The "extracapsular cystic" sign in pancreatic serous cystic neoplasms: A clinicopathologic study of 177 patients with cystic pancreatic lesions

To identify important MRI features to differentiate hepatic mucinous cystic neoplasms from septated hepatic cysts based on random forest

OBJECTIVE

To identify important MRI features to differentiate hepatic mucinous cystic neoplasms (MCN) from septated hepatic cysts (HC) using random forest and compared with logistic regression algorithm.

METHODS

Pathologically diagnosed hepatic cysts and hepatic MCNs with pre-operative contrast-enhanced MRI in our hospital from 2010 to 2023 were collected and only septated lesions on enhanced MRI were enrolled. A total of 21 septated HC and 18 MCNs were included in this study. Eighteen MRI features were analyzed and top important features were identified based on random forest (RF) algorithm. The results were evaluated by the prediction performance of a RF model combining the important features and compared with the performance of the logistic regression (LR) algorithm. Finally, for each identified feature, diagnostic probability, sensitivity, and specificity were calculated and compared.

RESULTS

Four variables, i.e., the septation arising from wall without indentation, multiseptate, intracapsular cyst sign, and solitary lesion were extracted as top important features with significance for MCNs by the random forest algorithm. The RF model using these variables had an AUC of 0.982 (0.95CI, 0.950-1.000), compared with the LR model based on two identified features with AUC of 0.931 (0.95CI, 0.846-1.000), p = 0.202. Among the four important features, multiseptate had the highest specificity (95.2%) and good sensitivity (72.2%, lower than the septation from wall without indentation, 94.4%) to diagnose MCNs.

CONCLUSION

Four out of 18 MRI features were extracted as reliably important factors to differ hepatic MCNs from septated HC. The combination of these four features in a RF model could achieve satisfactory diagnostic efficacy.

Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis

Objective

To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).

Materials and Methods

Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.

Results

Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.

Conclusion

This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

Role of Cross-sectional Imaging (CT/MRI) in Characterization and Distinguishing Benign from Malignant/Potentially Malignant Cystic Lesions of Pancreas

Objectives:

The aim of the study was to evaluate the accuracy of computed tomography/magnetic resonance imaging (CT/MRI) in characterizing cystic lesions of the pancreas and in differentiating between benign and malignant/potentially malignant lesions.

Material and Methods:

A retrospective study was performed on patients with pancreatic cystic lesions who underwent pre-operative imaging and surgery between October 2004 and April 2017 at a tertiary care teaching hospital. The images were reviewed for specific characteristics and diagnoses recorded independently by two radiologists who were blinded to the histopathological examination (HPE) report. Radiological diagnostic accuracy was assessed with HPE as reference standard.

Results:

A total of 80 patients fulfilled the inclusion criteria (M: F = 27:53). The final HPE diagnoses were solid pseudopapillary neoplasm (32.5%), walled off necrosis/pseudocyst (27.5%), mucinous cystadenoma (15%), serous cystadenoma (11.25%), intraductal papillary mucinous neoplasm (8.75%), mucinous cystadenocarcinoma (2.5%), simple epithelial cyst (1.25%), and unspecified benign cystic lesion (1.25%). Observer1 correctly identified the diagnosis in 73.75% of cases while observer 2 did so in 72.5%. Sensitivity for distinguishing benign versus malignant/potentially malignant lesions was 85.1% for observer 1 and 80.9% for observer 2. On multivariate logistic regression analysis: Solid cystic morphology, presence of mural nodule, and female gender were associated with premalignant/malignant lesions.

Conclusion:

Cross-sectional imaging is a valuable tool for characterization of pancreatic cystic lesions within its limitations.