Brodin N Patrik, Velten Christian, Lubin Jonathan, Eichler Jeremy, Zhu Shaoyu, Saha Sneha, Guha Chandan, Kalnicki Shalom, Tomé Wolfgang A, Garg Madhur K, Kabarriti Rafi
Institute for Onco-Physics, Albert Einstein College of Medicine, Bronx, NY, USA.
Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY, USA.
Phys Imaging Radiat Oncol. 2022 Feb 22;21:72-77. doi: 10.1016/j.phro.2022.02.005. eCollection 2022 Jan.
Disease recurrence and distant metastases (DM) are major concerns for oropharyngeal cancer (OPC) patients receiving definitive chemo-radiotherapy. Here, we investigated whether pre-treatment primary tumor positron emission tomography (PET) features could predict progression-free survival (PFS) or DM.
Primary tumors were delineated on pre-treatment PET scans for patients treated between 2005 and 2018 using gradient-based segmentation. Radiomic image features were extracted, along with SUV metrics. Features with zero variance and strong correlation to tumor volume, stage, p16 status, age or smoking were excluded. A random forest model was used to identify features associated with PFS. Kaplan-Meier methods, Cox regression and logistic regression with receiver operating characteristics (ROC) and 5-fold cross-validated areas-under-the-curve (CV-AUCs) were used.
A total of 114 patients were included. With median follow-up 40 months (range: 3-138 months), 14 patients had local recurrence, 21 had DM and 38 died. Two-year actuarial local control, distant control, PFS and overall survival was 89%, 84%, 70% and 84%, respectively. The wavelet_LHL_GLDZM_LILDE feature slightly improved PFS prediction compared to clinical features alone (CV-AUC 0.73 vs. 0.71). Age > 65 years (HR = 2.64 (95%CI: 1.36-5.2), p = 0.004) and p16-negative disease (HR = 3.38 (95%CI: 1.72-6.66), p < 0.001) were associated with poor PFS. A binary radiomic classifier strongly predicted DM with multivariable HR = 3.27 (95%CI: 1.15-9.31), p = 0.027, specifically for patients with p16-negative disease with 2-year DM-free survival 83% for low-risk vs. 38% for high-risk patients (p = 0.004).
A radiomics signature strongly associated with DM risk could provide a tool for improved risk stratification, potentially adding adjuvant immunotherapy for high-risk patients.
疾病复发和远处转移（DM）是接受根治性放化疗的口咽癌（OPC）患者主要关注的问题。在此，我们研究了治疗前原发肿瘤正电子发射断层扫描（PET）特征是否可预测无进展生存期（PFS）或DM。
对2005年至2018年间接受治疗的患者在治疗前PET扫描上勾勒出原发肿瘤，采用基于梯度的分割方法。提取了影像组学图像特征以及SUV指标。排除方差为零且与肿瘤体积、分期、p16状态、年龄或吸烟有强相关性的特征。使用随机森林模型识别与PFS相关的特征。采用Kaplan-Meier方法、Cox回归以及带有受试者工作特征（ROC）和5折交叉验证曲线下面积（CV-AUC）的逻辑回归。
共纳入114例患者。中位随访40个月（范围：3 - 138个月），14例患者出现局部复发，21例发生DM，38例死亡。两年精算局部控制率、远处控制率、PFS和总生存率分别为89%、84%、70%和84%。与单独临床特征相比，小波_LHL_GLDZM_LILDE特征在一定程度上改善了PFS预测（CV-AUC为0.73对0.71）。年龄>65岁（HR = 2.64（95%CI：1.36 - 5.2），p = 0.004）和p16阴性疾病（HR = 3.38（95%CI：1.72 - 6.66），p < 0.001）与PFS较差相关。一个二元影像组学分类器能有力地预测DM，多变量HR = 3.27（95%CI：1.15 - 9.31），p = 0.027，特别是对于p16阴性疾病患者，低风险患者2年无DM生存率为83%，高风险患者为38%（p = 0.004）。
与DM风险密切相关的影像组学特征可为改善风险分层提供一种工具，可能为高风险患者增加辅助免疫治疗。
