Mei Kun, Feng Zikang, Liu Hui, Wang Min, Ce Chao, Yin Shi, Zhang Xiaoying, Wang Bin
Department of Cardiothoracic Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, China.
School of Computer Science and Technology, Nanjing Tech University, Nanjing, China.
BMC Cancer. 2025 Apr 10;25(1):659. doi: 10.1186/s12885-025-14027-w.
The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment of GGN infiltration status using computed tomography (CT) imaging integrated with a neural network to enhance the precision of clinical decision-making in surgical planning and therapeutic interventions.
This multicenter retrospective study analyzed clinical data to quantify mismatch rates in surgical approaches across varying infiltration statuses. Regions of interest (ROIs) within the CT lung window level were manually delineated using ITK-SNAP software, enabling the extraction of relevant CT imaging features, including morphological descriptors, first-order statistical parameters, texture attributes, and high-order characteristics. Feature selection was performed using the Lasso algorithm to identify the most predictive variables, which were subsequently incorporated into the radiomics-based neural network model. The neural network architecture combined a 3D convolutional neural network (CNN) with random rotations for data augmentation and employed pre-trained parameters to optimize model weights.
The radiomics-integrated neural network exhibited high predictive performance, achieving an area under the subject operating characteristic curve (AUC) of 0.85, with validation set AUCs of 0.66 and 0.71. Additionally, the predicted mismatch rate between lobectomy and sublobectomy was 21.48%, representing a 35.57% reduction, while the mismatch rate within sublobectomy decreased by 13.66%, reaching 10.73% CONCLUSION: The neural network-enhanced imaging model provides a robust predictive tool for assessing the preoperative infiltration status of pulmonary GGNs. Its application significantly reduces mismatch rates in surgical decision-making, contributing to more precise and individualized treatment strategies.
肺磨玻璃结节（GGN）的浸润状态表现出显著的变异性，需要量身定制的手术策略和个体化的术后辅助治疗。本研究探索了使用计算机断层扫描（CT）成像结合神经网络对GGN浸润状态进行术前评估，以提高手术规划和治疗干预中临床决策的准确性。
这项多中心回顾性研究分析了临床数据，以量化不同浸润状态下手术方式的不匹配率。使用ITK-SNAP软件手动勾勒CT肺窗水平内的感兴趣区域（ROI），从而提取相关的CT成像特征，包括形态学描述符、一阶统计参数、纹理属性和高阶特征。使用套索算法进行特征选择，以识别最具预测性的变量，随后将这些变量纳入基于放射组学的神经网络模型。神经网络架构将三维卷积神经网络（CNN）与随机旋转相结合以进行数据增强，并采用预训练参数来优化模型权重。
整合放射组学的神经网络表现出较高的预测性能，受试者操作特征曲线（AUC）下面积为0.85，验证集AUC分别为0.66和0.71。此外，肺叶切除术和肺段切除术之间的预测不匹配率为21.48%，降低了35.57%，而肺段切除术中的不匹配率降低了13.66%，降至10.73%。结论：神经网络增强成像模型为评估肺GGN的术前浸润状态提供了一个强大的预测工具。其应用显著降低了手术决策中的不匹配率，有助于制定更精确和个体化的治疗策略。
