Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma.
Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma.
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基于放射组学特征的肝细胞癌患者微血管侵犯预测模型
DOI：
10.3389/fonc.2020.574228
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发表时间：
2020
影响因子：
4.7
通讯作者：
Jia F
中科院分区：
文献类型：
--
作者：
He M;Zhang P;Ma X;He B;Fang C;Jia F
目的建立并评价一种基于放射组学特征的肝细胞癌术前微血管侵犯预测模型。方法将145例患者随机分为2个独立队列，训练队列101例，验证队列44例。为了对这一预测模型进行试点研究，另有18名患者被招募到这项研究中。从门脉期CT图像中提取了1231个无肿瘤肝实质的CT图像特征。应用最小绝对收缩和选择算子(LASSO)Logistic回归建立放射组学评分(Rad-Score)模型。然后，用多因素Logistic回归模型建立了包括Rad-Score和其他临床病理危险因素的诺模图。评价诺模图的辨别效能、校准效能和临床实用价值。结果Rad-Score模型可以预测MVI，训练队列的曲线下面积为0.637(95%CI，0.516~0.758)，验证队列的曲线下面积为0.583(95%CI，0.395~0.770)，但上述判别方法不能完全优于现有的预测因子(甲胎蛋白、中性粒细胞、术前血红蛋白)。包括RAD评分、甲胎蛋白、中性粒细胞、血红蛋白的个体化预测诺模图的AUC值为0.865(95%CI，0.786~0.944)，高于常规方法的AUC值(分别为P<；0.001，P=0.025，P<；0.001，P=0.001)。当应用于验证队列时，诺模图辨别效率仍然优于上述三种方法(AUC：0.705；95%CI，0.537-0.874)。该方法的校正曲线在两个队列中显示出令人满意的一致性。一项前瞻性先导分析显示，诺模图可以预测MVI，其AUC值为0.844(95%CI，0.628-1.000)。结论基于放射组学特征的预测模型显著改善了肝癌患者术前MVI的预测。这可能是一种潜在的有价值的临床实用工具。
Objective This study aimed to build and evaluate a radiomics feature-based model for the preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma. Methods A total of 145 patients were retrospectively included in the study pool, and the patients were divided randomly into two independent cohorts with a ratio of 7:3 (training cohort: n = 101, validation cohort: n = 44). For a pilot study of this predictive model another 18 patients were recruited into this study. A total of 1,231 computed tomography (CT) image features of the liver parenchyma without tumors were extracted from portal-phase CT images. A least absolute shrinkage and selection operator (LASSO) logistic regression was applied to build a radiomics score (Rad-score) model. Afterwards, a nomogram, including Rad-score as well as other clinicopathological risk factors, was established with a multivariate logistic regression model. The discrimination efficacy, calibration efficacy, and clinical utility value of the nomogram were evaluated. Results The Rad-score scoring model could predict MVI with the area under the curve (AUC) of 0.637 (95% CI, 0.516–0.758) in the training cohort as well as of 0.583 (95% CI, 0.395–0.770) in the validation cohort; however, the aforementioned discriminative approach could not completely outperform those existing predictors (alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin). The individual predictive nomogram which included the Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin showed a better discrimination efficacy with AUC of 0.865 (95% CI, 0.786–0.944), which was higher than the conventional methods’ AUCs (nomogram vs Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin at P < 0.001, P = 0.025, P < 0.001, and P = 0.001, respectively). When applied to the validation cohort, the nomogram discrimination efficacy was still outbalanced those above mentioned three remaining methods (AUC: 0.705; 95% CI, 0.537–0.874). The calibration curves of this proposed method showed a satisfying consistency in both cohorts. A prospective pilot analysis showed that the nomogram could predict MVI with an AUC of 0.844 (95% CI, 0.628–1.000). Conclusions The radiomics feature-based predictive model improved the preoperative prediction of MVI in HCC patients significantly. It could be a potentially valuable clinical utility.
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影响因子：
11.2
作者：
van Griethuysen JJM;Fedorov A;Parmar C;Hosny A;Aucoin N;Narayan V;Beets-Tan RGH;Fillion-Robin JC;Pieper S;Aerts HJWL
通讯作者：
Aerts HJWL
影响因子：
29.4
作者：
Roayaie S;Blume IN;Thung SN;Guido M;Fiel MI;Hiotis S;Labow DM;Llovet JM;Schwartz ME
通讯作者：
Schwartz ME
乙型肝炎病毒相关肝细胞癌术前预测微血管侵犯风险的放射组学列线图
影响因子：
2.1
作者：
Peng, Jie;Zhang, Jing;Liu, Li
通讯作者：
Liu, Li
影响因子：
3.8
作者：
Du M;Chen L;Zhao J;Tian F;Zeng H;Tan Y;Sun H;Zhou J;Ji Y
通讯作者：
Ji Y
影响因子：
16.9
作者：
Lei, Zhengqing;Li, Jun;Shen, Feng
通讯作者：
Shen, Feng