| 张楠,杨根,陆启坚,等.局部晚期下咽鳞状细胞癌患者放疗后局部区域复发风险的多模态预测模型[J].中华放射医学与防护杂志,2025,45(9):876-883.Zhang Nan,Yang Gen,Lu Qijian,et al.Multi-omics prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma[J].Chin J Radiol Med Prot,2025,45(9):876-883 |
| 局部晚期下咽鳞状细胞癌患者放疗后局部区域复发风险的多模态预测模型 |
| Multi-omics prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma |
| 投稿时间：2025-03-05 |
| DOI：10.3760/cma.j.cn112271-20250305-00076 |
| 中文关键词: 下咽鳞状细胞癌 影像组学 剂量组学 Cox回归 放射治疗 |
| 英文关键词:Hypopharyngeal squamous cell carcinoma (HPSCC) Radiomics Dosiomics Cox regression Radiotherapy |
| 基金项目:国家自然科学基金(12275012,12475309,12411530076,12581360004,12375334,82202941);北京市自然科学基金(Z210008);国家重点研发计划(2019YFF01014402);北京大学专业学位研究生案例教学示范课程及案例库建设(6201600660);北京大学肿瘤医院临床研究青年基金(QNJJ2023018);中国国际人才交流基金会国际青年人才来华交流项目(JC202502001F);中央高校基本科研业务费/北京大学临床医学+X 青年专项(PKU2025PKULCXQ014);教育部内地与港澳高等学校师生交流计划项目(万人计划7111400072);内蒙古自治区科技计划(2022YFSH0064) |
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| 中文摘要: |
| 目的 探讨基于影像组学、剂量组学和临床因素构建的综合模型对局部晚期下咽鳞状细胞癌(HPSCC)患者放疗后的局部区域复发风险的预测价值,旨在为该发病率低、预后较差的少见癌种提供补充性的临床证据和个体化决策依据。方法 回顾性纳入2011年10月至2020年7月在北京大学肿瘤医院就诊的76 例HPSCC患者的临床影像及病理资料。以计划肿瘤靶区体积(PGTV)为感兴趣区,从计划 CT 和剂量分布图中分别提取1 316个影像组学和剂量组学特征。经稳定性测试,最小绝对收缩和选择算子(LASSO)及主成分分析(PCA)降低特征维度,分别获得影像组学主成份(RPCs)和剂量组学主成份(DPCs)。使用RPCs、DPCs和临床变量的各种组合作为预测因子,采用100次5折交叉验证构建多元Cox回归模型。通过赤池信息量准则(AIC)和一致性指数(C-index)评估模型性能。结果 分别利用筛选获得的两个RPCs与3个DPCs,构建剂量组学Cox风险比例模型与影像组学Cox风险比例模型,C-index分别为0.781与0.778,AIC分别为94.44与92.27。结果提示一个RPC与3个DPCs在Cox回归模型中显著(P < 0.05)。进一步纳入患者临床特征数据分别构建影像组学+临床、剂量组学+临床与影像组学+剂量组学 + 临床预测模型,发现与使用单个因素或两个组合成份的模型相比,多组学模型的预测效果最好(C-index:0.823,AIC:84.94)。结论 结合影像组学、剂量组学和临床变量的综合模型能够有效提升局部区域复发风险预测的准确性,有望为HPSCC的个体化治疗提供决策支持,改进患者的临床预后。 |
| 英文摘要: |
| Objective To explore the value of an integrated modeling approach combining radiomics, dosiomics, and clinical factors in the prediction of the locoregional recurrence (LRR) risk after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma (HPSCC), in order to provide supplementary clinical evidence and decision-making basis for personalized treatment for this rare disease characterized by low incidence and poor prognosis. Methods The clinical images and pathological data were retrospectively enrolled from 76 HPSCC patients treated at the Peking University Cancer Hospital from October 2011 to July 2020. The planning gross tumor volumes (PGTVs) were taken as the volumes of interest (VOIs). A total of 1 316 radiomic and dosiomic features were extracted from the planning CT and dose distribution images. After stability testing, feature dimensionality reduction was achieved using least absolute shrinkage and selection operator (LASSO) regression and principal component analysis (PCA), with radiomic principal components (RPCs) and dosiomic principal components (DPCs) obtained, respectively. Using various combinations of RPCs, DPCs, and clinical variables as predictors, multivariate Cox regression models were developed after 5-fold cross-validation 100 times. The model performance was evaluated based on the Akaike information criterion (AIC) and concordance index (C-index). Results Using two RPCs and three DPCs selected, dosiomics and radiomic Cox proportional hazards models were constructed, with C-index values of 0.781 and 0.778 and AIC values of 94.44 and 92.27, respectively. The result indicated that one RPC and three DPCs showed significant associations in Cox regression (P < 0.05). Other prediction models were established by integrating the clinical data of patients with radiomic features, dosiomic features, or both. The prediction result demonstrated that compared to models based on individual factors or dual components, the multi-omics model yielded the highest prediction accuracy (C-index: 0.823, AIC: 84.94). Conclusions Integrated models that combine radiomic features, dosiomic features, and clinical factors demonstrate great potential for enhancing the accuracy of LRR risk prediction. These models are expected to provide decision-making support for devising personalized treatment strategies and ultimately improve the prognosis of HPSCC patients. |
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