| 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].Chinese Journal of Radiological Medicine and Protection,2025,45(9):876-883 |
| Multi-omics prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma |
| Received:March 05, 2025 |
| DOI：10.3760/cma.j.cn112271-20250305-00076 |
| KeyWords:Hypopharyngeal squamous cell carcinoma (HPSCC) Radiomics Dosiomics Cox regression Radiotherapy |
| FundProject: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) |
| Author Name | Affiliation | E-mail | | Zhang Nan | State Key Laboratory of Nuclear Physics and Technology, Peking University School of Physics, Beijing 100871, China
Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China | | | Yang Gen | State Key Laboratory of Nuclear Physics and Technology, Peking University School of Physics, Beijing 100871, China | | | Lu Qijian | School of Physics, Beihang University, Beijing 100191, China | | | Liu Hongjia | Department of Radiation Oncology, Peking University Shenzhen Hospital, Shenzhen 518036, China | | | Zhao Dan | Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Lin Chen | State Key Laboratory of Nuclear Physics and Technology, Peking University School of Physics, Beijing 100871, China | | | Li Tian | Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China | | | Zhang Yibao | Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China | zhangyibao@pku.edu.cn |
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| Abstract:: |
| 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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