Peng Liu1, Qianbiao Gu1, Xiaoli Hu2, Xianzheng Tan1, Jianbin Liu1, An Xie1, Feng Huang1. 1. Department of Radiology, Hunan Provincial People's Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, China. 2. Department of Radiology, First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China.
Abstract
PURPOSE: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. RESULTS: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. -1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.
PURPOSE: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. RESULTS: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. -1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.
Authors: Calogero Casà; Antonio Piras; Andrea D'Aviero; Francesco Preziosi; Silvia Mariani; Davide Cusumano; Angela Romano; Ivo Boskoski; Jacopo Lenkowicz; Nicola Dinapoli; Francesco Cellini; Maria Antonietta Gambacorta; Vincenzo Valentini; Gian Carlo Mattiucci; Luca Boldrini Journal: Ther Adv Gastrointest Endosc Date: 2022-03-16