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Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.
Wen, Dong-Yue; Chen, Jia-Min; Tang, Zhi-Ping; Pang, Jin-Shu; Qin, Qiong; Zhang, Lu; He, Yun; Yang, Hong.
Afiliação
  • Wen DY; Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
  • Chen JM; Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
  • Tang ZP; Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
  • Pang JS; Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
  • Qin Q; Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
  • Zhang L; Department of Medical Pathology, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China.
  • He Y; Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China. heyun@gxmu.edu.cn.
  • Yang H; Department of Medical Ultrasonics, Guangxi Zhuang Autonomous Region, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People's Republic of China. yanghong@gxmu.edu.cn.
Biomed Eng Online ; 23(1): 56, 2024 Jun 18.
Article em En | MEDLINE | ID: mdl-38890695
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Pancreáticas / Ultrassonografia / Aprendizado de Máquina / Metástase Linfática Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Biomed Eng Online Assunto da revista: ENGENHARIA BIOMEDICA Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Pancreáticas / Ultrassonografia / Aprendizado de Máquina / Metástase Linfática Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Biomed Eng Online Assunto da revista: ENGENHARIA BIOMEDICA Ano de publicação: 2024 Tipo de documento: Article