Liu Shijiancong, Li Huili, Xiao Xujie, Huo Wenwen, Li Xiaojian, Zhong Beilong, Gan Xiangfeng, Cao Qingdong
Department of Thoracic Surgery, the Fifth Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Zhuhai, China.
Department of Cardiothoracic Surgery, Chongqing University Qianjiang Hospital, Qianjiang Central Hospital of Chongqing, China.
J Thorac Dis. 2024 Sep 30;16(9):5675-5687. doi: 10.21037/jtd-24-601. Epub 2024 Sep 19.
Currently, patients with cT1 stage non-small cell lung cancer measuring less than 2 cm are recommended to undergo sublobar resection. However, it should be noted that there is tumor heterogeneity within these lung nodules. Potential visceral pleural invasion (VPI) is regarded as a significant factor that contributes to local recurrence and poorer prognosis after sublobar resection and postoperative upstaging of the T-stage. Currently, there are no effective techniques for preoperative or intraoperative prediction of the status of VPI in lung nodules. The primary objective of this study is to develop a machine learning model for the non-invasive prediction of VPI, thereby providing surgical decision-making support for surgeons during operations.
A total of 983 patients with nodules located within 5 mm distance from the pleura were included. Machine learning models were developed utilizing preprocessed 2D, 2.5D, and 3D computed tomography (CT) imaging data. These models were employed to predict the status of VPI, leveraging radiomics and deep learning techniques. The aforementioned three groups of data were further categorized into region of interest (ROI)-only (exclusively focused on the ROI) and ROI-rect (the minimum bounding rectangle or cuboid of the ROI) groups, based on whether they included images outside the ROI. Receiver operating characteristic (ROC) curves were created for the assessment of predictive accuracy across different models. Employing the Youden's index, patients were categorized into high-risk and low-risk groups based on the model's criteria, which was then followed by an in-depth analysis of overall survival rates among the distinct patient cohorts.
This study included a training cohort of 786 patients and a validation cohort of 197 patients. In comparison to radiomic and radiological models, deep learning models, especially the 2D-rect model, demonstrated better predictive performance. Although the 3D-ROI-only model exhibited the highest areas under the curve (AUC) value (0.952), its predictive performance for the status of VPI was found to be inferior according to the decision curve, calibration curve, and survival analysis.
The developed deep learning signature offers a robust instrument for the precise prognostication of vascular invasion in stage cT1 lung adenocarcinoma, thereby enhancing stratification for prognostic evaluation. Moreover, the application of this advanced computational model aids in the refinement of therapeutic approach formulation for individuals diagnosed with cT1 lung adenocarcinoma.
目前，推荐对直径小于2 cm的cT1期非小细胞肺癌患者进行亚肺叶切除。然而，应注意的是，这些肺结节内存在肿瘤异质性。潜在脏层胸膜侵犯（VPI）被认为是导致亚肺叶切除术后局部复发和预后较差以及T分期术后上调的重要因素。目前，尚无有效的术前或术中预测肺结节VPI状态的技术。本研究的主要目的是开发一种用于VPI无创预测的机器学习模型，从而在手术过程中为外科医生提供手术决策支持。
共纳入983例距胸膜5 mm以内有结节的患者。利用预处理的二维、2.5维及三维计算机断层扫描（CT）影像数据开发机器学习模型。这些模型利用放射组学和深度学习技术来预测VPI状态。上述三组数据根据是否包含感兴趣区域（ROI）以外的图像，进一步分为仅ROI组（仅专注于ROI）和ROI-rect组（ROI的最小边界矩形或长方体）。绘制受试者工作特征（ROC）曲线以评估不同模型的预测准确性。根据约登指数，根据模型标准将患者分为高风险和低风险组，随后对不同患者队列的总生存率进行深入分析。
本研究包括一个786例患者的训练队列和一个197例患者的验证队列。与放射组学和放射学模型相比，深度学习模型，尤其是二维-rect模型，表现出更好的预测性能。尽管三维-仅ROI模型的曲线下面积（AUC）值最高（0.952），但根据决策曲线、校准曲线和生存分析，其对VPI状态的预测性能较差。
所开发的深度学习特征为精确预测cT1期肺腺癌的血管侵犯提供了一种强大的工具，从而加强了预后评估的分层。此外，这种先进计算模型的应用有助于优化针对诊断为cT1期肺腺癌患者的治疗方案制定。
