Hu Yuntao, Liu Nian, Tang Lingling, Liu Qianqian, Pan Ke, Lei Lixing, Huang Xiaohua
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Front Med (Lausanne). 2022 Mar 10;9:777368. doi: 10.3389/fmed.2022.777368. eCollection 2022.
To explore the diagnostic value of radiomics model based on magnetic resonance T2-weighted imaging for predicting the recurrence of acute pancreatitis.
We retrospectively collected 190 patients with acute pancreatitis (AP), including 122 patients with initial acute pancreatitis (IAP) and 68 patients with recurrent acute pancreatitis (RAP). At the same time, the clinical characteristics of the two groups were collected. They were randomly divided into training group and validation group in the ratio of 7:3. One hundred thirty-four cases in the training group, including 86 cases of IAP and 48 cases of RAP. There were 56 cases in the validation group, including 36 cases of IAP and 20 cases of RAP. Least absolute shrinkage and selection operator (LASSO) were used for feature screening. Logistic regression was used to establish the radiomics model, clinical model and combined model for predicting AP recurrence. The predictive ability of the three models was evaluated by the area under the curve (AUC). The recurrence risk in patients with AP was assessed using the nomogram.
The AUCs of radiomics model in training group and validation group were 0.804 and 0.788, respectively. The AUCs of the combined model in the training group and the validation group were 0.833 and 0.799, respectively. The AUCs of the clinical model in training group and validation group were 0.677 and 0.572, respectively. The sensitivities of the radiomics model, combined model, and clinical model were 0.646, 0.691, and 0.765, respectively. The specificities of the radiomics model, combined model, and clinical model were 0.791, 0.828, and 0.590, respectively. There was no significant difference in AUC between the radiomics model and the combined model for predicting RAP ( = 0.067). The AUCs of the radiomics model and combined model were greater than those of the clinical model ( = 0.008 and = 0.007, respectively).
Radiomics features based on magnetic resonance T2WI could be used as biomarkers to predict the recurrence of AP, and radiomics model and combined model can provide new directions for predicting recurrence of acute pancreatitis.
探讨基于磁共振T2加权成像的影像组学模型对预测急性胰腺炎复发的诊断价值。
回顾性收集190例急性胰腺炎（AP）患者，其中初发急性胰腺炎（IAP）患者122例，复发性急性胰腺炎（RAP）患者68例。同时收集两组患者的临床特征。将其按7∶3随机分为训练组和验证组。训练组134例，其中IAP 86例，RAP 48例。验证组56例，其中IAP 36例，RAP 20例。采用最小绝对收缩和选择算子（LASSO）进行特征筛选。采用逻辑回归建立预测AP复发的影像组学模型、临床模型和联合模型。通过曲线下面积（AUC）评估三种模型的预测能力。使用列线图评估AP患者的复发风险。
影像组学模型在训练组和验证组的AUC分别为0.804和0.788。联合模型在训练组和验证组的AUC分别为0.833和0.799。临床模型在训练组和验证组的AUC分别为0.677和0.572。影像组学模型、联合模型和临床模型的敏感度分别为0.646、0.691和0.765。影像组学模型、联合模型和临床模型的特异度分别为0.791、0.828和0.590。影像组学模型和联合模型预测RAP的AUC差异无统计学意义（P = 0.067）。影像组学模型和联合模型的AUC均大于临床模型（P分别为0.008和0.007）。
基于磁共振T2WI的影像组学特征可作为预测AP复发的生物标志物，影像组学模型和联合模型可为预测急性胰腺炎复发提供新方向。
