Prognostic value of CT findings to predict survival outcomes in patients with pancreatic neuroendocrine neoplasms: a single institutional study of 161 patients

Clinical and CT Quantitative Features for Predicting Liver Metastases in Patients with Pancreatic Neuroendocrine Tumors: A Study with Prospective/External Validation

RATIONALE AND OBJECTIVES

We aimed to evaluate clinical characteristics and quantitative CT imaging features for the prediction of liver metastases (LMs) in patients with pancreatic neuroendocrine tumors (PNETs).

METHODS

Patients diagnosed with pathologically confirmed PNETs were included, 133 patients were in the training group, 22 patients in the prospective internal validation group, and 28 patients in the external validation group. Clinical information and quantitative features were collected. The independent variables for predicting LMs were confirmed through the implementation of univariate and multivariate logistic analyses. The diagnostic performance was evaluated by conducting receiver operating characteristic curves for predicting LMs in the training and validation groups.

RESULTS

PNETs with LMs demonstrated significantly larger diameter and lower arterial/portal tumor-parenchymal enhancement ratio, arterial/portal absolute enhancement value (AAE/PAE value) (p < 0.05). After multivariate analyses, A high level of tumor marker (odds ratio (OR): 5.32; 95% CI, 1.54-18.35), maximum diameter larger than 24.6 mm (OR: 7.46; 95% CI, 1.70-32.72), and AAE value ≤ 51 HU (OR: 4.99; 95% CI, 0.93-26.95) were independent positive predictors of LMs in patients with PNETs, with area under curve (AUC) of 0.852 (95%CI, 0.781-0.907). The AUCs for prospective internal and external validation groups were 0.883 (95% CI, 0.686-0.977) and 0.789 (95% CI, 0.602-0.916), respectively.

CONCLUSION

Tumor marker, maximum diameter and absolute enhancement value in arterial phase were independent predictors with good predictive performance for the prediction of LMs in patients with PNETs. Combining clinical and quantitative features may facilitate the attainment of good predictive precision in predicting LMs.

A CT-based diagnostic nomogram and survival analysis for differentiating grade 3 pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas

Objective

To construct a CT-based diagnostic nomogram for distinguishing grade 3 pancreatic neuroendocrine tumors (G3 PNETs) from pancreatic ductal adenocarcinomas (PDACs) and assess their respective survival outcomes.

Methods

Patients diagnosed with G3 PNETs (n = 30) and PDACs (n = 78) through surgery or biopsy from two medical centers were retrospectively identified. Demographic and radiological information, including age, gender, tumor diameter, shape, margin, dilatation of pancreatic duct, and invasive behavior, were carefully collected. A nomogram was established after univariate and multivariate logistic regression analyses. The Kaplan-Meier survival was performed to analyze their survival outcomes.

Results

Factors with a p-value <0.05, including age, CA 19-9, pancreatic duct dilatation, irregular shape, ill-defined margin, pancreatic atrophy, combined pancreatitis, arterial/portal enhancement ratio, were included in the multivariate logistic analysis. The independent predictive factors, including age (OR, 0.91; 95% CI, 0.85-0.98), pancreatic duct dilatation (OR, 0.064; 95% CI, 0.01-0.32), and portal enhancement ratio (OR, 1,178.08; 95% CI, 5.96-232,681.2) were determined to develop a nomogram. The internal calibration curve and decision curve analysis demonstrate that the nomogram exhibits good consistency and discriminative capacity in distinguishing G3 PNETs from PDACs. Patients diagnosed with G3 PNETs exhibited considerably better overall survival outcomes compared to those diagnosed with PDACs (median survival months, 42 vs. 9 months, p < 0.001).

Conclusions

The nomogram model based on age, pancreatic duct dilatation, and portal enhancement ratio demonstrates good accuracy and discriminative ability effectively predicting the probability of G3 PNETs from PDACs. Furthermore, patients with G3 PNETs exhibit better prognosis than PDACs.

Radiologic tumor border can further stratify prognosis in patients with pancreatic neuroendocrine tumor

BACKGROUND AND OBJECTIVES

Pancreatic neuroendocrine tumor (PanNET), although rare in incidence, is increasing in recent years. Several clinicopathologic and molecular factors have been suggested for patient stratification due to the extensive heterogeneity of PanNETs. We aimed to discover the prognostic role of assessing the tumor border of PanNETs with pre-operative computed tomography (CT) images and correlate them with other clinicopathologic factors.

METHODS

The radiologic, macroscopic, and microscopic tumor border of 183 surgically resected PanNET cases was evaluated using preoperative CT images (well defined vs. poorly defined), gross images (expansile vs. infiltrative), and hematoxylin and eosin-stained slides (pushing vs. infiltrative). The clinicopathologic and prognostic significance of the tumor border status was compared with other clinicopathologic factors.

RESULTS

A poorly defined radiologic tumor border was observed in 65 PanNET cases (35.5 %), and were more frequent in male patients (P = 0.031), and tumor with larger size, infiltrative macroscopic growth pattern, infiltrative microscopic tumor border, higher tumor grade, higher pT category, lymph node metastasis, lymphovascular and perineural invasions (all, P < 0.001). Patients with PanNET with a poorly defined radiologic tumor border had significantly worse overall survival (OS) and recurrence-free survival (RFS; both, P < 0.001). Multivariable analysis revealed that PanNET with a poorly defined radiologic border is an independent poor prognostic factor for both OS (P = 0.049) and RFS (P = 0.027).

CONCLUSION

Pre-operative CT-based tumor border evaluation can provide additional information regarding survival and recurrence in patients with PanNET.

Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors

RATIONALE AND OBJECTIVES

To identify CT features for distinguishing grade 1 (G1)/grade 2 (G2) from grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) using different machine learning (ML) methods.

MATERIALS AND METHODS

A total of 147 patients with 155 lesions confirmed by pathology were retrospectively included. Clinical-demographic and radiological CT features was collected. The entire cohort was separated into training and validation groups at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were used to select features. Three ML methods, namely logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) were used to build a differential model. Receiver operating characteristic (ROC) curves and precision-recall curves for each ML method were generated. The area under the curve (AUC), accuracy rate, sensitivity, and specificity were calculated.

RESULTS

G3 PNETs were more likely to present with invasive behaviors and lower enhancement than G1/G2 PNETs. The LR classifier yielded the highest AUC of 0.964 (95% confidence interval [CI]: 0.930, 0.972), with 95.4% accuracy rate, 95.7% sensitivity, and 92.9% specificity, followed by SVM (AUC: 0.957) and KNN (AUC: 0.893) in the training group. In the validation group, the SVM classier reached the highest AUC of 0.952 (95% CI: 0.860, 0.981), with 91.5% accuracy rate, 97.3% sensitivity, and 70% specificity, followed by LR (AUC: 0.949) and KNN (AUC: 0.923).

CONCLUSIONS

The LR and SVM classifiers had the best performance in the training group and validation group, respectively. ML method could be helpful in differentiating between G1/G2 and G3 PNETs.

Prognostic value of CT-based radiomics in grade 1-2 pancreatic neuroendocrine tumors

BACKGROUND

Surgically resected grade 1-2 (G1-2) pancreatic neuroendocrine tumors (PanNETs) exhibit diverse clinical outcomes, highlighting the need for reliable prognostic biomarkers. Our study aimed to develop and validate CT-based radiomics model for predicting postsurgical outcome in patients with G1-2 PanNETs, and to compare its performance with the current clinical staging system.

METHODS

This multicenter retrospective study included patients who underwent dynamic CT and subsequent curative resection for G1-2 PanNETs. A radiomics-based model (R-score) for predicting recurrence-free survival (RFS) was developed from a development set (441 patients from one institution) using least absolute shrinkage and selection operator-Cox regression analysis. A clinical model (C-model) consisting of age and tumor stage according to the 8th American Joint Committee on Cancer staging system was built, and an integrative model combining the C-model and the R-score (CR-model) was developed using multivariable Cox regression analysis. Using an external test set (159 patients from another institution), the models' performance for predicting RFS and overall survival (OS) was evaluated using Harrell's C-index. The incremental value of adding the R-score to the C-model was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

The median follow-up periods were 68.3 and 59.7 months in the development and test sets, respectively. In the development set, 58 patients (13.2%) experienced recurrence and 35 (7.9%) died. In the test set, tumors recurred in 14 patients (8.8%) and 12 (7.5%) died. In the test set, the R-score had a C-index of 0.716 for RFS and 0.674 for OS. Compared with the C-model, the CR-model showed higher C-index (RFS, 0.734 vs. 0.662, p = 0.012; OS, 0.781 vs. 0.675, p = 0.043). CR-model also showed improved classification (NRI, 0.330, p < 0.001) and discrimination (IDI, 0.071, p < 0.001) for prediction of 3-year RFS.

CONCLUSIONS

Our CR-model outperformed the current clinical staging system in prediction of the prognosis for G1-2 PanNETs and added incremental value for predicting postoperative recurrence. The CR-model enables precise identification of high-risk patients, guiding personalized treatment planning to improve outcomes in surgically resected grade 1-2 PanNETs.

Duodenal neuroendocrine neoplasms on enhanced CT: establishing a diagnostic model with duodenal gastrointestinal stromal tumors in the non-ampullary area and analyzing the value of predicting prognosis

OBJECTIVE

To identify CT features and establish a diagnostic model for distinguishing non-ampullary duodenal neuroendocrine neoplasms (dNENs) from non-ampullary duodenal gastrointestinal stromal tumors (dGISTs) and to analyze overall survival outcomes of all dNENs patients.

MATERIALS AND METHODS

This retrospective study included 98 patients with pathologically confirmed dNENs (n = 44) and dGISTs (n = 54). Clinical data and CT characteristics were collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model between non-ampullary dNENs (n = 22) and dGISTs (n = 54). The ROC curve was created to determine diagnostic ability. Cox proportional hazards models were created and Kaplan-Meier survival analyses were performed for survival analysis of dNENs (n = 44).

RESULTS

Three CT features were identified as independent predictors of non-ampullary dNENs, including intraluminal growth pattern (OR 0.450; 95% CI 0.206-0.983), absence of intratumoral vessels (OR 0.207; 95% CI 0.053-0.807) and unenhanced lesion > 40.76 HU (OR 5.720; 95% CI 1.575-20.774). The AUC was 0.866 (95% CI 0.765-0.968), with a sensitivity of 90.91% (95% CI 70.8-98.9%), specificity of 77.78% (95% CI 64.4-88.0%), and total accuracy rate of 81.58%. Lymph node metastases (HR: 21.60), obstructive biliary and/or pancreatic duct dilation (HR: 5.82) and portal lesion enhancement ≤ 99.79 HU (HR: 3.02) were independent prognostic factors related to poor outcomes.

CONCLUSION

We established a diagnostic model to differentiate non-ampullary dNENs from dGISTs. Besides, we found that imaging features on enhanced CT can predict OS of patients with dNENs.

Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study

Background

Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs.

Methods

Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC).

Findings

The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91).

Interpretation

Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies.

Funding

This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).

Prognostic value of tumor-to-parenchymal contrast enhancement ratio on portal venous-phase CT in pancreatic neuroendocrine neoplasms

OBJECTIVES

We aimed to evaluate the prognostic value of tumor-to-parenchymal contrast enhancement ratio on portal venous-phase CT (CER on PVP) and compare its prognostic performance to prevailing grading and staging systems in pancreatic neuroendocrine neoplasms (PanNENs).

METHODS

In this retrospective study, data on 465 patients (development cohort) who underwent upfront curative-intent resection for PanNEN were used to assess the performance of CER on PVP and tumor size measured by CT (CT-Size) in predicting recurrence-free survival (RFS) using Harrell's C-index and to determine their optimal cutoffs to stratify RFS using a multi-way partitioning algorithm. External data on 184 patients (test cohort) were used to validate the performance of CER on PVP in predicting RFS and overall survival (OS) and compare its predictive performance with those of CT-Size, 2019 World Health Organization classification system (WHO), and the 8th American Joint Committee on Cancer staging system (AJCC).

RESULTS

In the test cohort, CER on PVP showed C-indexes of 0.83 (95% confidence interval [CI], 0.74-0.91) and 0.84 (95% CI, 0.73-0.95) for predicting RFS and OS, respectively, which were higher than those for the WHO (C-index: 0.73 for RFS [p = .002] and 0.72 for OS [p = .004]) and AJCC (C-index, 0.67 for RFS [p = .002] and 0.58 for OS [p = .002]). CT-Size obtained C-indexes of 0.71 for RFS and 0.61 for OS.

CONCLUSIONS

CER on PVP showed superior predictive performance on postoperative survival in PanNEN than current grading and staging systems, indicating its potential as a noninvasive preoperative prognostic tool.

KEY POINTS

• In pancreatic neuroendocrine neoplasms, the tumor-to-parenchymal enhancement ratio on portal venous-phase CT (CER on PVP) showed acceptable predictive performance of postoperative outcomes. • CER on PVP showed superior predictive performance of postoperative survival over the current WHO classification and AJCC staging system.

Quantitative analysis of enhanced CT in differentiating well-differentiated pancreatic neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas

OBJECTIVE

To identify quantitative CT features for distinguishing well-differentiated pancreatic neuroendocrine tumors (PNETs) from poorly differentiated pancreatic neuroendocrine carcinomas (PNECs).

MATERIALS AND METHODS

Seventeen patients with PNECs and 131 patients with PNETs confirmed by biopsy or surgery were retrospectively included. General demographic (sex, age) and CT quantitative parameters (arterial/portal absolute enhancement, arterial/portal relative enhancement ratio, arterial/portal enhancement ratio) were collected. Univariate and multivariate logistic regression analyses were performed to confirm independent variables for differentiating PNECs from PNETs. Receiver operating characteristic (ROC) curves for each quantitative parameter were generated to determine their diagnostic ability.

RESULTS

PNECs had a much lower mean arterial/portal absolute enhancement value (19.5 ± 11.0 vs. 78.8 ± 47.2; 28.1 ± 15.8 vs. 77.0 ± 39.4), arterial/portal relative enhancement ratio (0.57 ± 0.36 vs. 2.03 ± 1.31; 0.80 ± 0.52 vs. 1.99 ± 1.13), and arterial/portal enhancement ratio (0.62 ± 0.27 vs. 1.22 ± 0.49; 0.74 ± 0.19 vs. 1.21 ± 0.36) than PNETs (all p < 0.001). After multivariable analysis, arterial absolute enhancement (odds ratio [OR]: 0.96, 95% confidence interval [CI]: 0.93, 0.99) and portal absolute enhancement (OR: 0.96, 95% CI: 0.92, 0.99) were independent factors for differentiating PNECs from PNETs. For each quantitative parameter, arterial lesion enhancement yielded the highest diagnostic performance, with an area under the curve (AUC) of 0.922 (95% CI: 0.867-0.960), followed by portal absolute enhancement.

CONCLUSIONS

Arterial/portal absolute enhancements were independent predictors with good diagnostic accuracy for differentiating between PNETs and PNECs. Quantitative parameters of enhanced CT can distinguish PNECs from PNETs.

KEY POINTS

• PNECs were hypovascular and had a much lower enhanced CT attenuation in both arterial and portal phases than well-differentiated PNETs. • Quantitative parameters derived from enhanced CT can be used to distinguish PNECs from PNETs. • Arterial absolute enhancement and portal absolute enhancement were independent predictive factors for differentiating between PNETs and PNECs.

Preoperative Prediction of G1 and G2/3 Grades in Patients With Nonfunctional Pancreatic Neuroendocrine Tumors Using Multimodality Imaging

OBJECTIVES

We aimed to develop and validate a multimodality radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G).

METHODS

This retrospective study assessed 123 patients with surgically resected, pathologically confirmed NF-pNETs who underwent multidetector computed tomography and MRI scans between December 2012 and May 2020. Radiomic features were extracted from multidetector computed tomography and MRI. Wilcoxon rank-sum test and Max-Relevance and Min-Redundancy tests were used to select the features. The linear discriminative analysis (LDA) was used to construct the four models including a clinical model, MRI radiomics model, computed tomography radiomics model, and mixed radiomics model. The performance of the models was assessed using a training cohort (82 patients) and a validation cohort (41 patients), and decision curve analysis was applied for clinical use.

RESULTS

We successfully constructed 4 models to predict the tumor grade of NF- pNETs. Model 4 combined 6 features of T2-weighted imaging radiomics features and 1 arterial-phase computed tomography radiomics feature, and showed better discrimination in the training cohort (AUC = 0.92) and validation cohort (AUC = 0.85) relative to the other models. In the decision curves, if the threshold probability was 0.07-0.87, the use of the radiomics score to distinguish NF-pNET G1 and G2/3 offered more benefit than did the use of a "treat all patients" or a "treat none" scheme in the training cohort of the MRI radiomics model.

CONCLUSION

The LDA classifier combining multimodality images may be a valuable noninvasive tool for distinguishing NF-pNET grades and avoid unnecessary surgery.

Prognostic factors for relapse in resected gastroenteropancreatic neuroendocrine neoplasms: A systematic review and meta-analysis

BACKGROUND

Gastroenteropancreatic neoplasms (GEP-NENs)can potentially be cured through surgical resection, but only 42-57% achieve 5-year disease-free survival.There is a lack of consensus regarding the factorsassociated withrelapse followingresection ofGEP-NENs.

METHODS

Asystematic review identified studies reporting factors associated with relapse in patients with GEP-NENs following resection of a primary tumour. Meta-analysis was performed to identify the factors prognostic for relapse-free survival (RFS)oroverall survival (OS).

RESULTS

63 studies comprising 13,715 patients were included; 56 studies reported on pancreatic NENs (12,418 patients), 24 reported on patients with grade 1-2 tumours (4,735 patients). Median follow-up was 44.2 months, median RFS was 32 months. Pooling of multivariable analyses of GEP-NENs (all sites and grades) found the following factors predicted worse RFS (all p values < 0.05): vascular resection performed, metastatic disease resected, grade 2 disease, grade 3 disease, tumour size > 20 mm, R1 resection, microvascular invasion, perineural invasion, Ki-67 > 5% and any lymph node positivity. In a subgroup of studies comprising exclusively of grade 1-2 GEP-NENs, R1 resection, perineural invasion, grade 2 disease, any lymph node positivity and tumour size > 20 mm predicted worse RFS (all p values < 0.05). Few OSdata were available for pooling; in univariableanalysis(entire cohort), grade 2 predicted worse OS (p = 0.007), whileR1 resectiondid not (p = 0.14).

CONCLUSIONS

The factors prognostic for worse RFS following resection of a GEP-NEN identified in this meta-analysis could be included in post-curative treatment surveillance clinical guidelines and inform the stratification and inclusion criteria of future adjuvant trials.

Prognostic significance of extracellular volume fraction with equilibrium contrast-enhanced computed tomography for pancreatic neuroendocrine neoplasms

BACKGROUND

/Objectives: Identifying reliable pretreatment imaging biomarkers for pancreatic neuroendocrine neoplasm (PanNEN) is a key imperative. Extracellular volume (ECV) fraction quantified with equilibrium contrast-enhanced CT can be easily integrated into routine examinations. This study aimed to determine whether ECV fraction with equilibrium contrast-enhanced computed tomography (CECT) could predict long-term outcomes in patients with PanNEN.

METHODS

This study was a retrospective observational study of 80 patients pathologically diagnosed with PanNEN at a single institution. ECV fraction of the primary lesion was calculated using region-of-interest measurement within PanNEN and the aorta on unenhanced and equilibrium CECT. The impact of clinical factors and tumor ECV fraction on progression-free survival (PFS) and overall survival (OS) was assessed with univariate and multivariate analyses using Cox proportional hazards models. The correlation between WHO classification and tumor ECV fraction was evaluated using Kendall rank correlation coefficients.

RESULTS

PFS and OS rates were estimated as 93.4% and 94.6%, 78.7% and 86.2%, 78.7% and 77.0%, and 78.7% and 66.6% at 1, 3, 5, and 10 years, respectively. Multivariate analysis revealed that Union for International Cancer Control (UICC) stage (hazard ratio [HR] = 3.95, P = 0.003), WHO classification (HR = 12.27, P = 0.003), and tumor ECV fraction (HR = 11.93, P = 0.039) were independent predictors of PFS. Patient age (HR = 1.11, P < 0.001), UICC stage (HR = 3.14, P = 0.001), and tumor ECV fraction (HR = 5.27, P = 0.024) were independent significant variables for predicting OS. Tumor ECV fraction had a weak inverse relationship with WHO classification (P = 0.045, τ = -0.178).

CONCLUSIONS

ECV fraction determined by equilibrium CECT and UICC stage may predict survival in patients with PanNEN.

Assessment of Malignancy Potential in Intraductal Papillary Mucinous Neoplasms of the Pancreas on MDCT

RATIONALE AND OBJECTIVE

To assess the malignancy potential of intraduct papillary mucinous neoplasms (IPMNs) on multidetector-row computerized tomography according to the 2012 International Consensus Guidelines (ICG).

MATERIALS AND METHODS

This study retrospectively collected IPMNs confirmed by surgery from 2016 to 2019. The imaging findings of IPMNs were analyzed. IPMNs were classified as malignancy in the presence of high-grade dysplasia or invasive carcinoma and began in the presence of low- and intermediate-grade dysplasia.

RESULTS

A total of 207 patients (mean age: 63.7 ± 7.9 years) were included, and the prevalence of malignancy was 28.0% (58 of 207). According to the 2012 ICG, the imaging findings of IPMNs were divided into worrisome features (WFs) and high-risk stigmata (HRS). The malignancy of IPMN with only one WF was relatively low (1.4%, 3 of 207). In multivariate regression analyses, the independent factors of IPMNs were enhanced mural nodule ≥5 mm (odds ratio [OR] = 19.5, 95% confidence interval [CI] 6.8-55.4), abrupt change in the main pancreatic duct caliber with distal pancreatic atrophy (OR = 4.6, 95%CI 1.67-12.71), and thickened enhanced cyst walls (OR = 2.9, 95%CI 1.1-8.2). When the presence of more than two WFs or HRS (score ≥ 3) was regarded as indicating the malignancy potential of IPMNs on multidetector-row computerized tomography, the sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were 89.7%, 75.8%, 79.7%, 59.1%, and 95.0%, respectively.

CONCLUSION

According to the ICG in 2012, patients with IPMNs with only one WF have a low risk for malignancy, and the presence of at least two WFs or any HRS (score ≥3) suggests malignant IPMNs.

Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs

PURPOSE

We aimed to explore the relationship between the magnetic resonance imaging (MRI) radiomic score (rad-score) and the grades of non-functioning pancreatic neuroendocrine tumors (NF-pNETs) and evaluate the potential of the calculated MRI rad-score to differentiate grade 1 from grade 2/3 NF-pNETs.

METHODS

This retrospective study assessed 157 patients with surgically resected, pathologically confirmed NF-pNETs who underwent magnetic resonance scans from November 2012 to December 2019. Radiomic features were extracted from arterial and portal venous MRI. The least absolute shrinkage and selection operator method were used to select the features. Multivariate logistic regression models were used to analyze the association between the MRI rad-score and NF-pNET grades. The MRI rad-score performance was assessed based on its discriminative ability and clinical usefulness.

RESULTS

The MRI rad-score, which consisted of seven selected features, was significantly associated with the NF-pNET grades. Every 1-point increase in the rad-score was associated with a 35% increased risk of grade 2/3 disease. The score also showed high accuracy (area under the curve = 0.775). The best cut-off point for maximal sensitivity and specificity was at 0.41. In the decision curves, when the threshold probability was higher than 0.3, the rad-score used in this study to distinguish grades 1 and 2/3 NF-pNETs offered more benefits than the use of a treat-all-patients or a treat-none scheme.

CONCLUSIONS

The MRI rad-score showed a significant association with the grades of NF-pNETs. Thus, it may be used as a valuable non-invasive tool for differential NF-pNET grading.

Pancreatic Neuroendocrine Neoplasm Invading the Entire Main Pancreatic Duct Diagnosed by a Preoperative Endoscopic Biopsy

A 78-year-old man was referred to our hospital for a detailed examination of a pancreatic tumor that filled the main pancreatic duct (MPD). The histological diagnosis of the endoscopic biopsy specimen was neuroendocrine tumor (NET) G3. The patient subsequently underwent total pancreatectomy. The histological diagnosis of the surgical specimen was also NET G3. This is the first report of a NET that occupied the MPD and was diagnosed by a preoperative endoscopic biopsy through the papilla of Vater. This case is a good example of a histopathological diagnostic method for pancreatic tumors invading the entire MPD.

CT-Based Radiomics Score for Distinguishing Between Grade 1 and Grade 2 Nonfunctioning Pancreatic Neuroendocrine Tumors

OBJECTIVE. The objective of our study was to explore the relationship between a CT-based radiomics score and grade of nonfunctioning pancreatic neuroendocrine tumors (PNETs) and to evaluate the ability of a calculated CT radiomics score to distinguish between grade 1 and grade 2 nonfunctioning PNETs. MATERIALS AND METHODS. This retrospective study assessed 102 patients with surgically resected, pathologically confirmed nonfunctioning PNETs who underwent MDCT from January 2014 to December 2017. Radiomic methods were used to extract features from portal venous phase CT scans, and the least absolute shrinkage and selection operator (LASSO) method was used to select the features. Multivariate logistic regression models were used to analyze the association between the CT radiomics score and nonfunctioning PNET grades. The performance of the CT radiomics score was assessed on the basis of its discriminative ability and clinical usefulness. RESULTS. The CT radiomics score, which consisted of four selected features, was significantly associated with nonfunctioning PNET grades. Every 1-point increase in radiomics score was associated with a 57% increased risk of grade 2 disease. The score also showed high accuracy (AUC = 0.86 for all PNETs; AUC = 0.81 for PNETs ≤ 2 cm). The best cutoff point for maximal sensitivity and specificity was a CT radiomics score of -0.134. Decision curve analysis showed that the CT radiomics score is clinically useful. CONCLUSION. The CT radiomics score shows a significant association with the grade of nonfunctioning PNETs and provides a potentially valuable noninvasive tool for distinguishing between different grades of nonfunctioning PNET, especially among patients with tumors 2 cm or smaller.

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PURPOSE

To compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).

MATERIALS AND METHODS

Patients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.

RESULTS

Thirty-seven patients (21 men, 16 women; mean age, 56±13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60±46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70±51 [SD] mm [range: 18 - 196mm] vs. 42±24 [SD] mm [range: 8 - 94mm], respectively; P=0.039), with more tumor necrosis (75% vs. 33%, respectively; P=0.030) and lower attenuation on precontrast (30±4 [SD] HU [range: 25-39 HU] vs. 37±6 [SD] [range: 25-45 HU], respectively; P=0.002) and on portal venous phase CT images (75±18 [SD] HU [range: 43 - 108 HU] vs. 92±19 [SD] HU [range: 46 - 117 HU], respectively; P=0.014). Hemorrhagic content on MRI was only observed in NEC (P=0.007). The mean ADC value was lower in NEC ([1.1±0.1 (SD)]×10^-3 mm²/s [range: (0.91 - 1.3)×10^-3 mm²/s] vs. [1.4±0.2 (SD)]×10^-3 mm²/s [range: (1.1 - 1.6)×10^-3 mm²/s]; P=0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7±0.2 [SD] [range: 4.2-5.1] vs. 4.5±0.4 [SD] [range: 3.7-4.9]; P=0.023).

CONCLUSION

Pancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.

Grade 3 Pancreatic Neuroendocrine Tumors on MDCT: Establishing a Diagnostic Model and Comparing Survival Against Pancreatic Ductal Adenocarcinoma

OBJECTIVE. The purpose of this study is to establish a diagnostic model for differentiating grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) from pancreatic ductal adenocarcinomas (PDACs) and to analyze survival outcomes. MATERIALS AND METHODS. Twenty patients with G3 PNETs and 58 patients with PDACs confirmed by surgery or biopsy were retrospectively included. Demographic and radiologic information was collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model. An ROC curve was created to determine diagnostic ability. Kaplan-Meier survival analysis was performed. RESULTS. Patients with G3 PNETs were more likely to present with normal carbohydrate antigen (CA) 19-9 levels, normal pancreatic ducts, and round tumors with well-defined margins and higher portal enhancement ratios than were patients with PDAC (p < 0.05). After multivariate analysis, a normal CA 19-9 level (odds ratio, 0.0125; 95% CI, 0.0008-0.2036), round tumor shape (odds ratio, 0.0143; 95% CI, 0.0004-0.5461), and pancreatic duct dilation of 4 mm or less (odds ratio, 17.9804; 95% CI, 1.0098-320.1711) were independent predictors of G3 PNETs. The AUC of the ROC curve was 0.916, and sensitivity and specificity were 90.0% and 81.0%, respectively. Furthermore, patients with G3 PNETs had better overall survival than patients with PDACs. Among patients in the G3 PNET subgroup, patients with liver or lymph node metastases had worse overall survival than patients without metastases. CONCLUSION. A diagnostic model was established to differentiate G3 PNETs from PDACs. A normal CA 19-9 level, round tumor shape, and pancreatic duct dilation of 4 mm or less were factors that were strongly predictive of G3 PNET.

Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors

BACKGROUND

Endoscopic ultrasound-guided fine-needle aspiration is associated with the accurate determination of tumor grade. However, because it is an invasive procedure there is a need to explore alternative noninvasive procedures.

PURPOSE

To develop and validate a noncontrast radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G).

STUDY TYPE

Retrospective, single-center study.

SUBJECTS

Patients with pathologically confirmed PNETs (139) were included.

FIELD STRENGTH/SEQUENCE

3T/breath-hold single-shot fast-spin echo T₂ -weighted sequence and unenhanced and dynamic contrast-enhanced T₁ -weighted fat-suppressed sequences.

ASSESSMENT

Tumor features on contrast MR images were evaluated by three board-certified abdominal radiologists.

STATISTICAL TESTS

Multivariable logistic regression analysis was used to develop the clinical model. The least absolute shrinkage and selection operator method and linear discriminative analysis (LDA) were used to select the features and to construct a radiomics model. The performance of the models was assessed using the training cohort (97 patients) and the validation cohort (42 patients), and decision curve analysis (DCA) was applied for clinical use.

RESULTS

The clinical model included 14 imaging features, and the corresponding area under the curve (AUC) was 0.769 (95% confidence interval [CI], 0.675-0.863) in the training cohort and 0.729 (95% CI, 0.568-0.890) in the validation cohort. The LDA included 14 selected radiomics features that showed good discrimination-in the training cohort (AUC, 0.851; 95% CI, 0.758-0.916) and the validation cohort (AUC, 0.736; 95% CI, 0.518-0.874). In the decision curves, if the threshold probability was 0.17-0.84, using the radiomics score to distinguish NF-pNET G1 and G2/3, offered more benefit than did the use of a treat-all-patients or treat-none scheme.

DATA CONCLUSION

The developed radiomics model using noncontrast MRI could help differentiate G1 and G2/3 tumors, to make the clinical decision, and screen pNETs grade.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1124-1136.

The prognostic value of multidetector CT features in predicting overall survival outcomes in patients with pancreatic neuroendocrine tumors

PURPOSE

To assess the prognostic value of multidetector CT in predicting overall survival outcomes in patients with pancreatic neuroendocrine tumors (PNETs).

METHOD

Seventy-one patients pathologically diagnosed with PNETs were retrospectively included. The clinical and imaging information was evaluated by two radiologists. The difference between well-differentiated and poorly differentiated PNETs was analyzed. Cox proportional hazards models were created to determine the risk factors for overall survival. Kaplan-Meier survival analyses with log-rank tests were used among different subgroups of patients with PNETs.

RESULTS

In the whole cohort, the median survival was 36 months, and the 5-year survival rate was 84.8 %. Patients with poorly differentiated PNETs were more likely to present with symptoms, abnormal tumor markers, larger diameters, irregular shapes, ill-defined margins, invasion into nearby tissues, liver and lymph node metastases, and lower enhancement ratio than those with well-differentiated PNETs (P < 0.05). In the multivariate analysis, lymph node metastases (hazard ratio: 21.52, P = 0.009) and a portal enhancement ratio less than 1.02 (hazard ratio: 30.89, P = 0.024) were significant factors for overall survival. Overall survival decreased with an ill-defined margin, irregular shape, poor differentiation, grade 3 disease, nonfunctional status, abnormal tumor marker levels, invasion into nearby tissues, lymph node and liver metastases, and lower enhancement ratio (log-rank P < 0.05).

CONCLUSIONS

Poorly differentiated PNETs were more aggressiveness than well-differentiated PNETs. Lymph node metastases and a portal enhancement ratio < 1.02 were independent prognostic factors for worse overall survival outcomes in patients with PNETs.

Value of computed tomography evaluation in pathologic classification and prognosis prediction of gastric neuroendocrine tumors

Background

The study aims to investigate the correlation of CT characteristics with pathological classifications and the prognostic value of CT features in patients with gastric neuroendocrine neoplasms (g-NENs).

Methods

Ninety-one cases of pathologically diagnosed g-NENs, including 15 cases of well-differentiated neuroendocrine tumors (NETs) (G1 and G2) and 76 cases of poor-differentiated neuroendocrine carcinomas (NECs) (G3 and MANEC) were retrospectively studied. All cases were included in correlation analysis of CT characteristics with pathologic grades. Among them, 76 patients who had fulfilled follow-up data were included for overall survival (OS) and disease-free survival (DFS) analysis.

Results

CT characteristics that favor poor differentiation include tumor location (fundus and cardia), larger tumor size (>3.0 cm), infiltrative growth, unclear tumor margin, serosa involvement, ulceration and lymph node metastasis (P<0.05). Most variables had sensitivities >80% and specificities >60% to distinguish NECs from NETs. Through log-rank analysis, it was revealed that serosa involvement, cystic degeneration, necrosis, heterogeneous enhancement and lymph node metastasis led to worse DFS and OS for patients with g-NENs (P<0.05). COX regression analysis showed that serosa involvement and lymph node metastasis were independent risk factor for DFS and OS, respectively, despite of grading, staging and therapeutic choices (P<0.05). Moreover, high Ki-67 index (>55%) in G3 g-NENs is in correlation with serosa involvement and lymph node metastasis; accordingly, patients with higher Ki-67 index had worse 1-year DFS (61.7% vs. 92.3%; P<0.05).

Conclusions

CT characteristics can be useful discriminators and prognostic factors for g-NENs and may help identify G3 g-NEC from G3 g-NEN by revealing its poor differentiation and high invasive potential.

Prognostic and predictive factors on overall survival and surgical outcomes in pancreatic neuroendocrine tumors: recent advances and controversies

Introduction: Recent advances in diagnostic modalities and therapeutic agents have raised the importance of prognostic factors in predicting overall survival, as well as predictive factors for surgical outcomes, in tailoring therapeutic strategies of patients with pancreatic neuroendocrine neoplasms (panNENs).Areas covered: Numerous recent studies of panNEN patients report the prognostic values of a number of clinically related factors (clinical, laboratory, imaging, treatment-related factors), pathological factors (histological, classification, grading) and molecular factors on long-term survival. In addition, an increasing number of studies showed the usefulness of various factors, specifically biomarkers and molecular makers, in predicting recurrence and mortality related to surgical treatment. Recent findings (from the last 3 years) in each of these areas, as well as recent controversies, are reviewed.Expert commentary: The clinical importance of prognostic and predictive factors for panNENs is markedly increased for both overall outcome and post resection, as a result of recent advances in all aspects of the diagnosis, management and treatment of panNENs. Despite the proven prognostic utility of routinely used tumor grading/classification and staging systems, further studies are required to establish these novel prognostic factors to support their routine clinical use.

Imaging of pancreatic neuroendocrine tumors: recent advances, current status, and controversies

INTRODUCTION

Recently, there have been a number of advances in imaging pancreatic neuroendocrine tumors (panNETs), as well as other neuroendocrine tumors (NETs), which have had a profound effect on the management and treatment of these patients, but in some cases are also associated with controversies. Areas covered: These advances are the result of numerous studies attempting to better define the roles of both cross-sectional imaging, endoscopic ultrasound, with or without fine-needle aspiration, and molecular imaging in both sporadic and inherited panNET syndromes; the increased attempt to develop imaging parameters that correlate with tumor classification or have prognostic value; the rapidly increasing use of molecular imaging in these tumors and the attempt to develop imaging parameters that correlate with treatment/outcome results. Each of these areas and the associated controversies are reviewed. Expert commentary: There have been numerous advances in all aspects of the imaging of panNETs, as well as other NETs, in the last few years. The advances are leading to expanded roles of imaging in the management of these patients and the results being seen in panNETs/GI-NETs with these newer techniques are already being used in more common tumors.

A comparison of enhancement patterns on dynamic enhanced CT and survival between patients with pancreatic neuroendocrine tumors with and without intratumoral fibrosis

PURPOSE

To compare CT findings and survival between patients with pancreatic neuroendocrine tumors (pNETs) with and without fibrosis.

METHODS

Forty-five pNET patients with intratumoral fibrosis (group A) were matched for age, gender, and tumor size and grade with 45 pNET patients without (group B), and CT images were retrospectively reviewed. Hounsfield units (HUs) of tumors in unenhanced, arterial and portal phases, HU ratio (tumor to normal parenchyma) in each phase, enhancement patterns, visible enhancement pattern changes, and survival were compared.

RESULTS

Group A showed progressive enhancement patterns, while group B showed early enhancement and wash-out patterns (p < 0.05). HUs of tumors and HU ratio in the unenhanced phase were significantly higher in group A than group B (p ≤ 0.024), whereas those in the arterial phase were significantly lower in group A than group B (p ≤ 0.003). Peripheral to full or peripheral to peripheral enhancement change was more frequent in group A, while full to full enhancement change was more frequent in group B (p < 0.05). Group A showed significantly lower overall survival than group B (p = 0.029).

CONCLUSIONS

pNETs with fibrosis showed a progressive enhancement pattern and worse overall survival than pNETs without, which showed an early enhancement and wash-out pattern.

Computed Tomography Features Predictive of Lymph Node Involvement in Patients With a Nonfunctioning Pancreatic Neuroendocrine Tumor

OBJECTIVES

This study aims to identify the computed tomography (CT) features that may differentiate nonfunctioning pancreatic neuroendocrine tumors (NF-PanNETs) with lymph node (LN) metastasis from NF-PanNETs without lymph node metastasis.

METHODS

We retrospectively analyzed 166 NF-PanNETs in 166 patients who had undergone surgical resection (median age, 53). Two radiologists evaluated the qualitative and quantitative CT findings. Through univariate and multivariate logistic regression analyses, we determined independent significant findings for differentiating NF-PanNETs with LN metastasis from NF-PanNETs without LN metastasis. Recurrence-free survival (RFS) and overall survival (OS) were compared between the 2 groups using Kaplan-Meier analysis and log-rank testing.

RESULTS

Of the 166 NF-PanNETs, 24 (14.5%) tumors demonstrated LN metastasis. Three CT findings, radiologic LN enlargement (adjusted odds ratio [OR], 11.76; P = 0.001), liver metastasis (OR, 10.31; P = 0.027), and portal enhancement ratio of <1.238 (OR, 3.58; P = 0.033), were independently significant for differentiating NF-PanNETs with LN metastasis from NF-PanNETs without LN metastasis. Tumor size greater than 2 cm also showed a statistically marginal significance (OR, 8.47; P = 0.050). The median RFS and OS in NF-PanNETs with LN metastasis were significantly shorter than NF-PanNETs without LN metastasis (23.7 months vs 33.2 months, P < 0.001; 33.7 months vs 54.8 months, P < 0.001).

CONCLUSIONS

Four CT findings can be useful to differentiate NF-PanNETs with LN metastasis and NF-PanNETs without LN metastasis.

Prediction of pancreatic neuroendocrine tumour grade with MR imaging features: added value of diffusion-weighted imaging

OBJECTIVES

To evaluate the value of MR imaging including diffusion-weighted imaging (DWI) for the grading of pancreatic neuroendocrine tumours (pNET).

MATERIAL AND METHODS

Between 2006 and 2014, all resected pNETs with preoperative MR imaging including DWI were included. Tumour grading was based on the 2010 WHO classification. MR imaging features included size, T1-w, and T2-w signal intensity, enhancement pattern, apparent (ADC) and true diffusion (D) coefficients.

RESULTS

One hundred and eight pNETs (mean 40 ± 33 mm) were evaluated in 94 patients (48 women, 51 %, mean age 52 ± 12). Fifty-five (51 %), 42 (39 %), and 11 (10 %) tumours were given the following grades (G): G1, G2, and G3. Mean ADC and D values were significantly lower as grade increased (ADC: 2.13 ± 0.70, 1.78 ± 0.72, and 0.86 ± 0.22 10^-3 mm²/s, and D: 1.92 ± 0.70, 1.75 ± 0.74, and 0.82 ± 0.19 10^-3 mm²/s G1, G2, and G3, all p < 0.001). A higher grade was associated with larger sized tumours (p < 0.001). The AUROC of ADC and D to differentiate G3 and G1-2 were 0.96 ± 0.02 and 0.95 ± 0.02. Optimal cut-off values for the identification of G3 were 1.19 10^-3 mm²/s for ADC (sensitivity 100 %, specificity 92 %) and 1.04 10^-3 mm²/s for D (sensitivity 82 %, specificity 92 %).

CONCLUSION

Morphological/functional MRI features of pNETS depend on tumour grade. DWI is useful for the identification of high-grade tumours.

KEY POINTS

• Morphological and functional MRI features of pNETs depend on tumour grade. • Their combination has a high predictive value for grade. • All pNETs should be explored by MR imaging including DWI. • DWI is helpful for identification of high-grade and poorly-differentiated tumours.