Abstract：
BACKGROUND: Early diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN).
METHODS: Prospective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes\' theorem.
RESULTS: The radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80.
CONCLUSIONS: Incorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.
METHODS: Prospective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes\' theorem.
RESULTS: The radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80.
CONCLUSIONS: Incorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.
摘要：
背景：肺癌（LC）的早期诊断对于提高生存率至关重要。影像组学模型有望增强LC诊断。这项研究评估了基于深度学习整合临床和影像组学模型以预测肺结节（PN）恶性的影响。
方法：对93例患者的97例PNs进行前瞻性横断面研究。临床数据包括流行病学危险因素和肺功能检查。分析包含PN的每个胸部CT的感兴趣区域。影像组学模型采用预训练的卷积网络来提取视觉特征。从这些特征来看,选择具有正标准偏差的500作为优化神经网络的输入。使用临床数据通过逻辑回归模型估计临床模型。来自临床模型的恶性概率被用作疾病预测概率的最佳估计，以使用贝叶斯定理的列线图更新放射学模型的恶性概率。
结果：影像组学模型的阳性预测值（PPV）为86％，79%的准确度和0.67的AUC。临床模型确定了DLCO，梗阻指数和吸烟状况是与结局相关的最一致的临床预测因子。将临床特征集成到深度学习影像组学模型中可实现94%的PPV，准确率为76%，AUC为0.80。
结论：将临床数据纳入深度学习影像组学模型可改善PN恶性肿瘤评估，提高预测性能。这项研究支持基于图像和临床特征相结合的潜力，以改善LC诊断。
方法：对93例患者的97例PNs进行前瞻性横断面研究。临床数据包括流行病学危险因素和肺功能检查。分析包含PN的每个胸部CT的感兴趣区域。影像组学模型采用预训练的卷积网络来提取视觉特征。从这些特征来看,选择具有正标准偏差的500作为优化神经网络的输入。使用临床数据通过逻辑回归模型估计临床模型。来自临床模型的恶性概率被用作疾病预测概率的最佳估计，以使用贝叶斯定理的列线图更新放射学模型的恶性概率。
结果：影像组学模型的阳性预测值（PPV）为86％，79%的准确度和0.67的AUC。临床模型确定了DLCO，梗阻指数和吸烟状况是与结局相关的最一致的临床预测因子。将临床特征集成到深度学习影像组学模型中可实现94%的PPV，准确率为76%，AUC为0.80。
结论：将临床数据纳入深度学习影像组学模型可改善PN恶性肿瘤评估，提高预测性能。这项研究支持基于图像和临床特征相结合的潜力，以改善LC诊断。
