淋巴血管侵犯（LVI）和神经周围侵犯（PNI）已被确定为各种类型癌症的预后因素。 LVI 和 PNI 的术前预测有可能指导食管鳞状细胞癌 (ESCC) 患者的个性化医疗策略。本研究探讨术前增强 CT 得出的放射组学特征是否可以预测 ESCC 患者的 LVI 和 PNI。本研究纳入了 544 名接受食管切除术的 ESCC 患者的回顾性队列。收集术前增强CT图像、PNI和LVI的病理结果以及临床特征。对于每位患者，勾画出大体肿瘤体积（GTV-T）和淋巴结体积（GTV-N），并从 GTV-T 和 GTV-N 中提取四类放射组学特征（一级、形状、纹理和小波）。 N。采用Mann-Whitney U检验依次选择与LVI和PNI相关的显着特征。随后，使用 LASSO 回归和十倍交叉验证构建了 LVI 和 PNI 的放射组学特征。将显着的临床特征与放射组学特征相结合，开发了两种列线图模型，分别用于预测 LVI 和 PNI。曲线下面积（AUC）和校准曲线用于评估模型的预测性能。LVI预测的放射组学特征由28个特征组成，而PNI放射组学特征由14个特征组成。训练组和验证组中 LVI 放射组学特征的 AUC 分别为 0.77 和 0.74，而训练组和验证组中 PNI 放射组学特征的 AUC 分别为 0.69 和 0.68。结合放射组学特征和重要临床特征（例如年龄、性别、凝血酶时间和 D-二聚体）的列线图显示 LVI（训练组和验证组中 AUC：0.82 和 0.80）和 PNI（训练组和验证组中 AUC：0.75 和 0.72）的预测性能有所改善。从术前大体肿瘤和淋巴结的增强 CT 中提取的放射组学特征已证明其在预测 ESCC 患者的 LVI 和 PNI 方面的潜力。此外，临床特征的结合显示出额外的价值，从而提高了预测性能。© 2024。作者。

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.© 2024. The Author(s).