MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer

作者全名："Qiao, Xiaofeng; Gu, Xiling; Liu, Yunfan; Shu, Xin; Ai, Guangyong; Qian, Shuang; Liu, Li; He, Xiaojing; Zhang, Jingjing"

作者地址："[Qiao, Xiaofeng; Gu, Xiling; Liu, Yunfan; Shu, Xin; Ai, Guangyong; He, Xiaojing] Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, Chongqing 400010, Peoples R China; [Qian, Shuang; Liu, Li] Chongqing Univ, Big Data & Software Engn Coll, Chongqing 400000, Peoples R China; [Zhang, Jingjing] Natl Univ Singapore, Dept Diagnost Radiol, Singapore 119074, Singapore; [Zhang, Jingjing] Natl Univ Singapore, Clin Imaging Res Ctr, Ctr Translat Med, Singapore 117599, Singapore"

通信作者："He, XJ (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, Chongqing 400010, Peoples R China.; Zhang, JJ (通讯作者)，Natl Univ Singapore, Dept Diagnost Radiol, Singapore 119074, Singapore.; Zhang, JJ (通讯作者)，Natl Univ Singapore, Clin Imaging Res Ctr, Ctr Translat Med, Singapore 117599, Singapore."

来源：CANCERS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001075872100001

JCR分区：Q1

影响因子：4.5

年份：2023

卷号：15

期号：18

开始页：　

结束页：　

文献类型：Article

关键词：prostate cancer; MRI; Gleason grade group; Ki67; machine learning

摘要："Simple Summary: Given the variable aggressiveness of PCa, patients with indolent PCa do not require intervention, but rather require active surveillance and close lifelong follow-up, while those with invasive PCa require surgery, various types of radiation therapy, androgen-deprivation therapy (ADT), or multimodal treatment. Hence, it is critical to accurately distinguish indolent from invasive PCa for prognosis evaluation and treatment decision-making. The aim of the present study was to investigate the value of MR radiomics feature-based machine learning (ML) models in predicting the Ki67 index and Gleason grade group (GGG) of PCa. Biparametric magnetic resonance imaging (bpMRI) radiomics-based ML models to predict immuno-histochemically-determined Ki67 expression and the GGG demonstrated the ability to identify aggressive PCa. A preliminary exploration was performed in the conjoint analysis, laying the theoretical foundation for models predicting two or more variables; such models are expected to provide more comprehensive pathological information and provide valuable guidance for clinical decision-making in a noninvasive, synchronous, and objective manner. Purpose: The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa. Methods: A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices. Results: The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r = 0.382, p < 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230). Conclusion: The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method."

基金机构：grants from the General Program of the Joint Project of Chongqing Health Commission and Science and Technology Bureau [2019GDRC011]; High-Level Medical Reserved Personnel Training Project of Chongqing; Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University

基金资助正文："This work was supported by grants from the General Program of the Joint Project of Chongqing Health Commission and Science and Technology Bureau (2019GDRC011), the High-Level Medical Reserved Personnel Training Project of Chongqing and the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University."