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构建高分辨率CT影像组学模型预测肺部孤立性磨玻璃结节的良恶性

Constructing an imagomics model for high resolution CT to predict the benign and malignant characteristics of solitary ground glass nodules in the lung

    摘要
  • 摘要:
    目的 探讨构建体检高分辨率CT（high-resolution computed tomography, HRCT）的影像组学模型术前预测肺部孤立性磨玻璃结节（ground glass nodule, GGN）良恶性的价值。
    方法 回顾性分析2019年1月至2022年10月上海市奉贤区中心医院诊断为肺孤立性GGN的152例患者的肺部体检HRCT图像，按照7∶3的比例随机分为训练组（n＝106）和验证组（n＝46）。根据病理结果将训练组患者分为恶性组（n＝56）和良性组（n＝50），比较两组患者临床特征。评估训练组患者肺孤立性GGN的常规CT影像特征，采用PyRadiomics软件在每个病灶的全域感兴趣区（volume of interest, VOI）提取107个影像特征，采用Lasso回归筛选影像特征并建立影像组学标签。采用logistic回归建立3种预测模型（临床模型、影像组学模型和联合模型），采用ROC曲线和曲线下面积（area under the curve, AUC）评价3种模型的预测效能。
    结果 Lasso回归共筛选出13个与预测肺孤立性GGN良恶性最相关的影像组学特征；3个临床特征在恶性组和良性组的差异有统计学意义，分别为病灶密度（P＝0.018）、分叶征（P＝0.036）和支气管征（P＝0.033）。Logistic回归分析显示，影像组学特征的NCCT_original_firstorder_10Percentile、NCCT_original_glrlm_RunEntropy、NCCT_original_shape_Sphericity、临床特征的CT值、临床特征得分和影像组学得分可作为肺孤立性GGN良恶性的预测因素。影像组学模型、临床模型和联合诊断模型的AUC在训练组中分别为0.971（95%CI 0.884~0.996）、0.866（95%CI 0.786~0.925）和0.977（95%CI 0.827~0.996），在验证组中分别为0.883（95%CI 0.763~0.961）、0.692（95%CI 0.538~0.819）和0.934（95%CI 0.820~0.986）。
    结论 基于HRCT影像组学特征构建的模型可以有效预测肺孤立性GGN的良恶性。

     

    Abstract:
    Objective To explore the value of radiomics model based on high-resolution computed tomography (HRCT) in predicting the benign and malignant pulmonary solitary ground glass nodule (GGN) before operation.
    Methods The preoperative HRCT images of 152 patients with pulmonary solitary GGN from January 2019 to October 2022 in Shanghai Fengxian District Central Hospital were analyzed retrospectively. Patients were randomly divided into training group (n＝106) and validation group (n＝46) according to the proportion of 7∶3. According to the pathological results, patients in training group were divided into malignant group (n＝56) and benign group (n＝50), and clinical characteristics were compared between the two groups. The routine CT features of pulmonary solitary GGN in the training group were evaluated. PyRodiomics software was used to extract 107 radiomics features in the whole volume of interest (VOI) of each lesion and Lasso regression was used to screen the radiomics features. Logistic regression was used to establish three regression models (clinical model, radiomics model and combined model). The efficacy of the three prediction models was evaluated by the receiver operating characteristic (ROC) curve and area under the curve (AUC).
    Results A total of 13 radiomics features were screened by Lasso regression. Three clinical characteristics were significantly different between the malignant group and benign group. Radiomics features NCCT_original_firstorder_10Percentile, NCCT_original_glrlm_RunEntropy, and NCCT_original_shape_Sphericity, clinical features CT value, clinical feature score and radiomics score could be predictive factors for malignant pulmonary solitary GGN. AUC of the radiomicss model, clinical model, and combined model were 0.971 (95%CI 0.884-0.996), 0.866 (95%CI 0.786-0.925) and 0.977 (95%CI 0.827-0.996) in the train group and 0.883 (95%CI 0.763-0.961), 0.692 (95%CI 0.538-0.819) and 0.934 (95%CI 0.820-0.986) in validation group, respectively.
    Conclusions The radiomics model based on HRCT can effectively predict the benign and malignant of pulmonary solitary GGN.

     

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