摘要
目的 探究基于临床和影像组学特征的融合模型在宫颈癌淋巴血管间隙侵犯(LVSI)疾病中的预测诊断价值。方法 回顾性分析2017年11月至2022年6月于喀什地区第一人民医院就诊的192例宫颈癌患者资料,所有患者均行MRI平扫及动态增强磁共振(DCE-MRI)检查。收集患者一般资料、手术前的实验室数据、MRI定量参数进行单因素分析,将有统计学意义的指标纳入Logistic多因素回归分析,建立临床模型;获取动脉晚期(LAP)图像并标记肿瘤区域,提取影像组学特性[球形度、标准化的灰度级非均匀性、群集阴影、一阶特征峰度、一阶特征均值、高灰度级优势]进行二分类Logistic回归分析,构建影像组学模型;采用Logistic回归建立基于临床模型和影像模型的融合模型,分别绘制受试者工作特征(ROC)曲线,分析评估3个模型的预测效能。结果 192例宫颈癌患者中LVSI(+)85例,LVSI(-)111例。临床模型预测宫颈癌LVSI状态的ROC曲线下面积(AUC)为训练组0.736、验证组0.768。影像组学模型预测宫颈癌中LVSI状态的AUC为训练组0.709、验证组0.682。融合模型预测宫颈癌中LVSI状态的AUC为训练组0.828、验证组0.795。结论 利用基于临床和影像组学特征的融合模型可以准确预测宫颈癌患者LVSI的状态,比其他2种模型预测价值高,可为后续采取无创伤手术治疗策略提供依据。
Objective To explore the predictive diagnostic value of a fusion model based on clinical and radiomics features in cervical cancer with lymph vascular space invasion(LVSI) disease.Methods With the retrospective analysis of 192 cervical cancer patients visited the First People's Hospital in Kashgar from November 2017 to June 2022,all patients underwent MRI plain scan and dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) examination.With the collection of general patient information,preoperative laboratory data,and MRI quantitative parameters for univariate analysis,statistically significant indicators were included in Logistic multiple regression analysis to establish a clinical model;With the acquisition of late arterial phase(LAP) images and the labeling of late arterial phase(LAP) images,the radiomics characteristics [sphericity,standardized grayscale non-uniformity,clustered shadows,first-order feature kurtosis,first-order feature mean,high grayscale advantages] were extracted for binary logistic regression analysis to construct an radiomics model;Using Logistic regression to establish a fusion model based on clinical and imaging models,the receiver operating characteristic(ROC) curves were plotted separately,and the predictive performance of the three models was analyzed and evaluated.Results Among the 192 cervical cancer patients,there were 85 cases with LVSI(+) and 111 cases with LVSI(-).The area under the ROC curve(AUC) of LVSI status of cervical cancer predicted by clinical model was 0.736 in the training group and 0.768 in the validation group.The AUC of LVSI status in cervical cancer predicted by radiomics model was 0.709 in the training group and 0.682 in the verification group.The AUC of LVSI status in cervical cancer predicted by fusion model was 0.828 in training group and 0.795 in verification group.Conclusion The fusion model based on clinical and radiomics features can accurately predict the LVSI status of cervical cancer patients,with a higher preditive value than the other 2 models,
作者
迪丽阿热姆·艾海提
艾斯卡尔江·霍加
周仁冰
左尔比亚·买买提
米日古丽·达毛拉
马依迪丽·尼加提
张春霞
Diliaremu Aihaiti;Aisikaerjiang Huojia;Zhou Renbing;Miriguli Damaola;Zuoribiya Maimaiti;Mayidili Nijiati;Zhang Chunxia(The First People's Hospital of Kashgar,Kashgar Xinjiang 844000,China)
出处
《医疗装备》
2024年第11期1-5,9,共6页
Medical Equipment
基金
省部共建中亚高发病成因与防治国家重点实验室开放课题项目(SKL-HIDCA-2020-KS9)。
