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中华放射肿瘤学杂志
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中华放射肿瘤学杂志  2023, Vol. 32 Issue (8): 697-703    DOI: 10.3760/cma.j.cn113030-20230222-00033
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CT影像组学预测局部晚期宫颈癌同步放化疗后生存期
刘会岭1, 崔永斌2, 常诚3, 仇清涛2, 尹勇2, 王若峥1,4
1新疆肿瘤学重点实验室,新疆医科大学附属肿瘤医院放疗中心,乌鲁木齐 830011;
2山东省肿瘤防治研究院(山东省肿瘤医院)放射物理技术科,山东第一医科大学(山东省医学科学院),济南 250117;
3省部共建中亚高发病成因与防治国家重点实验室,新疆医科大学附属肿瘤医院核医学科,乌鲁木齐 830011;
4中国医学科学院肿瘤免疫与放疗研究重点实验室,乌鲁木齐 830011; 刘会岭于山东省肿瘤医院联合培养,现在滨州市人民医院肿瘤科,滨州 256600
CT radiomics model for predicting progression-free survival of locally advanced cervical cancer after concurrent chemoradiotherapy
Liu Huiling1, Cui Yongbin2, Chang Cheng3, Qiu Qingtao2, Yin Yong2, Wang Ruozheng1,4
1Department of Radiation Oncology, the Affiliated Tumor Hospital of Xinjiang Medical University, Xinjiang Key Laboratory of Oncology, Wulumuqi 830011, China;
2Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji'nan 250117, China;
3Department of Nuclear Medicine, the Affiliated Tumor Hospital of Xinjiang Medical University, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asian, Wulumuqi 830011, China;
4Key Laboratory of Cancer Immunotherapy and Radiotherapy, Chinese Academy of Medical Sciences, Wulumuqi 830011, China; Liu Huiling studied at Shandong Cancer Hospital and is working on Department of Oncology, Binzhou People's Hospital, Binzhou 256600, China
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摘要 目的 探讨基于CT图像的机器学习模型在预测局部晚期宫颈癌(LACC)患者同步放化疗(CCRT)后无进展生存(PFS)期中的价值。方法 回顾性分析2015年9月至2021年10月山东省肿瘤医院收治的167例LACC患者,按7∶3比例随机分为训练集和验证集,采用单变量和多变量Cox回归分析筛选临床特征(P<0.1),采用最小绝对收缩和选择算子(LASSO)Cox回归分析筛选影像组学特征构建模型,分别预测1、3、5年PFS。结合所筛选的临床特征和影像组学特征,构建联合模型及列线图,并利用Kaplan-Meier曲线、受试者操作特征(ROC)曲线、一致性指数(C-index)及校准曲线评价。结果 基于CT图像感兴趣区(ROI)提取1 409个影像组学特征。临床模型在训练集和验证集中预测1、3、5年PFS的表现均差于CT影像组学模型。联合模型在预测PFS方面表现最佳,在训练集中预测1、3、5年PFS的曲线下面积(AUC)分别为0.760、0.648、0.661,C-index分别为0.740、0.667、0.709,在验证集中预测1、3、5年PFS的AUC分别为0.763、0.677、0.648,C-index分别为0.748、0.668、0.678。结论 基于CT影像组学和临床特征构建的联合模型的预测性能,优于基于单纯的影像组学模型或临床特征构建的模型,作为一种客观的影像分析方法,在预测LACC患者CCRT后PFS具有较好的预测效能,可为临床决策提供参考。
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刘会岭
崔永斌
常诚
仇清涛
尹勇
王若峥
关键词 宫颈肿瘤同步放化疗计算机断层扫描影像组学生存预测    
AbstractObjective To construct machine learning models based on CT imaging and clinical parameters for predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC) patients after concurrent chemoradiotherapy (CCRT). Methods Clinical data of 167 LACC patients treated with CCRT at Shandong Cancer Hospital from September 2015 to October 2021 were retrospectively analyzed. All patients were randomly divided into the training and validation cohorts according to the ratio of 7 vs. 3. Clinical features were selected by univariate and multivariate Cox proportional hazards model (P<0.1). Radiomics models and nomograms were constructed by radiomics features which were selected by least absolute shrinkage and selection operator (LASSO) Cox regression model to predict the 1-, 3- and 5-year PFS. Combined models and nomogram models were developed by selected clinical and radiomics features. The Kaplan Meier-curve, receiver operating characteristic (ROC) curve, C-index and calibration curve were used to evaluate the model performance. Results A total of 1 409 radiomics features were extracted based on the region of interest (ROI) in CT images. CT radiomics models showed better performance for predicting 1-, 3-and 5-year PFS than the clinical model in the training and validation cohorts. The combined model displayed the optimal performance in predicting 1-, 3-and 5-year PFS in the training cohort [area under the curve (AUC): 0.760, 0.648, 0.661, C-index: 0.740, 0.667, 0.709] and verification cohort (AUC: 0.763, 0.677, 0.648, C-index: 0.748, 0.668, 0.678). Conclusions Combined model constructed based on CT radiomics and clinical features yield better prediction performance than that based on radiomics or clinical features alone. As an objective image analysis approach, it possesses high prediction efficiency for PFS of LACC patients after CCRT, which can provide reference for clinical decision-making.
Key wordsUterine cervical neoplasms    Concurrent chemoradiotherapy    Computed tomography    Radiomics    Survival prediction   
收稿日期: 2023-02-22     
基金资助:中央引导地方科技发展专项资金项目(ZYYD2022B18); 新疆维吾尔自治区重点研发计划项目(2022B03019-X); 省部共建中亚高发病成因与国家重点实验室开放课题项目(SKL-HIDCA-2020-GJ4)
通讯作者: 王若峥,Email:wrz8526@vip.163.com;尹勇,Email:yinyongsd@126.com   
引用本文:   
刘会岭,崔永斌,常诚等. CT影像组学预测局部晚期宫颈癌同步放化疗后生存期[J]. 中华放射肿瘤学杂志, 2023, 32(8): 697-703.
Liu Huiling,Cui Yongbin,Chang Cheng et al. CT radiomics model for predicting progression-free survival of locally advanced cervical cancer after concurrent chemoradiotherapy[J]. Chinese Journal of Radiation Oncology, 2023, 32(8): 697-703.
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