Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 200032 Shanghai, China.
Department of Radiology, Fudan University Shanghai Cancer Center, 200032 Shanghai, China.
Oral Oncol. 2024 Dec;159:107049. doi: 10.1016/j.oraloncology.2024.107049. Epub 2024 Sep 27.
Accurate prediction of neoadjuvant chemotherapy (NAC) response allows for NAC-guided personalized treatment de-intensification in HPV-positive oropharyngeal squamous cell carcinoma (OPSCC). In this study, we aimed to apply baseline MR radiomic features to predict NAC response to help select NAC-guided de-intensification candidates, and to explore biological underpinnings of response-oriented radiomics.
Pre-treatment MR images and clinical data of 131 patients with HPV-positive OPSCC were retrieved from Fudan University Shanghai Cancer Center. Patients were divided into training cohort (n = 47), validation cohort 1 (n = 49) from NAC response-adapted de-intensification trial (IChoice-01, NCT04012502) and real-world validation cohort 2 (n = 35). NAC prediction model using linear support vector machine (SVM) was built and validated. Subsequent nomograms combined radiomics and clinical characteristics were established to predict survival outcomes. RNA-seq and proteomic data were compared to interpret the molecular features underlying radiomic signatures with differential NAC response.
For NAC response prediction, the fusion model with both oropharyngeal and nodal signatures achieved encouraging performance to predict good responders in the training cohort (AUC 0·89, 95% CI, 0·79-0·95) and validation cohort 1 (AUC 0·71, 95% CI, 0·59-0·83). For prognosis prediction, radiomics-based nomograms exhibited satisfactory discriminative ability between low-risk and high-risk patients (PFS, C-index 0·85, 0·76 and 0·83; OS, C-index 0·79, 0·76 and 0·87, respectively) in three cohorts. Expression analysis unveiled NAC poor responders had predominantly enhanced keratinization while good responders were featured by upregulated immune response and oxidative stress.
The MR-based radiomic models and prognostic models efficiently discriminate among patients with different NAC response and survival risk, which help candidate selection in HPV-positive OPSCC with regard to personalized treatment de-intensification.
准确预测新辅助化疗(NAC)的反应,使得 HPV 阳性口咽鳞状细胞癌(OPSCC)患者能够接受 NAC 指导的个体化治疗强度降低。本研究旨在应用基线磁共振(MR)影像组学特征来预测 NAC 反应,以帮助选择 NAC 指导的强度降低候选者,并探索基于反应的影像组学的生物学基础。
从复旦大学附属肿瘤医院回顾性收集了 131 例 HPV 阳性 OPSCC 患者的治疗前 MR 图像和临床数据。患者被分为训练队列(n=47)、来自 NAC 反应适应性降强度试验(IChoice-01,NCT04012502)的验证队列 1(n=49)和真实世界验证队列 2(n=35)。采用线性支持向量机(SVM)构建并验证 NAC 预测模型。随后建立了结合影像组学和临床特征的列线图,以预测生存结局。比较 RNA-seq 和蛋白质组学数据,以解释具有不同 NAC 反应的影像组学特征的分子特征。
对于 NAC 反应预测,融合口咽和淋巴结特征的融合模型在训练队列(AUC 0.89,95%CI,0.79-0.95)和验证队列 1(AUC 0.71,95%CI,0.59-0.83)中均能很好地预测完全缓解者。对于预后预测,基于影像组学的列线图在三个队列中均表现出良好的低危和高危患者区分能力(无进展生存期,C 指数 0.85、0.76 和 0.83;总生存期,C 指数 0.79、0.76 和 0.87)。表达分析显示,NAC 反应不良者以角质化增强为主,而 NAC 反应良好者则表现出免疫反应和氧化应激增强。
基于 MR 的影像组学模型和预后模型可以有效地区分 NAC 反应和生存风险不同的患者,有助于 HPV 阳性 OPSCC 患者进行个体化治疗强度降低的候选者选择。