Zhang Yifan, Wu Jin, He Jian, Xu Shanshan
Department of PET/CT Center, Jiangsu Cancer Hospital, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Institute of Cancer Research, Nanjing, China.
Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Quant Imaging Med Surg. 2023 Oct 1;13(10):6395-6411. doi: 10.21037/qims-22-1192. Epub 2023 Aug 17.
Serous cystic neoplasm (SCN), mucinous cystic neoplasm (MCN), and intraductal papillary mucinous neoplasm (IPMN) comprise a large proportion of pancreatic cystic neoplasms (PCNs). Patients with MCN and IPMN require surgery due to the potential of malignant transformation, whereas those with SCN require periodic surveillance. However, the differential diagnosis of patients with PCNs before treatment remains a great challenge for all surgeons. Therefore, the establishment of a reliable diagnostic tool is urgently required for the improvement of precision diagnostics.
Between February 2015 and December 2020, 143 consecutive patients with PCNs who were confirmed by postoperative pathology were retrospectively included in the study cohort, then randomized into development and test cohorts at a ratio of 7:3. The predictors of preoperative clinical-radiologic parameters were evaluated by univariate and multivariable logistic regression analyses. A total of 1,218 radiomics features were computationally extracted from the enhanced computed tomography (CT) scans of the tumor region, and a radiomics signature was established by the random forest algorithm. In the development cohort, multi- and binary-class radiomics models integrating preoperative variables and radiomics features were constructed to distinguish between the 3 types of PCNs. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive efficiency of the model. An independent internal test cohort was applied to validate the classification models.
All preoperative prediction models were built by integrating the radiomics signature with 13 diagnosis-related radiomics features and 3 important clinical-radiologic parameters: age, sex, and tumor diameter. The multiclass prediction model presented an overall accuracy of 0.804 in the development cohort and 0.707 in the test cohort. The binary-class prediction models displayed higher overall accuracies of 0.853, 0.866, and 0.928 in the development dataset and 0.750, 0.839, and 0.889 in the test dataset. In the test cohort, the binary-class radiomics models showed better predictive performances {AUC =0.914 [95% confidence interval (CI): 0.786 to 1.000], 0.863 (95% CI: 0.714 to 0.941), and 0.926 (95% CI: 0.824 to 1.000)} than the multiclass radiomics model [AUC =0.850 (95% CI: 0.696 to 1.000)], with a large net benefit in the decision curve analysis (DCA). The radiomics-based nomogram provided the correct predicted probability for the diagnosis of PCNs.
The proposed radiomics models with clinical-radiologic parameters and radiomics features help to predict the accurate diagnosis among PCNs to advance personalized medicine.
浆液性囊性肿瘤（SCN）、黏液性囊性肿瘤（MCN）和导管内乳头状黏液性肿瘤（IPMN）占胰腺囊性肿瘤（PCN）的很大比例。MCN和IPMN患者因有恶变可能需要手术治疗，而SCN患者则需要定期监测。然而，对于所有外科医生来说，治疗前PCN患者的鉴别诊断仍然是一个巨大的挑战。因此，迫切需要建立一种可靠的诊断工具来提高诊断的准确性。
2015年2月至2020年12月，将143例经术后病理证实的连续性PCN患者纳入研究队列，然后按7:3的比例随机分为开发队列和测试队列。通过单因素和多因素逻辑回归分析评估术前临床放射学参数的预测因素。从肿瘤区域的增强计算机断层扫描（CT）图像中计算提取总共1218个影像组学特征，并通过随机森林算法建立影像组学特征模型。在开发队列中，构建整合术前变量和影像组学特征的多分类和二分类影像组学模型，以区分3种类型的PCN。采用受试者操作特征（ROC）曲线和曲线下面积（AUC）评估模型的预测效率。应用独立的内部测试队列验证分类模型。
所有术前预测模型均通过将影像组学特征与13个与诊断相关的影像组学特征以及3个重要的临床放射学参数（年龄、性别和肿瘤直径）整合而建立。多分类预测模型在开发队列中的总体准确率为0.804，在测试队列中的总体准确率为0.707。二分类预测模型在开发数据集中的总体准确率更高，分别为0.853、0.866和0.928，在测试数据集中的总体准确率分别为0.750、0.839和0.889。在测试队列中，二分类影像组学模型的预测性能{AUC =0.914 [95%置信区间（CI）：0.786至1.000]，0.863（95%CI：从0.714至0.941），和0.926（95%CI：0.824至1.000）}优于多分类影像组学模型[AUC =0.850（95%CI：0.696至1.000）]，在决策曲线分析（DCA）中有很大的净效益。基于影像组学的列线图为PCN的诊断提供了正确的预测概率。
所提出的结合临床放射学参数和影像组学特征的影像组学模型有助于预测PCN的准确诊断，从而推进个性化医疗。
