基于计算机断层扫描的放射素学列图预测食管鳞状细胞癌患者的淋巴血管和周围性侵袭：回顾性队列研究

淋巴血管侵袭（LVI）和周围性侵袭（PNI）已被确定为各种类型癌症的预后因素。 LVI和PNI的术前预测有潜力指导食管鳞状细胞癌（ESCC）患者的个性化医学策略。这项研究研究了ESCC患者的LVI和PNI是否可以预测来自术前对比度增强的CT的放射素学特征。这项研究包括了接受食管切除术的544例ESCC患者的回顾性队列。收集了术前对比增强的CT图像，PNI和LVI的病理结果以及临床特征。对于每位患者，划定了总肿瘤体积（GTV-T）和淋巴结体积（GTV-N），并从GTV-T和GTV-N中提取了四类放射线特征（一阶，形状，纹理和小波）的四种类别。 Mann-Whitney U检验用于选择与LVI和PNI相关的重要特征。随后，使用带有十倍交叉验证的Lasso回归构建了LVI和PNI的放射组特征。将显着的临床特征与放射线学签名结合在一起，分别开发了两个列诺图模型，以预测LVI和PNI。曲线下的面积（AUC）和校准曲线用于评估模型的预测性能。LVI预测的放射组学签名由28个特征组成，而PNI放射线透镜签名则包括14个功能。在培训和验证组中，LVI放射素学签名的AUC分别为0.77和0.74，而PNI放射素学签名的AUC在培训和验证组中为0.69和0.68。 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总肿瘤和淋巴结的CT已显示出它们在ESCC患者中预测LVI和PNI方面的潜力。此外，临床特征的结合显示了额外的价值，从而提高了预测性能。

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.