论著摘要 |【Radiomics-CT】将影像组学特征与miRNA分类器结合可以改善对胰管导管内乳头状粘液性肿瘤的恶性病理学的预测(双语版)

2018-01-17 11:21:53 admin 0
标签:   影像组学 胰腺 CT 逻辑回归 前期恶性病变 miRNA

Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms.

发表日期: 2016.12.27   来源:Oncotarget. 2016 Dec 27;7(52):85785-85797.

作者:

Permuth JB1,2, Choi J3, Balarunathan Y4, Kim J5, Chen DT5, Chen L5 , Orcutt S2, Doepker MP6, Gage K3, Zhang G4,7, Latifi K4,7, Hoffe S2,7, Jiang K8, Coppola D8, Centeno BA8, Magliocco A8, Li Q4,9 , Trevino J10, Merchant N11, Gillies R4, Malafa M2; Florida Pancreas Collaborative.

作者介绍:

1. Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

2. Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

3. Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

4. Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

5. Biostatistics and Bioinformatics, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

6. Department of Clinical Surgery/Surgical Oncology, Palmetto Health/USC School of Medicine, Columbia, South Carolina, USA.

7. Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

8. Anatomic Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

9. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.

10. Department of Surgery, Division of General Surgery, University of Florida Health Sciences Center, Gainesville, Florida, USA.

11. Department of Surgery, Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, Miami, Florida, USA.

摘要

导管内乳头状粘液性肿瘤(IPMNs)是通过横断层面成像偶然发现的胰腺癌的前体细胞。 IPMN管理共识指南依赖于标准的放射学特征来预测病理学,但缺乏准确性。使用38例手术切除,病理学证实的IPMNs(20例良性,18例恶性肿瘤)和术前计算机断层扫描(CT)图像和匹配的基于血浆的“miRNA基因组分类器(MGC)”数据的回顾性队列研究,我们确定了定量“影像组学”CT特征(+/- MGC)跟标准的放射学特征检测“高风险”或“令人担忧”的恶性肿瘤相比,能否可以更准确地预测IPMN病理学。 逻辑回归,主成分分析和交叉验证用于进行关联分析,灵敏性,特异性,阳性和阴性预测值(PPV,NPV)进行估算。 MGC,“高风险”和“令人担忧的”的放射学特征,受试者工作特征曲线(AUC)值分别为0.83,0.84和0.54。 14个影像组学特征将恶性与良性IPMNs分开(p < 0.05),并且共有AUC = 0.77。将影像组学特征与MGC组合显示出AUC = 0.92,灵敏度(83%),特异性(89%),阳性预测值(88%)和阴性预测值(85%),优于其他模型。通过10折交叉验证进行不确定性评估,AUC > 0.80(0.87(95%CI:0.84-0.89))。这个概念验证研究表明,与传统的共识指南中考虑的“令人担忧的”放射学特征相比,非侵入性放射基因组学方法可能更准确地预测IPMN病理学。

Abstact

Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cancer precursors incidentally discovered by cross-sectional imaging. Consensus guidelines for IPMN management rely on standard radiologic features to predict pathology, but they lack accuracy. Using a retrospective cohort of 38 surgically-resected, pathologically-confirmed IPMNs (20 benign; 18 malignant) with preoperative computed tomography (CT) images and matched plasma-based 'miRNA genomic classifier (MGC)' data, we determined whether quantitative 'radiomic' CT features (+/- the MGC) can more accurately predict IPMN pathology than standard radiologic features 'high-risk' or 'worrisome' for malignancy. Logistic regression, principal component analyses, and cross-validation were used to examine associations. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were estimated. The MGC, 'high-risk,' and 'worrisome' radiologic features had area under the receiver operating characteristic curve (AUC) values of 0.83, 0.84, and 0.54, respectively. Fourteen radiomic features differentiated malignant from benign IPMNs (p<0.05) and="" collectively="" had="" an="" auc="" combining="" radiomic="" features="" with="" the="" mgc="" revealed="" superior="" sensitivity="" specificity="" ppv="" npv="" than="" other="" models.="" evaluation="" of="" uncertainty="" by="" 10-fold="" cross-validation="" retained="">0.80 (0.87 (95% CI:0.84-0.89)). This proof-of-concept study suggests a noninvasive radiogenomic approach may more accurately predict IPMN pathology than 'worrisome' radiologic features considered in consensus guidelines.

关键词:

miRNA;胰腺;前期恶性病变;影像组学;风险分级

Keywords:

miRNA; pancreas; pre-malignant lesions; radiomics; risk stratification

阅读原文:PMID: 27589689  PMCID: PMC5349874  DOI: 10.18632/oncotarget.11768


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