基于CT影像组学的肺癌淋巴血管和神经侵犯预测列线图：一项回顾性队列研究

淋巴血管侵犯（LVI）和神经侵犯（PNI）已被确立为多种癌症的预后因素。术前预测LVI和PNI具有指导个性化医疗策略的潜力，特别是对于食管鳞状细胞癌（ESCC）患者。本研究探讨利用术前增强CT影像中提取的影像组学特征是否能预测ESCC患者的LVI和PNI。本研究包括544例接受食管切除术的ESCC患者的回顾性队列。收集术前增强CT影像、PNI和LVI的病理结果及临床特征。对每位患者， delineated肿瘤体积（GTV-T）和淋巴结体积（GTV-N），并从GTV-T和GTV-N中提取四类影像组学特征（一级特征、形状、纹理和Wavelet特征）。采用Mann-Whitney U检验逐步筛选与LVI和PNI显著相关的特征。随后，利用LASSO回归结合十折交叉验证构建LVI和PNI的影像组学特征签名。将显著临床特征与影像组学签名结合，建立两个列线图模型，分别用于预测LVI和PNI。使用曲线下面积（AUC）和校准曲线评估模型的预测性能。LVI预测的影像组学签名包含28个特征，而PNI签名包含14个特征。在训练集和验证集中的AUC分别为0.77和0.74（LVI），0.69和0.68（PNI）。结合临床特征（如年龄、性别、凝血酶时间和D-二聚体）的列线图模型在训练组（AUC：0.82和0.75）及验证组（AUC：0.80和0.72）均优于单纯的影像组学签名。由术前增强CT中提取的肿瘤和淋巴结的影像组学特征显示出在预测ESCC患者LVI和PNI方面的潜力。此外，结合临床特征进一步提升了模型的预测性能。

Lympho-vascular invasion (LVI) and perineural invasion (PNI) have been established as prognostic factors in various types of cancers. The preoperative prediction of LVI and PNI has the potential to guide personalized medicine strategies for patients with esophageal squamous cell cancer (ESCC). This study investigates whether radiomics features derived from preoperative contrast-enhanced CT could predict LVI and PNI in ESCC patients.A retrospective cohort of 544 ESCC patients who underwent esophagectomy were included in this study. Preoperative contrast-enhanced CT images, pathological results of PNI and LVI, and clinical characteristics were collected. For each patient, the gross tumor volume (GTV-T) and lymph nodes volume (GTV-N) were delineated and four categories of radiomics features (first-order, shape, textural and wavelet) were extracted from GTV-T and GTV-N. The Mann-Whitney U test was used to select significant features associated with LVI and PNI in turn. Subsequently, radiomics signatures for LVI and PNI were constructed using LASSO regression with ten-fold cross-validation. Significant clinical characteristics were combined with radiomics signature to develop two nomogram models for predicting LVI and PNI, respectively. The area under the curve (AUC) and calibration curve were used to evaluate the predictive performance of the models.The radiomics signature for LVI prediction consisted of 28 features, while the PNI radiomics signature comprised 14 features. The AUCs of the LVI radiomics signature were 0.77 and 0.74 in the training and validation groups, respectively, while the AUCs of the PNI radiomics signature were 0.69 and 0.68 in the training and validation groups. The nomograms incorporating radiomics signatures and significant clinical characteristics such as age, gender, thrombin time and D-Dimer showed improved predictive performance for both LVI (AUC: 0.82 and 0.80 in the training and validation group) and PNI (AUC: 0.75 and 0.72 in the training and validation groups) compared to the radiomics signature alone.The radiomics features extracted from preoperative contrast-enhanced CT of gross tumor and lymph nodes have demonstrated their potential in predicting LVI and PNI in ESCC patients. Furthermore, the incorporation of clinical characteristics has shown additional value, resulting in improved predictive performance.