Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China.
Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA.
Abdom Radiol (NY). 2021 Jun;46(6):2637-2646. doi: 10.1007/s00261-021-02954-8. Epub 2021 Feb 8.
To develop and validate a CT-based radiomics nomogram in preoperative differential diagnosis of SCNs from mucin-producing PCNs.
A total of 89 patients consisting of 31 SCNs, 30 IPMNs, and 28 MCNs who underwent preoperative CT were analyzed. A total of 710 radiomics features were extracted from each case. Patients were divided into training (n = 63) and validation cohorts (n = 26) with a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) method and logistic regression analysis were used for feature selection and model construction. A nomogram was created from a comprehensive model consisting of clinical features and the fusion radiomics signature. A decision curve analysis was used for clinical decisions.
The radiomics features extracted from CT could assist with the differentiation of SCNs from mucin-producing PCNs in both the training and validation cohorts. The signature of the combination of the plain, late arterial, and venous phases had the largest areas under the curve (AUCs) of 0.960 (95% CI 0.910-1) in the training cohort and 0.817 (95% CI 0.651-0.983) in the validation cohort with good calibration. The value and efficacy of the nomogram was verified using decision curve analysis.
A comprehensive nomogram incorporating clinical features and fusion radiomics signature can differentiate SCNs from mucin-producing PCNs.
开发并验证一种基于 CT 的放射组学列线图，用于术前鉴别分泌黏液的胰腺囊性肿瘤中的实性假乳头状瘤。
共分析了 89 例患者，包括 31 例实性假乳头状瘤、30 例交界性导管内乳头状黏液瘤和 28 例黏液性囊腺瘤。从每个病例中提取了 710 个放射组学特征。患者分为训练集（n=63）和验证集（n=26），比例为 7:3。采用最小绝对收缩和选择算子（LASSO）方法和逻辑回归分析进行特征选择和模型构建。从包含临床特征和融合放射组学特征的综合模型中创建了一个列线图。采用决策曲线分析进行临床决策。
从 CT 中提取的放射组学特征可辅助鉴别训练集和验证集中分泌黏液的胰腺囊性肿瘤中的实性假乳头状瘤。平扫、动脉晚期和静脉期融合特征的特征具有最大的曲线下面积（AUC），在训练集中为 0.960（95%CI 0.910-1），在验证集中为 0.817（95%CI 0.651-0.983），具有良好的校准度。决策曲线分析验证了列线图的价值和效能。
综合列线图结合临床特征和融合放射组学特征可鉴别分泌黏液的胰腺囊性肿瘤中的实性假乳头状瘤。
