Predictive value of multi-parameter model incorporating PET-based radiomics features for survival of older patients(≥60 years) with diffuse large B-cell lymphoma
10.3760/cma.j.cn321828-202111119-00408
- VernacularTitle:PET影像组学特征多参数模型对≥60岁弥漫性大B细胞淋巴瘤患者生存预后的预测价值
- Author:
Chong JIANG
1
;
Yue TENG
;
Ang LI
;
Jianxin CHEN
;
Jingyan XU
Author Information
1. 南京大学医学院附属鼓楼医院核医学科，南京 210008
- Keywords:
Lymphoma, large B-cell, diffuse;
Aged;
Prognosis;
Forecasting;
Positron-emission tomography;
Fluorodeoxyglucose F18;
Radiomics
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2023;43(5):257-262
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the prognostic value of 18F-FDG PET-based radiomics features by machine learning in older patients(≥60 years) with diffuse large B-cell lymphoma (DLBCL). Methods:A total of 166 older patients (88 males, 78 females, age: 60-93 years) with DLBCL who underwent pre-therapy 18F-FDG PET/CT from March 2011 to November 2019 were enrolled in the retrospective study. There were 115 patients in training cohort and 51 patients in validation cohort. The lesions in PET images were manually drawn and the obtained radiomics features from patients in training cohort were selected by the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (Xgboost), and then classified by support vector machine (SVM) to build radiomics signatures (RS) for predicting overall survival (OS). A multi-parameter model was constructed by using Cox proportional hazard model and assessed by concordance index (C-index). Results:A total of 1 421 PET radiomics features were extracted and 10 features were selected to build RS. The univariate Cox regression analysis showed that RS was a predictor of OS (hazard ratio ( HR)=5.685, 95% CI: 2.955-10.939; P<0.001). The multi-parameter model that incorporated RS, metabolic metrics, and clinical risk factors, exhibited significant prognostic superiority over the clinical model, PET-based model, and the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) in terms of OS (training cohort: C-index: 0.752 vs 0.737 vs 0.739 vs 0.688; validation cohort: C-index: 0.845 vs 0.798 vs 0.844 vs 0.775). Conclusions:RS can be used as a survival predictor for older patients(≥60 years) with DLBCL. Furthermore, the multi-parameter model incorporating RS is able to successfully predict prognosis.
