PET-based radiomic feature based on the cross-combination method for predicting the mid-term efficacy and prognosis in high-risk diffuse large B‑cell lymphoma patients

Author:

Chen Man1,Rong Jian2,Zhao Jincheng1,Teng Yue3,Chen Jianxin2,Jiang Chong4,Xu jingyan3

Affiliation:

1. China Pharmaceutical University Nanjing Drum Tower Hospital

2. Nanjing University of Posts and Telecommunications

3. Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School

4. West China Hospital of Sichuan University

Abstract

Abstract Objectives This study aims to develop machine-learning cross-combinatorial methods for predicting the mid-term efficacy and prognosis in high-risk patients with diffuse large B-cell lymphoma (DLBCL). Methods Retrospectively, we recruited 177 high-risk DLBCL patients from two medical centers between October 2012 and September 2022 and divided them into a training cohort (n = 123) and a validation cohort (n = 52). We extracted 111 radiomic features along with SUVmax, MTV, and TLG from the baseline PET. 49 feature selection-classification pairs were using to obtain the Radiomics Score (RadScore). Logistic regression was employed to identify independent clinical and PET factors. The models were evaluated using receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was conducted to assess the predictive power of the models. The prognostic power of RadScore was assessed using Kaplan–Meier plots (KM). Results 177 patients (mean age,63 ± 13 years,129 men) were evaluated. Multivariate analyses showed that gender (OR,2.760;95%CI:1.196,6.368);p = 0.017), B symptoms (OR,4.065;95%CI:1.837,8.955; p = 0.001), SUVmax (OR,2.619;95%CI:1.107,6.194; p = 0.028), and RadScore (OR,7.167;95%CI:2.815,18.248; p<0.001) independently contributed to the risk factors for predicting mid-term outcome. The AUC values of the combined models in the training and validation groups were 0.846 (95%CI:0.775,0.917; p < 0.05) and 0.724 (95%CI:0.591,0.858; p < 0.05) respectively. DCA showed that the combined model incorporating RadScore, clinical risk factors, and metabolic metrics has optimal net clinical benefit. The low RadScore group outperformed progression-free survival (PFS)(HR,0.4601;95%CI:0.2748,0.7702) and overall survival (OS)(HR,0.4683,95%CI: 0.2754,0.7961) compared to the high RadScore group. Conclusion The combined model incorporating RadScore demonstrates a significant enhancement in predicting medium-term efficacy and prognosis in high-risk DLBCL patients. RadScore using selection-classification methods holds promise as a potential method for evaluating medium-term treatment outcome and prognosis in high-risk DLBCL patients.

Publisher

Research Square Platform LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3