{"title":"Baseline <sup>18</sup>F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement.","authors":"Fenglian Jing, Xinchao Zhang, Yunuan Liu, Xiaolin Chen, Jianqiang Zhao, Xinming Zhao, Xiaoshan Chen, Huiqing Yuan, Meng Dai, Na Wang, Zhaoqi Zhang, Jingmian Zhang","doi":"10.1007/s12094-024-03633-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The objective of this investigation is to explore the capability of baseline <sup>18</sup>F-FDG PET/CT radiomics to predict the prognosis of diffuse large B-cell lymphoma (DLBCL) with extranodal involvement (ENI).</p><p><strong>Methods: </strong>126 patients diagnosed with DLBCL with ENI were included in the cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression was utilized to refine the optimum subset from the 1328 features. Cox regression analyses were employed to discern significant clinical variables and conventional PET parameters, which were then employed with radiomics score to develop combined model for predicting both progression-free survival (PFS) and overall survival (OS). The fitness and the predictive capability of the models were assessed via the Akaike information criterion (AIC) and concordance index (C-index).</p><p><strong>Results: </strong>62 patients experienced disease recurrence or progression and 28 patients ultimately died. The combined model exhibited a lower AIC value compared to the radiomics model and SDmax/clinical variables for both PFS (507.101 vs. 510.658 vs. 525.506) and OS (215.667 vs. 230.556 vs. 219.313), respectively. The C-indices of the combined model, radiomics model, and SDmax/clinical variables were 0.724, 0.704, and 0.615 for PFS, and 0.842, 0.744, and 0.792 for OS, respectively. Kaplan--Meier curves showed significantly higher rates of relapse and mortality among patients classified as high-risk compared to those classified as low-risk (all P < 0.05).</p><p><strong>Conclusions: </strong>The combined model of clinical variables, conventional PET parameters, and baseline PET/CT radiomics features demonstrates a higher accuracy in predicting the prognosis of DLBCL with ENI.</p>","PeriodicalId":50685,"journal":{"name":"Clinical & Translational Oncology","volume":" ","pages":"727-735"},"PeriodicalIF":2.8000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical & Translational Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12094-024-03633-y","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/31 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: The objective of this investigation is to explore the capability of baseline 18F-FDG PET/CT radiomics to predict the prognosis of diffuse large B-cell lymphoma (DLBCL) with extranodal involvement (ENI).
Methods: 126 patients diagnosed with DLBCL with ENI were included in the cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression was utilized to refine the optimum subset from the 1328 features. Cox regression analyses were employed to discern significant clinical variables and conventional PET parameters, which were then employed with radiomics score to develop combined model for predicting both progression-free survival (PFS) and overall survival (OS). The fitness and the predictive capability of the models were assessed via the Akaike information criterion (AIC) and concordance index (C-index).
Results: 62 patients experienced disease recurrence or progression and 28 patients ultimately died. The combined model exhibited a lower AIC value compared to the radiomics model and SDmax/clinical variables for both PFS (507.101 vs. 510.658 vs. 525.506) and OS (215.667 vs. 230.556 vs. 219.313), respectively. The C-indices of the combined model, radiomics model, and SDmax/clinical variables were 0.724, 0.704, and 0.615 for PFS, and 0.842, 0.744, and 0.792 for OS, respectively. Kaplan--Meier curves showed significantly higher rates of relapse and mortality among patients classified as high-risk compared to those classified as low-risk (all P < 0.05).
Conclusions: The combined model of clinical variables, conventional PET parameters, and baseline PET/CT radiomics features demonstrates a higher accuracy in predicting the prognosis of DLBCL with ENI.
目的:本研究旨在探索基线18F-FDG PET/CT放射组学预测弥漫大B细胞淋巴瘤(DLBCL)伴结节外受累(ENI)预后的能力。利用最小绝对缩小和选择算子(LASSO)Cox回归从1328个特征中提炼出最佳子集。Cox回归分析用于鉴别重要的临床变量和常规PET参数,然后与放射组学评分一起用于建立预测无进展生存期(PFS)和总生存期(OS)的组合模型。通过阿凯克信息准则(AIC)和一致性指数(C-index)评估模型的适配性和预测能力:结果:62 名患者疾病复发或进展,28 名患者最终死亡。在PFS(507.101 vs. 510.658 vs. 525.506)和OS(215.667 vs. 230.556 vs. 219.313)方面,联合模型的AIC值分别低于放射组学模型和SDmax/临床变量。综合模型、放射组学模型和SDmax/临床变量的C指数在PFS方面分别为0.724、0.704和0.615,在OS方面分别为0.842、0.744和0.792。Kaplan--Meier曲线显示,与低风险患者相比,高风险患者的复发率和死亡率明显更高(均为P):临床变量、传统PET参数和基线PET/CT放射组学特征的组合模型在预测伴有ENI的DLBCL预后方面具有更高的准确性。
期刊介绍:
Clinical and Translational Oncology is an international journal devoted to fostering interaction between experimental and clinical oncology. It covers all aspects of research on cancer, from the more basic discoveries dealing with both cell and molecular biology of tumour cells, to the most advanced clinical assays of conventional and new drugs. In addition, the journal has a strong commitment to facilitating the transfer of knowledge from the basic laboratory to the clinical practice, with the publication of educational series devoted to closing the gap between molecular and clinical oncologists. Molecular biology of tumours, identification of new targets for cancer therapy, and new technologies for research and treatment of cancer are the major themes covered by the educational series. Full research articles on a broad spectrum of subjects, including the molecular and cellular bases of disease, aetiology, pathophysiology, pathology, epidemiology, clinical features, and the diagnosis, prognosis and treatment of cancer, will be considered for publication.