基于 MRI 的放射组学方法鉴别乏血供无功能胰腺神经内分泌肿瘤和胰腺实性假乳头状肿瘤。
MRI-based radiomics approach for differentiation of hypovascular non-functional pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms of the pancreas.
机构信息
Department of Radiology, Changhai Hospital, The Navy Medical University (Second Military Medical University), 168 Changhai Road, Shanghai, 200433, China.
GE Healthcare China, Pudong New Town, No.1 Huatuo Road, Shanghai, 210000, China.
出版信息
BMC Med Imaging. 2021 Feb 23;21(1):36. doi: 10.1186/s12880-021-00563-x.
BACKGROUND
This study aims to investigate the value of radiomics parameters derived from contrast enhanced (CE) MRI in differentiation of hypovascular non-functional pancreatic neuroendocrine tumors (hypo-NF-pNETs) and solid pseudopapillary neoplasms of the pancreas (SPNs).
METHODS
Fifty-seven SPN patients and twenty-two hypo-NF-pNET patients were enrolled. Radiomics features were extracted from T1WI, arterial, portal and delayed phase of MR images. The enrolled patients were divided into training cohort and validation cohort with the 7:3 ratio. We built four radiomics signatures for the four phases respectively and ROC analysis were used to select the best phase to discriminate SPNs from hypo-NF-pNETs. The chosen radiomics signature and clinical independent risk factors were integrated to construct a clinic-radiomics nomogram.
RESULTS
SPNs occurred in younger age groups than hypo-NF-pNETs (P < 0.0001) and showed a clear preponderance in females (P = 0.0185). Age was a significant independent factor for the differentiation of SPNs and hypo-NF-pNETs revealed by logistic regression analysis. With AUC values above 0.900 in both training and validation cohort (0.978 [95% CI, 0.942-1.000] in the training set, 0.907 [95% CI, 0.765-1.000] in the validation set), the radiomics signature of the arterial phase was picked to build a clinic-radiomics nomogram. The nomogram, composed by age and radiomics signature of the arterial phase, showed sufficient performance for discriminating SPNs and hypo-NF-pNETs with AUC values of 0.965 (95% CI, 0.923-1.000) and 0.920 (95% CI, 0.796-1.000) in the training and validation cohorts, respectively. Delong Test did not demonstrate statistical significance between the AUC of the clinic-radiomics nomogram and radiomics signature of arterial phase.
CONCLUSION
CE-MRI-based radiomics approach demonstrated great potential in the differentiation of hypo-NF-pNETs and SPNs.
背景
本研究旨在探讨从增强对比磁共振成像（CE-MRI）中提取的放射组学参数在鉴别低血供非功能性胰腺神经内分泌肿瘤（hypo-NF-pNETs）和胰腺实性假乳头状肿瘤（SPNs）中的价值。
方法
纳入 57 例 SPN 患者和 22 例 hypo-NF-pNET 患者。从 T1WI、动脉期、门静脉期和延迟期的 MRI 图像中提取放射组学特征。将入组患者按 7:3 的比例分为训练队列和验证队列。我们分别为四个阶段构建了四个放射组学特征，并使用 ROC 分析选择最佳阶段来区分 SPN 和 hypo-NF-pNET。选择的放射组学特征和临床独立危险因素被整合到一个临床放射组学列线图中。
结果
SPN 患者的年龄小于 hypo-NF-pNET 患者（P<0.0001），且女性患者明显多于男性（P=0.0185）。年龄是通过逻辑回归分析鉴别 SPN 和 hypo-NF-pNET 的显著独立因素。在训练和验证队列中，AUC 值均高于 0.900（训练集为 0.978[95%可信区间，0.942-1.000]，验证集为 0.907[95%可信区间，0.765-1.000]），动脉期的放射组学特征被选中构建临床放射组学列线图。该列线图由年龄和动脉期的放射组学特征组成，在训练和验证队列中鉴别 SPN 和 hypo-NF-pNET 的 AUC 值分别为 0.965（95%可信区间，0.923-1.000）和 0.920（95%可信区间，0.796-1.000），表现出足够的性能。Delong 检验表明临床放射组学列线图的 AUC 与动脉期的放射组学特征之间没有统计学意义。
结论
CE-MRI 为基础的放射组学方法在鉴别 hypo-NF-pNET 和 SPN 方面具有很大的潜力。
Zotero 插件