Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

Abstract

Objectives: This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC).

Methods: This research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model.

Results: A total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions.

Conclusion: The combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.

Keywords: Lymph node metastasis; Machine learning; Pancreatic cancer; Radiomics; Ultrasound.

Fig. 1

A histogram of radiographic scoring based on the final selected radiomics features

Fig. 2

Receiver operating characteristic (ROC) curve set of radiomics-clinical parameter features. ROC curve set of radiomics models on the training a and testing b sets. ROC curve set of clinical parameter models on the training c and testing d cohorts. E. Collection of ROC curves of training e and testing f sets based on the logistic regression (LR) machine learning algorithm joint model

Fig. 3

Calibration curves in the training a and testing b sets. Decision curve analysis (DCA) of radiomics, clinical parameters, and joint models in the testing c cohort. Clinical application of omics model in lymph node metastasis of pancreatic cancer d

Fig. 4

The machine learning workflow diagram based on ultrasound-based imaging omics

Fig. 5

The workflow diagram of inclusion and exclusion criteria for this study