为了开发一种基于CT增强放射学的放射组学模型，以识别出哪些局部晚期鼻咽癌（LA-NPC）患者会受益于减弱化疗放疗。接受低剂量顺行性顺铂化疗（累计：150 mg/m2）的LA-NPC患者被随机分为训练组和验证组。从每个预治疗CECT扫描中提取了基于原发性鼻咽肿瘤的107个放射组学特征。通过Cox回归分析，使用具有预测独立放射组学特征的放射组学模型和患者的相应放射组学评分。将T分期（T）和放射组学评分（R）作为预测因素进行比较。构建了合并N分期（N）的临床模型（T + N）和替代模型（R + N）。训练组和验证组分别由66名和33名患者组成。发现了三个显著且独立的放射组学特征（平坦度、均值和灰度级别非均匀性）。放射组学评分显示比T分期具有更好的预测能力（一致性指数（C-index）：0.67 vs. 0.61，曲线下面积（AUC）：0.75 vs. 0.60）。R + N模型比T + N模型具有更好的预测性能和更有效的风险分层（C-index：0.77 vs. 0.68，AUC：0.80 vs. 0.70）。R + N模型确定了一个低风险组作为减弱化疗候选者，在3年内未发生任何患者的进展，5年无进展生存率（PFS）和总生存率（OS）均为90.7%（风险比（HR）= 4.132，p = 0.018）。我们的基于放射组学的模型结合了放射组学评分和N分期，可以识别出特定的LA-NPC候选人，使得减弱化疗可以在不影响治疗效果的情况下进行。我们的研究表明，基于放射组学的模型（R + N）可以准确地将患者分层为不同的风险组，低风险组在接受低剂量顺行化疗时有令人满意的预后，为个体化减弱策略提供了新选项。•一种包括3个预测放射组学特征（平坦度、均值和GLDM-GLN）与N分期相结合的放射组学评分可以识别出特定的LA-NPC人群进行减弱治疗。•在选择LA-NPC减弱治疗候选者时，基于CECT提取的放射组学评分从原发性鼻咽肿瘤中可能优于传统的T分期分类作为预测因素。© 2023年. 作者（们）独家授权于欧洲放射学会。

To develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy.LA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m2), were randomly divided into training and validation groups. 107 radiomics features based on the primary nasopharyngeal tumor were extracted from each pre-treatment CECT scan. Through Cox regression analysis, a radiomics model and patients' corresponding radiomics scores were created with predictive independent radiomics features. T stage (T) and radiomics score (R) were compared as predictive factors. Combining the N stage (N), a clinical model (T + N), and a substitution model (R + N) were constructed.Training and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018).Our radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy.Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies.• A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.© 2023. The Author(s), under exclusive licence to European Society of Radiology.