Multi-Phase CT-Based Radiomics Nomogram for Discrimination Between Pancreatic Serous Cystic Neoplasm From Mucinous Cystic Neoplasm

PurposeThis study aimed to develop and verify a multi-phase (MP) computed tomography (CT)-based radiomics nomogram to differentiate pancreatic serous cystic neoplasms (SCNs) from mucinous cystic neoplasms (MCNs), and to compare the diagnostic efficacy of radiomics models for different phases of CT scans.Materials and MethodsA total of 170 patients who underwent surgical resection between January 2011 and December 2018, with pathologically confirmed pancreatic cystic neoplasms (SCN=115, MCN=55) were included in this single-center retrospective study. Radiomics features were extracted from plain scan (PS), arterial phase (AP), and venous phase (VP) CT scans. Algorithms were performed to identify the optimal features to build a radiomics signature (Radscore) for each phase. All features from these three phases were analyzed to develop the MP-Radscore. A combined model comprised the MP-Radscore and imaging features from which a nomogram was developed. The accuracy of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration tests, and decision curve analysis.ResultsFor each scan phase, 1218 features were extracted, and the optimal ones were selected to construct the PS-Radscore (11 features), AP-Radscore (11 features), and VP-Radscore (12 features). The MP-Radscore (14 features) achieved better performance based on ROC curve analysis than any single phase did [area under the curve (AUC), training cohort: MP-Radscore 0.89, PS-Radscore 0.78, AP-Radscore 0.83, VP-Radscore 0.85; validation cohort: MP-Radscore 0.88, PS-Radscore 0.77, AP-Radscore 0.83, VP-Radscore 0.84]. The combination nomogram performance was excellent, surpassing those of all other nomograms in both the training cohort (AUC, 0.91) and validation cohort (AUC, 0.90). The nomogram also performed well in the calibration and decision curve analyses.ConclusionsRadiomics for arterial and venous single-phase models outperformed the plain scan model. The combination nomogram that incorporated the MP-Radscore, tumor location, and cystic number had the best discriminatory performance and showed excellent accuracy for differentiating SCN from MCN.

Laparoscopic S7 hepatectomy for hepatic mucinous neoplasm: a case report and literature review

Background Mucinous cystic neoplasm of the Liver is rare tumors with malignant potential that occur in the biliary epithelium. Because of its rare presentation, it is often misdiagnosed before surgery. Case presentation A 63-year-old female patient presented with intermittent upper abdominal pain for three months. Laparoscopic hepatectomy of Segment 7 was conducted based on the preoperative diagnosis of space-occupying lesion in the right lobe of the liver. Postoperative pathology showed a low-grade mucinous cystic neoplasm in the right posterior lobe of the liver. The preoperative CA19-9 level was significantly increased while the postoperative CA19-9 returned to the normal range. Conclusions The diagnosis of mucinous cystic neoplasm of the liver is closely related to the thickening of the cystic wall or the increase of CA19-9, which has great significance and deserves clinical attention.

Mucinous cystic neoplasm of pancreas a case report

Mucinous cystic neoplasm of pancreas are relatively rare >95% occur in the body and tail of pancreas. Majority occur in young and middle aged female containing ovarian type subepithelial stroma. These tumors are either premalignant (MCN with low grade dysplasia) or (MCN with high grade dysplasia) or invasive carcinoma. Differential diagnosis includes pancreatic pseudocyst and pancreatic hydatid cyst. Investigations include ultrasonography (USG), Magnetic resonance imaging (MRI), Contrast enhanced computed tomography (CECT) supplemented by endoscopic USG with cyst fluid aspiration.

Preoperative Differentiation of Pancreatic Cystic Neoplasm Subtypes on CT-Based Radiomics: SCN, MCN And IPMN

Background:The serous cystic neoplasm (SCN), mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN) comprise the large proportion of pancreatic cystic neoplasm (PCN). The appropriate clinical management of MCN and IPMN isextremely essential to improve the 5-years survival rate for the early detection of pancreatic cancer. However, the differential diagnosis of patients with PCN before the treatment is still a tough challenge for all surgeons. Therefore, a reliable diagnosis tool is urgently required to be established for the improvement of precision diagnostics.Method:Between February 2016 and December 2020, 143 consecutive patients with PCN who were confirmed by postoperative pathology were retrospectively included in the study cohort, randomized into development and test cohort at the ratio of 7:3. The predictors of preoperative clinical-radiologic paraments were evaluated by the use of univariate and multivariable logistic regression analysis. A total of 1218 radiomics features were computationally extracted from the enhanced computed tomography (CT) of tumor region and a radiomics signature was established by the random forest algorithm. In the development cohort, the multi-class and binary-class radiomics models integrating preoperative variables and radiomics features were constructed to distinguish between the three types of PCN. The independent internal test cohort was applied to validate the classification models.Result:All preoperative prediction models were built by integrating the radiomics signature with thirteen diagnosis-related radiomics features and three important clinical-radiologic parameters of age, sex and tumor diameter. The multi-class 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 the higher overall accuracy of 0.853, 0.866, 0.928 in the development dataset and 0.750, 0.839, 0.889 in the test dataset. In the test cohort, the binary-class radiomics models showed better predictive performances (AUC = 0.914, 0.863 ,0.926) than the multi-class radiomics model (AUC = 0.850), with a large net benefit in the decisive curve analysis. The radiomics-based nomogram provided the correct predicted probability for the diagnosis of PCN.Conclusion: The proposed radiomics models with clinical-radiologic parameters and radiomics features helped predict the accurate diagnosis among SCN, MCN, and IPMN to advance personalized medicine.