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Abstract：
UNASSIGNED: The radiographic classification of pulmonary nodules into benign versus malignant categories is a pivotal component of early lung cancer diagnosis. The present study aimed to investigate clinical and computed tomography (CT) clinical-radiomics nomogram for preoperative differentiation of benign and malignant pulmonary nodules.
UNASSIGNED: This retrospective study included 342 patients with pulmonary nodules who underwent high-resolution CT (HRCT) examination. We assigned them to a training dataset (n=239) and a validation dataset (n=103). There are 1781 tumor characteristics quantified by extracted features from the lesion segmented from patients\' CT images. The features with poor reproducibility and high redundancy were removed. Then a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to further select features and build radiomics signatures. The independent predictive factors were identified by multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability. The performance and clinical utility of the clinical-radiomics nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
UNASSIGNED: After dimension reduction by the LASSO algorithm and multivariate logistic regression, four radiomic features were selected, including original_shape_Sphericity, exponential_glcm_Maximum Probability, log_sigma_2_0_mm_3D_glcm_Maximum Probability, and ogarithm_firstorder_90Percentile. Multivariate logistic regression showed that carcinoembryonic antigen (CEA) [odds ratio (OR) 95% confidence interval (CI): 1.40 (1.09-1.88)], CT rad score [OR (95% CI): 2.74 (2.03-3.85)], and cytokeratin-19-fragment (CYFRA21-1) [OR (95% CI): 1.80 (1.14-2.94)] were independent influencing factors of malignant pulmonary nodule (all P<0.05). The clinical-radiomics nomogram combining CEA, CYFRA21-1 and radiomics features achieved an area of curve (AUC) of 0.85 and 0.76 in the training group and verification group for the prediction of malignant pulmonary nodules. The clinical-radiomics nomogram demonstrated excellent agreement and practicality, as evidenced by the calibration curve and DCA.
UNASSIGNED: The clinical-radiomics nomogram combined of CT-based radiomics signature, along with CYFRA21-1 and CEA, demonstrated strong predictive ability, calibration, and clinical usefulness in distinguishing between benign and malignant pulmonary nodules. The use of CT-based radiomics has the potential to assist clinicians in making informed decisions prior to biopsy or surgery while avoiding unnecessary treatment for non-cancerous lesions.
UNASSIGNED: This retrospective study included 342 patients with pulmonary nodules who underwent high-resolution CT (HRCT) examination. We assigned them to a training dataset (n=239) and a validation dataset (n=103). There are 1781 tumor characteristics quantified by extracted features from the lesion segmented from patients\' CT images. The features with poor reproducibility and high redundancy were removed. Then a least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to further select features and build radiomics signatures. The independent predictive factors were identified by multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability. The performance and clinical utility of the clinical-radiomics nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
UNASSIGNED: After dimension reduction by the LASSO algorithm and multivariate logistic regression, four radiomic features were selected, including original_shape_Sphericity, exponential_glcm_Maximum Probability, log_sigma_2_0_mm_3D_glcm_Maximum Probability, and ogarithm_firstorder_90Percentile. Multivariate logistic regression showed that carcinoembryonic antigen (CEA) [odds ratio (OR) 95% confidence interval (CI): 1.40 (1.09-1.88)], CT rad score [OR (95% CI): 2.74 (2.03-3.85)], and cytokeratin-19-fragment (CYFRA21-1) [OR (95% CI): 1.80 (1.14-2.94)] were independent influencing factors of malignant pulmonary nodule (all P<0.05). The clinical-radiomics nomogram combining CEA, CYFRA21-1 and radiomics features achieved an area of curve (AUC) of 0.85 and 0.76 in the training group and verification group for the prediction of malignant pulmonary nodules. The clinical-radiomics nomogram demonstrated excellent agreement and practicality, as evidenced by the calibration curve and DCA.
UNASSIGNED: The clinical-radiomics nomogram combined of CT-based radiomics signature, along with CYFRA21-1 and CEA, demonstrated strong predictive ability, calibration, and clinical usefulness in distinguishing between benign and malignant pulmonary nodules. The use of CT-based radiomics has the potential to assist clinicians in making informed decisions prior to biopsy or surgery while avoiding unnecessary treatment for non-cancerous lesions.
摘要：
■肺结节的影像学分类为良性和恶性类别是早期肺癌诊断的关键组成部分。本研究旨在研究临床和计算机断层扫描（CT）临床-影像组学列线图，用于良恶性肺结节的术前鉴别。
■这项回顾性研究包括342例接受高分辨率CT（HRCT）检查的肺结节患者。我们将它们分配到训练数据集（n=239）和验证数据集（n=103）。通过从患者CT图像分割的病变中提取的特征量化了1781个肿瘤特征。去除再现性差和冗余性高的特征。然后使用具有10倍交叉验证的最小绝对收缩和选择算子（LASSO）逻辑回归模型来进一步选择特征并构建放射组学签名。通过多因素logistic回归确定独立预测因素。开发了放射组学列线图来预测恶性概率。通过受试者工作特征（ROC）曲线评估临床影像组学列线图的性能和临床实用性，校正曲线,和决策曲线分析(DCA)。
■在通过LASSO算法和多变量逻辑回归降维之后，选择了四个放射学特征，包括original_shape_Sphericity，指数_glcm_最大概率，log_sigma_2_0_mm_3D_glcm_最大概率，和ogarthm_firstorder_90百分位。多因素logistic回归显示癌胚抗原(CEA)[比值比(OR)95%置信区间(CI):1.40(1.09-1.88)],CTrad评分[OR（95%CI）：2.74（2.03-3.85）]，细胞角蛋白19片段(CYFRA21-1)[OR(95%CI):1.80(1.14～2.94)]是恶性肺结节的独立影响因素(均P<0.05)。结合CEA的临床-影像组学列线图，CYFRA21-1和影像组学特征在训练组和验证组中用于预测恶性肺结节的曲线面积（AUC）为0.85和0.76。临床-影像组学列线图显示出极好的一致性和实用性，校准曲线和DCA证明。
■结合基于CT的放射组学签名的临床放射组学列线图，以及CYFRA21-1和CEA，表现出很强的预测能力，校准,以及区分良性和恶性肺结节的临床有用性。基于CT的影像组学的使用有可能帮助临床医生在活检或手术之前做出明智的决定，同时避免非癌性病变的不必要治疗。
■这项回顾性研究包括342例接受高分辨率CT（HRCT）检查的肺结节患者。我们将它们分配到训练数据集（n=239）和验证数据集（n=103）。通过从患者CT图像分割的病变中提取的特征量化了1781个肿瘤特征。去除再现性差和冗余性高的特征。然后使用具有10倍交叉验证的最小绝对收缩和选择算子（LASSO）逻辑回归模型来进一步选择特征并构建放射组学签名。通过多因素logistic回归确定独立预测因素。开发了放射组学列线图来预测恶性概率。通过受试者工作特征（ROC）曲线评估临床影像组学列线图的性能和临床实用性，校正曲线,和决策曲线分析(DCA)。
■在通过LASSO算法和多变量逻辑回归降维之后，选择了四个放射学特征，包括original_shape_Sphericity，指数_glcm_最大概率，log_sigma_2_0_mm_3D_glcm_最大概率，和ogarthm_firstorder_90百分位。多因素logistic回归显示癌胚抗原(CEA)[比值比(OR)95%置信区间(CI):1.40(1.09-1.88)],CTrad评分[OR（95%CI）：2.74（2.03-3.85）]，细胞角蛋白19片段(CYFRA21-1)[OR(95%CI):1.80(1.14～2.94)]是恶性肺结节的独立影响因素(均P<0.05)。结合CEA的临床-影像组学列线图，CYFRA21-1和影像组学特征在训练组和验证组中用于预测恶性肺结节的曲线面积（AUC）为0.85和0.76。临床-影像组学列线图显示出极好的一致性和实用性，校准曲线和DCA证明。
■结合基于CT的放射组学签名的临床放射组学列线图，以及CYFRA21-1和CEA，表现出很强的预测能力，校准,以及区分良性和恶性肺结节的临床有用性。基于CT的影像组学的使用有可能帮助临床医生在活检或手术之前做出明智的决定，同时避免非癌性病变的不必要治疗。
