Integrated clinical-radiomic model for predicting treatment response of concurrent chemo-radiotherapy and radiotherapy alone in controversial subgroup of AJCC/UICC ninth edition stage I nasopharyngeal cancer.

IF 7 2区 医学 Q1 ONCOLOGY
Ka Yan Ng, Xinyue Chen, Mohan Huang, Luoyi Kong, Steven Kwoon-Ting Cheung, Lawrence Wing Chi Chan
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引用次数: 0

Abstract

Objective: Radiotherapy (RT) is the definitive treatment for stage II nasopharyngeal carcinoma (NPC), which is classified as stages IA and IB in the latest ninth edition of American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC). A crucial question is whether concurrent chemo-radiotherapy (CCRT) could derive additional benefits to this recent "down-staging" subgroup of NPC patients. This study aimed to interrogate clinical and radiomic features for predicting 5-year progression-free survival (PFS) of stage II NPC treated with RT alone or CCRT.

Methods: Imaging and clinical data of 166 stage II NPC (eighth edition AJCC/UICC) patients were collected. Data were allocated into training, internal testing, and external testing sets. For each case, 851 radiomic features were extracted and 10 clinical features were collected. Radiomic and clinical features most associated with the 5-year PFS were selected separately. A combined model was developed using multivariate logistic regression by integrating selected features and treatment option to predict 5-year PFS. Model performances were evaluated by area under the receiver operating curve (AUC), prediction accuracy, and decision curve analysis. Survival analyses including Kaplan-Meier analysis and Cox regression model were performed for further analysis.

Results: Thirteen radiomic features, three clinical features, and treatment option were considered for model development. The combined model showed higher prognostic performance than using either. For the merged testing set (internal and external testing sets), AUC is 0.76 (combined) vs. 0.56-0.80 (clinical or radiomic alone) and accuracy is 0.75 (combined) vs. 0.62-0.73 (clinical or radiomic alone). Kaplan-Meier analysis using the combined model showed significant discrimination in PFS of the predicted low-risk and high-risk groups in the training and internal testing cohorts (P<0.05).

Conclusions: Integrating with clinical and radiomic features could provide prognostic information on 5-year PFS under either treatment regimen, guiding individualized decisions of chemotherapy based on the predicted treatment outcome.

AJCC/UICC第九版ⅰ期鼻咽癌争议亚组同步放化疗和单独放疗的综合临床-放射学模型预测治疗反应
目的:放疗(RT)是II期鼻咽癌(NPC)的最终治疗方法,在最新第九版美国癌症联合委员会(AJCC)/国际癌症控制联盟(UICC)中,鼻咽癌分为IA期和IB期。一个关键的问题是同步化疗(CCRT)是否可以为最近的“低分期”鼻咽癌患者亚组带来额外的益处。本研究旨在探讨临床和放射学特征,以预测单纯放疗或CCRT治疗II期鼻咽癌的5年无进展生存期(PFS)。方法:收集166例II期鼻咽癌(第八版AJCC/UICC)患者的影像学及临床资料。数据被分配到训练集、内部测试集和外部测试集。每个病例提取851个放射学特征,收集10个临床特征。分别选择与5年PFS最相关的放射学和临床特征。采用多变量逻辑回归,结合所选特征和治疗方案,建立了一个联合模型来预测5年PFS。通过受试者工作曲线下面积(AUC)、预测精度和决策曲线分析来评价模型的性能。生存分析包括Kaplan-Meier分析和Cox回归模型进行进一步分析。结果:13个放射学特征,3个临床特征和治疗方案被考虑到模型的建立。联合模型的预后效果优于任何一种。对于合并的检测集(内部和外部检测集),AUC为0.76(联合)vs. 0.56-0.80(临床或单独放射组学),准确率为0.75(联合)vs. 0.62-0.73(临床或单独放射组学)。联合模型Kaplan-Meier分析显示,在培训组和内测组中,预测的低危组和高危组的PFS存在显著差异(PFS)。结论:结合临床和放射学特征,可以提供两种治疗方案下5年PFS的预后信息,指导基于预测治疗结果的个性化化疗决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
自引率
9.80%
发文量
1726
审稿时长
4.5 months
期刊介绍: Chinese Journal of Cancer Research (CJCR; Print ISSN: 1000-9604; Online ISSN:1993-0631) is published by AME Publishing Company in association with Chinese Anti-Cancer Association.It was launched in March 1995 as a quarterly publication and is now published bi-monthly since February 2013. CJCR is published bi-monthly in English, and is an international journal devoted to the life sciences and medical sciences. It publishes peer-reviewed original articles of basic investigations and clinical observations, reviews and brief communications providing a forum for the recent experimental and clinical advances in cancer research. This journal is indexed in Science Citation Index Expanded (SCIE), PubMed/PubMed Central (PMC), Scopus, SciSearch, Chemistry Abstracts (CA), the Excerpta Medica/EMBASE, Chinainfo, CNKI, CSCI, etc.
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