Comparison of Multiple Radiomics Models for Identifying Histological Grade of Pancreatic Ductal Adenocarcinoma Preoperatively Based on Multiphasic Contrast-Enhanced Computed Tomography: A Two-Center Study in Southwest China

作者全名："Liao, Hongfan; Li, Yongmei; Yang, Yaying; Liu, Huan; Zhang, Jiao; Liang, Hongwei; Yan, Gaowu; Liu, Yanbing"

作者地址："[Liao, Hongfan; Liu, Yanbing] Chongqing Med Univ, Coll Med Informat, 1 Yixueyuan Rd, Chongqing 400016, Peoples R China; [Li, Yongmei; Liang, Hongwei] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400016, Peoples R China; [Yang, Yaying] Chongqing Med Univ, Dept Pathol, Mol Med & Canc Res Ctr, Chongqing 400016, Peoples R China; [Liu, Huan] GE Healthcare, Shanghai 201203, Peoples R China; [Zhang, Jiao] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 3, Chongqing 401120, Peoples R China; [Yan, Gaowu] Suining Cent Hosp, Dept Radiol, Suining 429000, Peoples R China"

通信作者："Liu, YB (通讯作者)，Chongqing Med Univ, Coll Med Informat, 1 Yixueyuan Rd, Chongqing 400016, Peoples R China."

来源：DIAGNOSTICS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000845894400001

JCR分区：Q1

影响因子：3.6

年份：2022

卷号：12

期号：8

开始页：　

结束页：　

文献类型：Article

关键词：pancreatic ductal adenocarcinoma; histological grade; radiomics; machine learning; prognosis

摘要："Background: We designed and validated the value of multiple radiomics models for diagnosing histological grade of pancreatic ductal adenocarcinoma (PDAC), holding a promise of assisting in precision medicine and providing clinical therapeutic strategies. Methods: 198 PDAC patients receiving surgical resection and pathological confirmation were enrolled and classified as 117 low-grade PDAC and 81 high-grade PDAC group. An external validation group was used to assess models' performance. Available radiomics features were selected using GBDT algorithm on the basis of the arterial and venous phases, respectively. Five different machine learning models were built including k-nearest neighbour, logistic regression, naive bayes model, support vector machine, and random forest using ten times tenfold cross-validation. Multivariable logistic regression analysis was applied to establish clinical model and combined model. The models' performance was assessed according to its predictive performance, calibration curves, and decision curves. A nomogram was established for visualization. Survival analysis was conducted for stratifying the overall survival prior to treatment. Results: In the training group, the RF model demonstrated the optimal predictive ability and robustness with an AUC of 0.943; the SVM model achieved the secondary performance, followed by Bayes model. In the external validation group, these three models (Bayes, RF, SVM) also achieved the top three predictive ability. A clinical model was built by selected clinical features with an AUC of 0.728, and combined model was established by an RF model and a clinical model with an AUC of 0.961. The log-rank test revealed that the low-grade group survived longer than the high-grade group. Conclusions: The multiphasic CECT radiomics models offered an accurate and noninvasive perspective to differentiate histological grade in PDAC and advantages of machine learning models including RF, SVM and Bayes were more remarkable."

基金机构：National "Ten Thousand Talents Plan" Talent Special Project [9840X]

基金资助正文：This research received National "Ten Thousand Talents Plan" Talent Special Project (9840X).