{"title":"预测非转移性鼻咽癌患者无进展生存期的放射病理组学综合模型。","authors":"Jing Hou, Xiaochun Yi, Handong Li, Qiang Lu, Huashan Lin, Junjun Li, Biao Zeng, Xiaoping Yu","doi":"10.1007/s00432-024-05930-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients.</p><p><strong>Methods: </strong>357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.</p><p><strong>Results: </strong>The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).</p><p><strong>Conclusion: </strong>The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma.\",\"authors\":\"Jing Hou, Xiaochun Yi, Handong Li, Qiang Lu, Huashan Lin, Junjun Li, Biao Zeng, Xiaoping Yu\",\"doi\":\"10.1007/s00432-024-05930-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients.</p><p><strong>Methods: </strong>357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.</p><p><strong>Results: </strong>The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).</p><p><strong>Conclusion: </strong>The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. 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引用次数: 0
摘要
目的:构建一个综合放射病理组学模型,用于预测非转移性鼻咽癌(NPC)患者的无进展生存期(PFS)。方法:本研究纳入了357例接受了治疗前核磁共振成像(MRI)和病理全切片成像(WSI)的鼻咽癌患者,并将其随机分为两组:训练集(n = 250)和验证集(n = 107)。从核磁共振成像中提取的放射组学特征采用最小冗余最大相关性、最小绝对收缩和选择算子法进行筛选。基于 WSI 的病理组学特征是使用深度学习架构 Swin Transformer 构建的。放射病理组学模型是通过整合三个特征集构建的:放射组学特征、病理组学特征和独立临床因素。该模型的预后效果使用一致性指数(C-index)进行评估。分层风险组的卡普兰-梅耶曲线通过对数秩检验进行检验:结果:放射病理组学模型显示出更优越的预测性能,与任何单一模式模型相比,训练集的C指数为0.791(95%置信区间[CI]:0.724-0.871),验证集的C指数为0.785(95% CI:0.716-0.875)(放射组学:0.619,95% CI:0.553-0.706;病理组学:0.732,95% CI:0.662-0.802;临床模型:0.655,95% CI:0.553-0.706):0.655,95% CI:0.581-0.728)(均为 P 结论:所开发的放射病理组学模型证明了其在预测鼻咽癌患者的生存期方面的可靠性。它有效地将患者分为不同的风险组别,为预后评估提供了有价值的见解。
Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma.
Purpose: To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients.
Methods: 357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test.
Results: The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001).
Conclusion: The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment.
期刊介绍:
The "Journal of Cancer Research and Clinical Oncology" publishes significant and up-to-date articles within the fields of experimental and clinical oncology. The journal, which is chiefly devoted to Original papers, also includes Reviews as well as Editorials and Guest editorials on current, controversial topics. The section Letters to the editors provides a forum for a rapid exchange of comments and information concerning previously published papers and topics of current interest. Meeting reports provide current information on the latest results presented at important congresses.
The following fields are covered: carcinogenesis - etiology, mechanisms; molecular biology; recent developments in tumor therapy; general diagnosis; laboratory diagnosis; diagnostic and experimental pathology; oncologic surgery; and epidemiology.