Background/Objectives: Adenoid Cystic Carcinoma (AdCC) is a rare malignant salivary gland tumor, with high rates of recurrence and distant metastasis. This study aims to stratify patients Relapse-Free Survival (RFS) using a combined model of clinical and radiomic features from preoperative MRI. Methods: This retrospective study included patients with primary AdCC who underwent surgery and adjuvant radiotherapy. Segmentations were manually performed by two head and neck radiologists. Radiomic features were extracted using the 3D Slicer software. Descriptive statistics was performed. A Survival Random Forest model was employed to select which radiological feature predict RFS. Cox proportional hazards models were constructed using clinical, radiological variables or both. Synthetic data augmentation was applied to address the small sample size and improve model robustness. Models were validated on real data and compared using the C-index and Prediction Error Curves (PEC). Results: Three Cox models were developed: one with clinical features (C-index = 0.67), one with radiomic features (C-index = 0.68), and one combining both (C-index = 0.77). The combined clinical-radiomic model had the highest predictive accuracy and outperformed models based on clinical or radiomic features. The combined model also exhibited the lowest mean Brier score in PEC analysis, indicating better predictive performance. Conclusions: This study demonstrate that a combined radiomic-clinical model can predict RFS in AdCC patients. This model may provide clinicians a valuable tool in patient’s management and may aid in personalized treatment planning.
背景/目的：腺样囊性癌是一种罕见的恶性唾液腺肿瘤，具有较高的复发率和远处转移率。本研究旨在通过结合术前MRI的临床特征与影像组学特征构建联合模型，对患者的无复发生存期进行分层预测。方法：这项回顾性研究纳入了接受手术及辅助放疗的原发性腺样囊性癌患者。由两位头颈部放射科医师手动完成病灶分割。使用3D Slicer软件提取影像组学特征，并进行描述性统计分析。采用生存随机森林模型筛选预测无复发生存期的影像学特征。分别基于临床变量、影像组学变量及两者联合构建Cox比例风险模型。针对样本量较小的问题，应用合成数据增强技术以提高模型稳健性。所有模型均在真实数据上进行验证，并通过一致性指数和预测误差曲线进行比较评估。结果：共构建三种Cox模型：临床特征模型（C指数=0.67）、影像组学特征模型（C指数=0.68）以及临床-影像组学联合模型（C指数=0.77）。联合模型具有最高的预测准确性，其性能优于单一特征模型。在预测误差曲线分析中，联合模型的平均Brier分数最低，表明其预测性能更优。结论：本研究证实临床-影像组学联合模型能够有效预测腺样囊性癌患者的无复发生存期。该模型可为临床医生提供有价值的患者管理工具，并有助于制定个体化治疗方案。
肿瘤(癌症)患者之家