（SCC）患者的疾病进展。
纳入 2008 年至 2017 年间在一家癌症专科医院接受根治性（放化疗）治疗的接受治疗的局部晚期喉和下咽 SCC 患者。使用 LIFEx 软件（法国巴黎萨克雷大学）对肿瘤进行分割和放射组学分析。数据根据 TRIPOD 指南分配到训练（80%）和验证（20%）队列中。使用递归特征消除确定的特征为四个预测模型创建随机森林分类器：（A）PET，（B）CT，（C）临床和（D）PET-CT 联合参数。使用曲线下面积（AUC）接收者操作特征（ROC）分析评估模型性能。
纳入 72 例患者（40 例下咽癌，32 例喉癌），平均年龄 61 岁（范围 41-77 岁），50 例（69%）为男性。45 例（62.5%）接受放化疗，27 例（37.5%）仅接受放疗。中位随访时间为 26 个月（范围 12-105 个月）。27 例（37.5%）患者在 12 个月内进展。模型 A、B、C 和 D 在训练和验证队列中的 AUC 分别为 0.91、0.94、0.88 和 0.93。模型 D 中的参数包括代谢肿瘤体积（MTV）、最大 CT 值、最小标准化摄取值（SUVmin）、灰度区长度矩阵（GLZLM）小区域低灰度强调（SZLGE）和直方图峰度。
FDG PET-CT 衍生的放射组学特征可能是局部晚期喉和下咽 SCC 患者早期疾病进展的潜在预测指标。
