Yang Jing, Guo Xinli, Ou Xuejin, Zhang Weiwei, Ma Xuelei
State Key Laboratory of Biotherapy, Department of Biotherapy, West China Hospital, Cancer Center, Sichuan University, Chengdu, China.
West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China.
Front Oncol. 2019 Jun 12;9:494. doi: 10.3389/fonc.2019.00494. eCollection 2019.
This study was designed to estimate the performance of textural features derived from contrast-enhanced CT in the differential diagnosis of pancreatic serous cystadenomas and pancreatic mucinous cystadenomas. Fifty-three patients with pancreatic serous cystadenoma and 25 patients with pancreatic mucinous cystadenoma were included. Textural parameters of the pancreatic neoplasms were extracted using the LIFEx software, and were analyzed using random forest and Least Absolute Shrinkage and Selection Operator (LASSO) methods. Patients were randomly divided into training and validation sets with a ratio of 4:1; random forest method was adopted to constructed a diagnostic prediction model. Scoring metrics included sensitivity, specificity, accuracy, and AUC. Radiomics features extracted from contrast-enhanced CT were able to discriminate pancreatic mucinous cystadenomas from serous cystadenomas in both the training group (slice thickness of 2 mm, AUC 0.77, sensitivity 0.95, specificity 0.83, accuracy 0.85; slice thickness of 5 mm, AUC 0.72, sensitivity 0.90, specificity 0.84, accuracy 0.86) and the validation group (slice thickness of 2 mm, AUC 0.66, sensitivity 0.86, specificity 0.71, accuracy 0.74; slice thickness of 5 mm, AUC 0.75, sensitivity 0.85, specificity 0.83, accuracy 0.83). In conclusion, our study provided preliminary evidence that textural features derived from CT images were useful in differential diagnosis of pancreatic mucinous cystadenomas and serous cystadenomas, which may provide a non-invasive approach to determine whether surgery is needed in clinical practice. However, multicentre studies with larger sample size are needed to confirm these results.
本研究旨在评估对比增强CT衍生的纹理特征在胰腺浆液性囊腺瘤和胰腺黏液性囊腺瘤鉴别诊断中的性能。纳入了53例胰腺浆液性囊腺瘤患者和25例胰腺黏液性囊腺瘤患者。使用LIFEx软件提取胰腺肿瘤的纹理参数，并采用随机森林和最小绝对收缩和选择算子（LASSO）方法进行分析。患者以4:1的比例随机分为训练集和验证集；采用随机森林方法构建诊断预测模型。评分指标包括敏感性、特异性、准确性和AUC。在训练组（层厚2 mm，AUC 0.77，敏感性0.95，特异性0.83，准确性0.85；层厚5 mm，AUC 0.72，敏感性0.90，特异性0.84，准确性0.86）和验证组（层厚2 mm，AUC 0.66，敏感性0.86，特异性0.71，准确性0.74；层厚5 mm，AUC 0.75，敏感性0.85，特异性0.83，准确性0.83）中，对比增强CT提取的影像组学特征均能够区分胰腺黏液性囊腺瘤和浆液性囊腺瘤。总之，我们的研究提供了初步证据，表明CT图像衍生的纹理特征在胰腺黏液性囊腺瘤和浆液性囊腺瘤的鉴别诊断中有用，这可能为临床实践中确定是否需要手术提供一种非侵入性方法。然而，需要更大样本量的多中心研究来证实这些结果。
