建立基于松弛图的影像组学列线图,预测直肠癌(RC)壁外静脉侵犯(EMVI),并比较放射科医师主观评价的诊断效果。
在94例接受直接手术切除的RC患者中,65人被随机分配到训练队列,29人被随机分配到验证队列。从合成磁共振成像中提取影像组学特征,包括T1,T2和质子密度(PD)图。最小绝对收缩和选择算子方法用于降维,特征选择,和影像组学模型构建。多变量logistic回归分析用于列线图的发展。根据其校准评估列线图的性能,接收机工作特性(ROC)曲线,和决策曲线分析。
影像组学模型对EMVI表现出良好的预测效果,ROC曲线下面积(AUC),灵敏度,特异性为0.912(95%置信区间(CI),0.837-0.986),训练队列中的0.824和0.875,以及0.877(95%CI0.751-1.000),0.833和0.826在验证队列中。列线图具有良好的诊断性能,在训练和验证队列中,AUC分别为0.925(95%CI0.862-0.988)和0.899(95%CI0.782-1.000)。此外,与两位读者的主观评估相比,影像组学签名显示出更好的诊断效率(AUC=0.912vs.0.732和0.763,P分别=0.023和0.028)。
建立了放射组学列线图,用于术前预测RC患者的EMVI。基于松弛图的影像组学模型的应用可以提高EMVI的诊断效能。
To establish a radiomics nomogram based on relaxation maps for predicting the extramural venous invasion (EMVI) of rectal cancer (RC) and compare the diagnostic efficacy of the nomogram and subjective assessment by radiologists.
Among 94 RC patients receiving direct surgical resection, 65 were randomly allocated to the training cohort and 29 to the validation cohort. Radiomics features were extracted from synthetic magnetic resonance imaging including T1, T2, and proton density (PD) maps. The least absolute shrinkage and selection operator methods were used for dimension reduction, feature selection, and radiomics model building. Multivariable logistic regression analysis was used for nomogram development. The performance of the nomogram was assessed with respect to its calibration, receiver operating characteristics (ROC) curve, and decision curve analysis.
The radiomics model demonstrated good predictive efficacy for EMVI, with an area under the ROC curve (AUC), sensitivity, and specificity of 0.912 (95% confidence interval (CI), 0.837-0.986), 0.824, and 0.875 in the training cohort and 0.877 (95% CI 0.751-1.000), 0.833, and 0.826 in the validation cohort. The nomogram had good diagnostic performance, with AUCs of 0.925 (95% CI 0.862-0.988) and 0.899 (95% CI 0.782-1.000) in the training and validation cohort. Furthermore, the radiomics signature showed better diagnostic efficiency than the subjective assessment by both readers (AUC =0.912 vs. 0.732 and 0.763, P = 0.023 and 0.028, respectively).
A radiomics nomogram was developed to preoperatively predict EMVI in RC patients. The application of the radiomics model based on relaxation maps could improve the diagnostic efficacy of EMVI.