Objectives:This study utilized artificial intelligence (AI)–based radiomics analysis of computed tomography (CT) images using a modified U–Net for lung nodule segmentation and convolutional neural network based on VGG–16 to predict lymphovascular invasion (LVI) in stage 0–I lung adenocarcinoma. Additionally, the study investigated whether combining radiomics data with serum microRNA (miR)–30d level as a potential biomarker could enhance predictive performance.Methods:A total of 1265 patients who underwent complete resection between 2008 and 2018 were included. AI–based CT analysis was performed, and logistic regression was applied to predict LVI using 35 imaging features. A risk score (RS) generated from 840 patients in the derivation cohort was used to identify a high–risk group, with validation performed using 425 patients. Additionally, 47 cases with extracellular vesicle (EV)–derived miR–30d level data were analyzed to evaluate the value of the integrated approach.Results:Among all the patients, 467 patients (36.9%) were LVI–positive, and LVI was independently associated with poorer overall survival. The receiver operating characteristic curve for LVI based on the RS yielded an area under the curve of 0.899. For LVI prediction, the sensitivity, specificity, and accuracy were 84.8%, 83.7%, and 83.9%, respectively, in the derivation group, and 82.3%, 79.4%, and 80.5%, respectively, in the validation group. The integrated approach with miR–30d enhanced the predictability of LVI, achieving a sensitivity of 93.3%, specificity of 70.5%, and accuracy of 85.1%.Conclusions:AI–based radiomics demonstrated high effectiveness for predicting LVI, with RSs showing broad clinical applications. The addition of EV–derived miR–30d modestly improved predictability.
目的：本研究采用基于人工智能（AI）的放射组学方法，通过改进的U-Net网络对计算机断层扫描（CT）图像进行肺结节分割，并基于VGG-16架构的卷积神经网络预测0–I期肺腺癌的淋巴血管侵犯（LVI）。同时，研究探讨了将放射组学数据与血清微小RNA（miR）-30d水平作为潜在生物标志物相结合，能否提升预测性能。 方法：研究纳入了2008年至2018年间接受完全切除术的1265例患者。采用基于AI的CT分析技术，并利用35项影像特征通过逻辑回归预测LVI。基于推导队列中840例患者生成的风险评分（RS）用于识别高危组，并在425例患者的验证队列中进行验证。此外，对47例具有细胞外囊泡（EV）来源的miR-30d水平数据的病例进行分析，以评估整合方法的预测价值。 结果：在所有患者中，467例（36.9%）为LVI阳性，且LVI与较差的总生存期独立相关。基于RS的LVI预测受试者工作特征曲线下面积为0.899。在LVI预测方面，推导组的敏感性、特异性和准确率分别为84.8%、83.7%和83.9%，验证组分别为82.3%、79.4%和80.5%。结合miR-30d的整合方法进一步提升了LVI的预测能力，敏感性达93.3%，特异性为70.5%，准确率为85.1%。 结论：基于AI的放射组学方法在预测LVI方面表现出高效性，风险评分具有广泛的临床应用前景。联合EV来源的miR-30d可适度提升预测效能。
肿瘤(癌症)患者之家