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Abstract：
BACKGROUND: To develop and validate a preoperative nomogram model combining the radiomics signature and clinical features for preoperative prediction of visceral pleural invasion (VPI) in lung nodules presenting as part-solid density.
METHODS: We retrospectively reviewed 156 patients with pathologically confirmed invasive lung adenocarcinomas after surgery from January 2016 to August 2019. The patients were split into training and validation sets by a ratio of 7:3. The radiomic features were extracted with the aid of FeAture Explorer Pro (FAE). A CT-based radiomics model was constructed to predict the presence of VPI and internally validated. Multivariable regression analysis was conducted to construct a nomogram model, and the performance of the models were evaluated with the area under the receiver operating characteristic curve (AUC) and compared with each other.
RESULTS: The enrolled patients were split into training (n = 109) and validation sets (n = 47). A total of 806 features were extracted and the selected 10 optimal features were used in the construction of the radiomics model among the 707 stable features. The AUC of the nomogram model was 0.888 (95% CI: 0.762-0.961), which was superior to the clinical model (0.787, 95% CI: 0.643-0.893; p = 0.049) and comparable to the radiomics model (0.879, 95% CI: 0.751-0.965; p > 0.05). The nomogram model achieved a sensitivity of 90.5% and a specificity of 76.9% in the validation dataset.
CONCLUSIONS: The nomogram model could be considered as a noninvasive method to predict VPI with either highly sensitive or highly specific diagnoses depending on clinical needs.
METHODS: We retrospectively reviewed 156 patients with pathologically confirmed invasive lung adenocarcinomas after surgery from January 2016 to August 2019. The patients were split into training and validation sets by a ratio of 7:3. The radiomic features were extracted with the aid of FeAture Explorer Pro (FAE). A CT-based radiomics model was constructed to predict the presence of VPI and internally validated. Multivariable regression analysis was conducted to construct a nomogram model, and the performance of the models were evaluated with the area under the receiver operating characteristic curve (AUC) and compared with each other.
RESULTS: The enrolled patients were split into training (n = 109) and validation sets (n = 47). A total of 806 features were extracted and the selected 10 optimal features were used in the construction of the radiomics model among the 707 stable features. The AUC of the nomogram model was 0.888 (95% CI: 0.762-0.961), which was superior to the clinical model (0.787, 95% CI: 0.643-0.893; p = 0.049) and comparable to the radiomics model (0.879, 95% CI: 0.751-0.965; p > 0.05). The nomogram model achieved a sensitivity of 90.5% and a specificity of 76.9% in the validation dataset.
CONCLUSIONS: The nomogram model could be considered as a noninvasive method to predict VPI with either highly sensitive or highly specific diagnoses depending on clinical needs.
摘要：
背景：开发并验证一种结合影像组学特征和临床特征的术前列线图模型，用于预测肺结节中内脏胸膜侵犯（VPI）的部分固体密度。
方法：回顾性分析2016年1月至2019年8月156例经手术病理证实的侵袭性肺腺癌患者。以7:3的比例将患者分成训练集和验证集。借助FeAtureExplorerPro（FAE）提取放射学特征。构建了基于CT的影像组学模型来预测VPI的存在并进行了内部验证。进行多元回归分析以构建列线图模型，用受试者工作特征曲线下面积（AUC）评估模型的性能，并相互比较。
结果：将入选患者分为训练组（n=109）和验证组（n=47）。总共提取了806个特征，并在707个稳定特征中，将所选的10个最佳特征用于构建影像组学模型。列线图模型的AUC为0.888(95%CI:0.762-0.961),优于临床模型(0.787,95%CI:0.643-0.893;p=0.049),与影像组学模型(0.879,95%CI:0.751-0.965;p>0.05)相当。在验证数据集中,列线图模型实现了90.5%的灵敏度和76.9%的特异性。
结论：根据临床需要，列线图模型可以被认为是一种非侵入性的方法来预测VPI，具有高度敏感性或高度特异性的诊断。
方法：回顾性分析2016年1月至2019年8月156例经手术病理证实的侵袭性肺腺癌患者。以7:3的比例将患者分成训练集和验证集。借助FeAtureExplorerPro（FAE）提取放射学特征。构建了基于CT的影像组学模型来预测VPI的存在并进行了内部验证。进行多元回归分析以构建列线图模型，用受试者工作特征曲线下面积（AUC）评估模型的性能，并相互比较。
结果：将入选患者分为训练组（n=109）和验证组（n=47）。总共提取了806个特征，并在707个稳定特征中，将所选的10个最佳特征用于构建影像组学模型。列线图模型的AUC为0.888(95%CI:0.762-0.961),优于临床模型(0.787,95%CI:0.643-0.893;p=0.049),与影像组学模型(0.879,95%CI:0.751-0.965;p>0.05)相当。在验证数据集中,列线图模型实现了90.5%的灵敏度和76.9%的特异性。
结论：根据临床需要，列线图模型可以被认为是一种非侵入性的方法来预测VPI，具有高度敏感性或高度特异性的诊断。
