Application of MSCT characteristic nomogram model in predicting invasion of pancreatic solid pseudopapillary neoplasms

Comparison between solid pseudopapillary neoplasms of the pancreas and pancreatic ductal adenocarcinoma with cystic changes using computed tomography

BACKGROUND

Solid pseudopapillary neoplasms of the pancreas (SPN) share similar imaging findings with pancreatic ductal adenocarcinoma with cystic changes (PDAC with cystic changes), which may result in unnecessary surgery.

AIM

To investigate the value of computed tomography (CT) in differentiation of SPN from PDAC with cystic changes.

METHODS

This study retrospectively analyzed the clinical and imaging findings of 32 patients diagnosed with SPN and 14 patients diagnosed with PDAC exhibiting cystic changes, confirmed through pathological diagnosis. Quantitative and qualitative analysis was performed, including assessment of age, sex, tumor size, shape, margin, density, enhancement pattern, CT values of tumors, CT contrast enhancement ratios, “floating cloud sign,” calcification, main pancreatic duct dilatation, pancreatic atrophy, and peripancreatic invasion or distal metastasis. Multivariate logistic regression analysis was used to identify relevant features to differentiate between SPN and PDAC with cystic changes, and receiver operating characteristic curves were obtained to evaluate the diagnostic performance of each variable and their combination.

RESULTS

When compared to PDAC with cystic changes, SPN had a lower age (32 years vs 64 years, P < 0.05) and a slightly larger size (5.41 cm vs 3.90 cm, P < 0.05). SPN had a higher frequency of “floating cloud sign” and peripancreatic invasion or distal metastasis than PDAC with cystic changes (both P < 0.05). No significant difference was found with respect to sex, tumor location, shape, margin, density, main pancreatic duct dilatation, calcification, pancreatic atrophy, enhancement pattern, CT values of tumors, or CT contrast enhancement ratios between the two groups (all P > 0.05). The area under the receiver operating characteristic curve of the combination was 0.833 (95% confidence interval: 0.708-0.957) with 78.6% sensitivity, 81.3% specificity, and 80.4% accuracy in differentiation of SPN from PDAC with cystic changes.

CONCLUSION

A larger tumor size, “floating cloud sign,” and peripancreatic invasion or distal metastasis are useful CT imaging features that are more common in SPN and may help discriminate SPN from PDAC with cystic changes.

Solid pseudopapillary neoplasm (SPN) of the pancreas: current understanding on its malignant potential and management

Solid pseudopapillary neoplasms (SPN) of the pancreas are presently recognized as low-grade malignant tumors that are frequently observed in young females. This tumor has a low incidence and is associated with an excellent prognosis following surgical resection. Typical SPNs primarily affect the pancreas and tend to have moderate or asymptomatic manifestations. Based on retrospective research, it is anticipated that patients with SPN can achieve disease-free survival, even in cases when metastasis is detected during inspection. However, the incidence of malignant SPN has been consistently underestimated, as evidenced by recent research findings. Malignancy of SPN primarily encompasses invasion and infiltration, metastasis, and recurrence after R0 resection. Imaging technologies such as Ultrasound, Computed Tomography, Magnetic Resonance Imaging, and Position Emission Tomography are capable of preliminarily identifying malignant SPN, which is primarily based on its invasive clinical features. Research on risk factors of malignant SPN revealed that larger tumor size, Ki-67 index, and several other parameters had significant correlations with invasive tumor behavior. Pathologic features of malignant SPNs overlay other pancreatic tumors, nevertheless they can provide valuable assistance in the process of diagnosis. Several confirmed specific pathologic biomarkers are related to its cellular origin, characteristic gene mutation, and cell proliferation. Considering the invasiveness of malignant SPN, it is imperative to enhance the comprehensiveness of its therapy. Tumor resection remains a suggested course of action in line with typical SPN, and additional lymph node dissection is seen as reasonable. Compared to benign SPNs, malignant SPNs have worse prognosis, underscoring the necessity of early identification and treatment in comprehensive medical centers to get improved clinical outcomes.

Development of a nomogram for predicting the high-risk groups of solid-pseudopapillary neoplasms of the pancreas

Background

Solid pseudopapillary neoplasms (SPNs) of the pancreas are indolent rare tumors with malignant potential. The risk factors associated with the malignant behavior of SPNs are still unclear.

Methods

A retrospective analysis of patients with SPNs who underwent surgical treatment in the First Hospital of Jilin University from January 2010 to January 2022 was conducted. The clinical baseline data, pathology, imaging, and laboratory indicators of the patients were analyzed by univariate and multivariate logistic regression to identify the independent risk factors associated with the high-risk groups, and a predictive model was established in the form of a nomogram.

Results

In multivariate analysis, clinical symptoms (P < 0.001), unclear tumor margins (P = 0.001), incomplete tumor capsules (P = 0.005), maximum tumor diameters ≥ 7.2 cm (P = 0.003), and prognostic nutritional index values < 47.45 (P = 0.007) were independent risk factor for SPNs with high-risk groups. A nomogram model was successfully established to predict high-risk groups of SPNs. The area under the receiver operating characteristic curve was 0.856. The calibration prediction curve was in good agreement with the standard curve.

Conclusion

The nomogram model based on clinical symptoms, inflammatory markers, and imaging features had a high application value in the preoperative prediction of the high-risk groups of SPNs. A novel nomogram of the affiliated hospital of Jilin University-SPNs risk model was proposed for routine application to guide the patient counseling in clinical practice.

Preoperative prediction of invasive behavior of pancreatic solid pseudopapillary neoplasm by MRI-based multiparametric radiomics models

OBJECTIVE

A log-combined model was developed to predict the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN) based on clinical and radiomic features extracted from multiparametric magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A total of 111 patients with pathologically confirmed pSPN who underwent preoperative plain and contrast-enhanced MRI were included, and divided into an invasive group (n = 34) and non-invasive group (n = 77). Clinical features and laboratory data related to pSPN invasive behavior were analyzed. Regions of interest were delineated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) to extract radiomic features. Correlation analysis was performed for these features, followed by L1_based feature selection (C = 0.15). A logistic regression algorithm was used to construct models based on each of the four sequences and a log-combined model was used to integrate the sequences. A receiver operating characteristic (ROC) curve was plotted to evaluate the model performance, and the Brier score was used to assess the overall accuracy of the model predictions.

RESULTS

The area under the ROC curve was 0.68, 0.73, 0.71, and 0.49 for Log-T1WI, Log-T2WI, Log-DWI, and Log-CE models, respectively, and 0.81 for the log-combined model. The accuracy, precision, sensitivity, and specificity of the log-combined model were 0.77, 0.88, 0.75, and 0.78, respectively. The best performance was obtained with the log-combined model with a Brier score of 0.18. Tumor location was identified as a significant clinical feature in comparison between the two groups (p < 0.05), and invasive pSPN was more frequent in the tail of the pancreas.

CONCLUSION

The log-combined model based on multiparametric MRI and clinical features can be used as a non-invasive diagnostic tool for preoperative prediction of pSPN invasive behavior and to facilitate the development of individualized treatment strategies and monitoring management plans.