Department of Radiation Oncology, Radiation Onocology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041, China.
Department of Thoracic Surgery, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, 610041, China.
Cancer Imaging. 2024 Oct 4;24(1):131. doi: 10.1186/s40644-024-00781-w.
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.
淋巴血管侵犯（LVI）和神经周围侵犯（PNI）已被确立为各种癌症的预后因素。术前预测 LVI 和 PNI 有可能为食管鳞状细胞癌（ESCC）患者的个体化治疗策略提供指导。本研究探讨了术前增强 CT 提取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 例接受食管切除术的 ESCC 患者的回顾性队列。收集了术前增强 CT 图像、PNI 和 LVI 的病理结果以及临床特征。为每位患者勾画大体肿瘤体积（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（一阶、形状、纹理和小波）。依次使用 Mann-Whitney U 检验选择与 LVI 和 PNI 相关的显著特征。然后，使用 10 折交叉验证的 LASSO 回归构建 LVI 和 PNI 的放射组学特征。将显著的临床特征与放射组学特征相结合，分别建立预测 LVI 和 PNI 的两个列线图模型。使用曲线下面积（AUC）和校准曲线评估模型的预测性能。
LVI 预测的放射组学特征由 28 个特征组成，PNI 放射组学特征由 14 个特征组成。LVI 放射组学特征的 AUC 在训练组和验证组分别为 0.77 和 0.74，PNI 放射组学特征的 AUC 在训练组和验证组分别为 0.69 和 0.68。将放射组学特征和年龄、性别、凝血酶时间、D-二聚体等显著临床特征相结合的列线图，在训练组和验证组中对 LVI（AUC：0.82 和 0.80）和 PNI（AUC：0.75 和 0.72）的预测性能均优于单独使用放射组学特征。
术前增强 CT 勾画的大体肿瘤和淋巴结的放射组学特征在预测 ESCC 患者的 LVI 和 PNI 方面显示出了一定的潜力。此外，纳入临床特征具有额外的价值，可提高预测性能。
