Noninvasive prediction of lymph-vascular space invasion in cervical cancer based on ultrasomics nomogram

The aim of this research was to develop a nomogram that integrates ultrasomics features and clinical factors to non-invasively predict preoperative lymph-vascular space invasion (LVSI) in patients with cervical cancer (CC). A total of 217 patients from three hospitals were retrospectively analyzed (the training set, n = 122; the test set, n = 53; and the validation set, n = 42). Tumor segmentation of the ultrasound(US) images was performed manually, then extracting a multitude of ultrasomics features from the segmented regions of interest (ROIs). After identifying the most significant ultrasomics features via a series of analyses and algorithms, five machine learning (ML) classification algorithms were utilized to develop and compare the ultrasomics models. Besides, we obtained clinically independent predictors for the diagnosis of LVSI and established the clinical model by univariate and multivariate analyses. Next, we compared the predictive capabilities of the clinical, ultrasomics, and combined models in forecasting LVSI in CC. Artificial neural networks (ANN) emerged as the top performer among the five ML classification algorithms. International Federation of Gynecology and Obstetrics (FIGO) staging for CC served as the independent predictor of LVSI. The nomogram, incorporating ultrasomics features and FIGO staging, demonstrated the highest diagnostic performance, with area under the curve (AUC) (95% CI) values of 0.911 (0.852–0.957), 0.835 (0.716–0.934), and 0.832 (0.685–0.939) in the training, test, and validation sets, respectively. Furthermore, the nomogram’s calibration curve exhibited excellent agreement between the predicted and actual LVSI outcomes in three datesets. The nomogram based on ultrasomics features and FIGO staging is a potential method for non-invasive prediction LVSI of CC.