分子影像学杂志2024，Vol.47Issue(8)：793-799,7.DOI:10.12122/j.issn.1674-4500.2024.08.03
基于多参数磁共振的影像组学模型预测前列腺癌Ki67的表达
Prediction of Ki67 expression in prostate cancer by radiomics models based on multiparameter magnetic resonance imaging
摘要
Abstract
Objective To establish a radiomics model to predict Ki67 expression of prostate cancer on multiparameter magnetic resonance imaging(mp-MRI).Methods A total of 176 prostate cancer patients confirmed by postoperative pathology in our hospital from December 1,2020 to June 30,2023 with complete magnetic resonance date were rest retrospectively analyzed,and the patients were divided to the training group(n=140)and validation group(n=36)in a 7:3 ratio.The DICOM images of patients T2-weighted imaging(T2WI),fat-suppression T2-weighted imaging(FS-T2WI),zoomed imaging technique with parallel transmission diffusion weighted imaging(ZOOMit-DWI),the apparent diffusion coefficient(ADC)were exported from the PACS workstation,the three-dimension volume region of interest of the tumor was manually delineated on the four sequential images,and radiomics features were extracted,and the Spearman correlation analysis and LASSO analysis were used to single out the most valuable radiomic features.The radiomics models were built using the radiomics features The diagnostic value of the model was analyzed by using ROC curve and calculating the AUC,and the diagnostic efficacy was verified in the validation group.Results A total of 1834 radiomics features were extracted from T2WI,FS-T2WI,ZOOMit-DWI,ADC and 20 features were selected,which were related to Ki67 status.Among the eight radiomics models established for Logistic Regression,Support Vector Machine,K-Nearest Neighbor,RandomForest,ExtraTrees,eXtreme Gradient Boosting,Light Gradient Boosting Machine,Multilayer Perceptron,The Light Gradient Boosting Machine model was optimal with an AUC of 0.948(95%CI:0.913-0.982)in the training group and an AUC of the test group of 0.832(95%CI:0.698-0.967).Conclusion The radiomics models based on mp-MRI can noninvasively predict the expression of Ki67,and the LightGBM model is the best.关键词
前列腺癌/影像组学/多参数磁共振成像/Ki67/小视野扩散加权成像Key words
prostate cancer/radiomics/multiparameter magnetic resonance imaging/Ki67/zoomed imaging technique with parallel transmission diffusion weighted imaging引用本文复制引用
翟承凤,何永胜,戚轩,杨宏楷,杨馨..基于多参数磁共振的影像组学模型预测前列腺癌Ki67的表达[J].分子影像学杂志,2024,47(8):793-799,7.基金项目
安徽省重点研究与开发计划(2022e07020065) （2022e07020065）
