| 朱礼阳,任正婷,潘淑豪,等.基于宫颈癌影像组学和临床特征的列线图模型在慢性放射性肠炎中的应用[J].中华放射医学与防护杂志,2025,45(8):803-809.Zhu Liyang,Ren Zhengting,Pan Shuhao,et al.Application of a nomogram model based on cervical cancer radiomics and clinical features in the treatment of chronic radiation enteritis[J].Chin J Radiol Med Prot,2025,45(8):803-809 |
| 基于宫颈癌影像组学和临床特征的列线图模型在慢性放射性肠炎中的应用 |
| Application of a nomogram model based on cervical cancer radiomics and clinical features in the treatment of chronic radiation enteritis |
| 投稿时间：2024-09-13 |
| DOI：10.3760/cma.j.cn112271-20240913-000349 |
| 中文关键词: 宫颈癌 调强放射治疗 放射性肠炎 影像组学 影像组学得分 |
| 英文关键词:Cervical cancer Intensity-modulated radiotherapy Radiation enteritis Radiomics Radscore |
| 基金项目:安徽高校协同创新项目(GXXT-2022-011);安徽省高校自然科学基金(KJ2021A0300) |
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| 中文摘要: |
| 目的 利用磁共振成像(MRI)的放射组学特征,结合临床参数建立模型,预测宫颈癌慢性放射性肠炎的发生,为临床医生判断该类患者预后和个体化诊疗提供参考。方法 回顾性分析安徽医科大学第一附属医院111例接受宫颈癌根治性放疗患者,从放疗前宫颈癌局部病灶MRI的T1增强相中提取放射学特征,并使用最小绝对收缩和选择算子进行特征选择,获得影像组学得分。采用单因素和多因素逻辑回归分析对影像组学得分及临床参数进行评估并建立列线图。应用曲线下面积、校准曲线及决策曲线评估影像组学预测慢性放射性肠炎的能力。结果 多因素logistic回归分析结果显示,影像组学得分(HR: 17.457,95% CI: 5.540~55.009,P<0.001)、肿瘤体积(HR: 3.617,95% CI: 1.293~10.115,P=0.014)、盆腔转移淋巴结(HR: 3.559,95% CI: 1.013~12.501,P=0.048)是鉴别患者慢性放射性肠炎的独立危险因素。影像组学联合临床数据的模型在训练组和验证组的曲线下面积(AUC)(0.888,0.870)高于单纯影像组学(0.842,0.804)及单纯临床数据模型(0.721,0.704)。校准曲线及决策曲线分析证实了临床影像组学列线图的应用价值。结论 影像组学联合临床模型在预测CRE展现出了良好的性能。影像组学特征能够作为CRE颇具前景的影像学生物标志物。 |
| 英文摘要: |
| Objective To predict the occurrence of chronic radiation enteritis (CRE) in cervical cancer patients by developing a prediction model based on the combination of radiomic features derived from magnetic resonance imaging (MRI) scans and clinical parameters, in order to provide a reference for clinicians to determine the prognosis of these patients and offer them individualized diagnosis and treatment. Methods A retrospective analysis was conducted on 111 cervical cancer patients who received radical radiotherapy at the First Affiliated Hospital of Anhui Medical University. Radiological features were extracted from the T1-weighted MRI images of local lesions of cervical cancer obtained before the radiotherapy. Features were selected using the least absolute shrinkage and selection operator (LASSO) to obtain the radiomics score. The radiomics scores and clinical parameters were assessed using univariate and multivariate logistic regression analyses, followed by the establishment of nomograms. The ability of radiomics to achieve CRE prediction was assessed using the area under the curve (AUC) and the calibration and decision curves. Results Multivariate logistic regression analysis result revealed that the independent risk factors for identifying CRE in patients included radiomics score (HR: 17.457, 95% CI: 5.540-55.009, P<0.001), tumor volume (HR: 3.617, 95% CI: 1.293-10.115, P=0.014), and pelvic lymph node metastasis (HR: 3.559, 95% CI: 1.013-12.501, P=0.048). The model combining radiomics and clinical data demonstrated high performance, with its AUCs of the training and validation groups (0.888 and 0.870, respectively) higher than those of the radiomics model (0.842 and 0.804, respectively) and the clinical data model (0.721 and 0.704, respectively). The analyses of calibration and decision curves confirmed the application value of clinical radiomic nomograms. Conclusions The model combining radiomics and clinical data allows for accurate CRE prediction. Therefore, radiomic features have the potential to serve as a promising imaging biomarker for CRE. |
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