Dynamic computed tomography is useful for prediction of pathological grade in pancreatic neuroendocrine neoplasm

Development and validation of a CT-based nomogram to preoperative prediction of pancreatic neuroendocrine tumors (pNETs) grade

BACKGROUND/PURPOSE

It is challenging to determine the pancreatic neuroendocrine tumors (pNETs) malignancy grade noninvasively. We aim to establish a CT - based diagnostic nomogram to predict the tumor grade of pNETs.

METHODS

The patients with pathologically confirmed pNETs were recruited in two centers between January 2009 and November 2020. PNETs were subdivided into three grades according to the 2017 World Health Organization classification: low-grade G1 NETs, intermediate-grade G2 NETs, and high-grade G3 NETs. The features on the CT images were carefully evaluated. To build the nomogram, multivariable logistic regression analysis was performed on the imaging features selected by LASSO to generate a combined indicator for estimating the tumor grade.

RESULTS

A total of 162 pNETs (training set n = 114, internal validation set n = 21, external validation set, n = 48) were admitted, including 73 (45.1%) G1 and 89 (54.9%) G2/3. A nomogram comprising the tumor margin, tumor size, neuroendocrine symptoms and the enhanced ratio on portal vein phase images of tumor was established to predict the malignancy grade of pNETs. The mean AUC for the nomogram was 0.848 (95% CI, 0.918-0.953). Application of the developed nomogram in the internal validation dataset still yielded good discrimination (AUC, 0.835; 95% CI, 0.915-0.954). The externally validated nomogram yielded a slightly lower AUC of 0.770 (95% CI, 0.776-0.789).

CONCLUSIONS

The nomogram model demonstrated good performance in preoperatively predicting the malignancy grade of pNETs, and can provide clinicians with a simple, practical, and non-invasive tool for personalized management of pNETs patients.

Preoperative prediction of pancreatic neuroendocrine tumors grade based on computed tomography, magnetic resonance imaging and endoscopic ultrasonography

PURPOSE

To establish a preoperative prediction model for pathological grade of PanNETs based on computed tomography (CT), magnetic resonance imaging (MRI) and endoscopic ultrasonography (EUS).

METHODS

Clinical data of 58 patients with pathologically confirmed PanNETs were included in this retrospectively study and they were divided into grade 1 and grade 2/3. CT, MRI and EUS images were collected within one week before surgery. A clinical predictive model based on the independent clinical risk factors and significant radiological features was established. The area under receiver operating characteristic curve (AUC) was performed to assess the model.

RESULTS

Gender, pancreatic duct dilatation (PDD) and portal enhancement ratio (PER) were the independent predictors for PanNETs grading (P < 0.05). PanNETs grade 1 and grade 2/3 had statistical difference in elastography score (P = 0.001). The combination of gender, PDD and PER had better predictive efficiency than each of these three predictors alone, with a high AUC of 0.925. The elastography score also achieved an AUC of 0.838.

CONCLUSION

We proposed a comprehensive model based on preoperative CT, MRI and EUS to predict grade 1 and grade 2/3 of PanNETs and better informs clinicians on individualized diagnosis and treatment of patients with PanNETs.

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.

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.

A nomogram to preoperatively predict the aggressiveness of non-functional pancreatic neuroendocrine tumors based on CT features

OBJECTIVES

To develop a nomogram to predict the aggressiveness of non-functional pancreatic neuroendocrine tumors (NF-pNETs) based on preoperative computed tomography (CT) features.

METHODS

This study included 176 patients undergoing radical resection for NF-pNETs. These patients were randomly divided into the training (n = 123) and validation sets (n = 53). A nomogram was developed based on preoperative predictors of aggressiveness of the NF-pNETs which were identified by univariable and multivariable logistic regression analysis. The aggressiveness of NF-pNETs was defined as a composite measure including G3 grading, N+, distant metastases, and/ or disease recurrence.

RESULTS

Altogether, the number of patients with highly aggressive NF-pNETs was 37 (30.08 %) and 15 (28.30 %) in the training and validation sets, respectively. Multivariable logistic regression analysis identified that tumor size, biliopancreatic duct dilatation, lymphadenopathy, and enhancement pattern were preoperative predictors of aggressiveness. Those variables were used to develop a nomogram with good concordance statistics of 0.89 and 0.86 for predicting aggressiveness in the training and validation sets, respectively. With a nomogram score of 59, patients with NF-pNETs were divided into low-aggressive and high-aggressive groups. The high-aggressive group had decreased overall survival (OS) and disease-free survival (DFS). Moreover, the nomogram showed good performance in predicting OS and DFS at 3, 5, and 10 years.

CONCLUSION

The nomogram integrating CT features helped preoperatively predict the aggressiveness of NF-pNETs and could potentially facilitate clinical decision-making.

Pancreatic neuroendocrine tumor: prediction of tumor grades by radiomics models based on ultrasound images

OBJECTIVE

We aimed to investigate whether the radiomics analysis based on B-mode ultrasound (BMUS) images could predict histopathological tumor grades in pancreatic neuroendocrine tumors (pNETs).

METHODS

A total of 64 patients with surgery and histopathologically confirmed pNETs were retrospectively included (34 male and 30 female, mean age 52.4 ± 12.2 years). Patients were divided into training cohort (n = 44) and validation cohort (n = 20). All pNETs were classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity according to WHO 2017 criteria. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator were used for feature selection. Receiver operating characteristic curve analysis was used to evaluate the model performance.

RESULTS

Finally, 18 G1 pNETs, 35 G2 pNETs, and 11 G3 pNETs patients were included. The radiomic score derived from BMUS images to predict G2/G3 from G1 displayed a good performance with an area under the receiver operating characteristic curve of 0.844 in the training cohort, and 0.833 in the testing cohort. The radiomic score achieved an accuracy of 81.8% in the training cohort and 80.0% in the testing cohort, a sensitivity of 0.750 and 0.786, a specificity of 0.833 and 0.833 in the training/testing cohorts. Clinical benefit of the score also exhibited superior usefulness of the radiomic score, as shown by the decision curve analysis.

CONCLUSIONS

Radiomic data constructed from BMUS images have the potential for predicting histopathological tumor grades in patients with pNETs.

ADVANCES IN KNOWLEDGE

The radiomic model constructed from BMUS images has the potential for predicting histopathological tumor grades and Ki-67 proliferation indexes in patients with pNETs.

Improving Tumor Classification by Reusing Self-predicted Segmentation of Medical Images as Guiding Knowledge

Differential diagnosis of tumors is important for computer-aided diagnosis. In computer-aided diagnosis systems, expert knowledge of lesion segmentation masks is limited as it is only used during preprocessing or as supervision to guide feature extraction. To improve the utilization of lesion segmentation masks, this study proposes a simple and effective multitask learning network that improves medical image classification using self-predicted segmentation as guiding knowledge; we call this network RS 2-net. In RS 2-net, the predicted segmentation probability map obtained from the initial segmentation inference is added to the original image to form a new input, which is then reinput to the network for the final classification inference. We validated the proposed RS 2-net using three datasets: the pNENs-Grade dataset, which tested the prediction of pancreatic neuroendocrine neoplasm grading, and the HCC-MVI dataset, which tested the prediction of microvascular invasion of hepatocellular carcinoma, and ISIC 2017 public skin lesion dataset. The experimental results indicate that the proposed strategy of reusing self-predicted segmentation is effective, and RS 2-net outperforms other popular networks and existing state-of-the-art studies. Interpretive analytics based on feature visualization demonstrates that the improved classification performance of our reuse strategy is due to the semantic information that can be acquired in advance in a shallow network.

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.

Tumour Heterogeneity and the Consequent Practical Challenges in the Management of Gastroenteropancreatic Neuroendocrine Neoplasms

Tumour heterogeneity is a common phenomenon in neuroendocrine neoplasms (NENs) and a significant cause of treatment failure and disease progression. Genetic and epigenetic instability, along with proliferation of cancer stem cells and alterations in the tumour microenvironment, manifest as intra-tumoural variability in tumour biology in primary tumours and metastases. This may change over time, especially under selective pressure during treatment. The gastroenteropancreatic (GEP) tract is the most common site for NENs, and their diagnosis and treatment depends on the specific characteristics of the disease, in particular proliferation activity, expression of somatostatin receptors and grading. Somatostatin receptor expression has a major role in the diagnosis and treatment of GEP-NENs, while Ki-67 is also a valuable prognostic marker. Intra- and inter-tumour heterogeneity in GEP-NENS, however, may lead to inaccurate assessment of the disease and affect the reliability of the available diagnostic, prognostic and predictive tests. In this review, we summarise the current available evidence of the impact of tumour heterogeneity on tumour diagnosis and treatment of GEP-NENs. Understanding and accurately measuring tumour heterogeneity could better inform clinical decision making in NENs.

Prediction of Pathological Grades of Pancreatic Neuroendocrine Tumors Based on Dynamic Contrast-Enhanced Ultrasound Quantitative Analysis

Objective: To investigate whether the dynamic contrast-enhanced ultrasound (DCE-US) analysis and quantitative parameters could be helpful for predicting histopathologic grades of pancreatic neuroendocrine tumors (pNETs). Methods: This retrospective study conducted a comprehensive review of the CEUS database between March 2017 and November 2021 in Zhongshan Hospital, Fudan University. Ultrasound examinations were performed by an ACUSON Sequioa unit equipped with a 3.5 MHz 6C−1 convex array transducer, and an ACUSON OXANA2 unit equipped with a 3.5 MHz 5C−1 convex array transducer. SonoVue® (Bracco Inc., Milan, Italy) was used for all CEUS examinations. Time intensity curves (TICs) and quantitative parameters of DCE-US were created by Vuebox® software (Bracco, Italy). Inclusion criteria were: patients with histopathologically proved pNETs, patients who underwent pancreatic B-mode ultrasounds (BMUS) and CEUS scans one week before surgery or biopsy and had DCE-US imaging documented for more than 2 min, patients with solid or predominantly solid lesions and patients with definite diagnosis of histopathological grades of pNETs. Based on their prognosis, patients were categorized into two groups: pNETs G1/G2 group and pNETs G3/pNECs group. Results: A total of 42 patients who underwent surgery (n = 38) or biopsy (n = 4) and had histopathologically confirmed pNETs were included. According to the WHO 2019 criteria, all pNETs were classified into grade 1 (G1, n = 10), grade 2 (G2, n = 21), or grade 3 (G3)/pancreatic neuroendocrine carcinomas (pNECs) (n = 11), based on the Ki−67 proliferation index and the mitotic activity. The majority of the TICs (27/31) of pNETs G1/G2 were above or equal to those of pancreatic parenchyma in the arterial phase, but most (7/11) pNETs G3/pNECs had TICs below those of pancreatic parenchyma from arterial phase to late phase (p < 0.05). Among all the CEUS quantitative parameters of DCE-US, values of relative rise time (rPE), relative mean transit time (rmTT) and relative area under the curve (rAUC) were significantly higher in pNETs G1/G2 group than those in pNETs G3/pNECs group (p < 0.05). Taking an rPE below 1.09 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [58.70% to 99.80%], 67.64% [48.61% to 83.32%] and 85.78% [74.14% to 97.42%], respectively. Taking rAUC below 0.855 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [66.26% to 99.53%], 83.87% [67.37% to 92.91%] and 94.72% [88.30% to 100.00%], respectively. Conclusions: Dynamic contrast-enhanced ultrasound analysis might be helpful for predicting the pathological grades of pNETs. Among all quantitative parameters, rPE, rmTT and rAUC are potentially useful parameters for predicting G3/pNECs with aggressive behavior.

Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors

BACKGROUND

The treatment strategies and prognosis for gastroenteropancreatic neuroendocrine tumors were associated with tumor grade. Preoperative predictive grading could be of great benefit in the selection of treatment options for patients. However, there is still a lack of effective non-invasive strategies to detect gastrointestinal neuroendocrine tumors (GI-NETs) grading preoperatively.

METHODS

The data on 147 consecutive GI-NETs patients was retrospectively collected from January 1, 2012, to December 31, 2019. Logistic regression was used to construct a predictive model of gastrointestinal neuroendocrine tumor grading using preoperative laboratory and imaging parameters.The validity of the model was assessed by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

RESULTS

The factors associated with GI-NETs grading were age, tumor size, lymph nodes, neuron-specific enolase (NSE), hemoglobin (HGB) and sex, and two models were constructed by logistic regression for prediction. Combining these 6 factors, the nomogram was constructed for model 1 to distinguish between G3 and G1/2, achieving a good AUC of 0.921 (95% CI: 0.884-0.965), and the sensitivity, specificity, accuracy were 0.9167, 0.8256, 0.8630, respectively. The model 2 was to distinguish between G1 and G2/3, and the variables were age, tumor size, lymph nodes, NSE, with an AUC of 0.847 (95% CI: 0.799-0.915), and the sensitivity, specificity, accuracy were 0.7882, 0.8710, 0.8231, respectively. Two online web servers were established on the basis of the proposed nomogram to facilitate clinical use. Both models showed an excellent calibration curve through 1000 times bootstrapped dataset and the clinical usefulness were confirmed using decision curve analysis.

CONCLUSION

The model served as a valuable non-invasive tool for differentiating between different grades of GI-NETs, personalizing the calculation which can lead to a rational treatment choice.

Gastrointestinal stromal tumors in the duodenum show increased contrast enhancement compared with those in the stomach on computed tomography

Duodenal gastrointestinal stromal tumors (D-GISTs) are a rare and relatively small subset of GISTs whose imaging features are not well known. The present study aimed to evaluate the enhancement pattern of D-GISTs compared with that of gastric GISTs (G-GISTs) using dynamic computed tomography. This single-center, retrospective, clinicopathological analysis was conducted on 10 patients with D-GISTs who underwent surgery between June 2006 and October 2018. In the same period, 25 patients with G-GISTs underwent surgery and were enrolled. The contrast ratio was defined as the ratio between Hounsfield units in contrast enhanced and unenhanced images in different phases, and these ratios were compared between the D-GIST and G-GIST groups. Furthermore, microvessel density, analyzed by immunohistochemical staining for CD31, was compared between the D-GIST and G-GIST groups. The contrast ratio of D-GIST was significantly higher than that of G-GIST in the arterial, portal and delayed phases (P<0.01, P<0.01 and P=0.02, respectively). The microvessel density of the D-GISTs was significantly higher than that of the G-GISTs (P<0.0001). D-GISTs were more hypervascular than G-GISTs on both imaging and pathological analyses.

Utility of Quantitative Metrics from Dual-Layer Spectral-Detector CT for Differentiation of Pancreatic Neuroendocrine Tumor and Neuroendocrine Carcinoma

Background: The 2019 WHO classification separates neuroendocrine neoplasms (NENs) into neuroendocrine tumors (NET) and neuroendocrine carcinomas (NEC), which are considered to represent pathologically distinct entities warranting different management approaches. Dual-layer spectral-detector CT (DLCT) may aid their differentiation through specific material decomposition. Objective: To assess the utility of quantitative metrics derived from DLCT for the differentiation of pancreatic NET and NEC. Methods: This retrospective study included 104 patients (mean age 51±13 years; 53 women, 51 men) with pathologically confirmed NEN [89 NET, including 22 grade 1, 48 grade 2, and 19 grade 3 (G3); 15 NEC], who underwent multiphase DLCT within 15 days before biopsy or resection. Two radiologists independently placed ROIs to record tumor attenuation, iodine concentration (IC), and effective atomic number (Zeff) across phases, and also assessed qualitative features (composition, homogeneity, margins, calcifications, main pancreatic duct dilation, vascular invasion, lymphadenopathy). Interreader agreement was assessed. Mean values between readers were obtained for quantitative measures; consensus was reached for qualitative features. NET and NEC were compared using multivariable regression analysis and ROC analysis. Results: Interobserver agreement, expressed as intraclass correlation coefficients, ranged from 0.879 to 0.992 for quantitative metrics, and, expressed as kappa coefficients, from 0.763 to 0.823 for qualitative features. In multivariable analysis of qualitative and quantitative features, significant independent predictors of NEC (P<.05) were IC in portal venous phase (1.3 mg/mL in NEC vs 2.7 mg/mL in NET), Zeff in portal venous phase (8.1 vs 8.6), and attenuation in portal venous phase (78.2 vs 113.5 HU). AUC for predicting NEC was 0.897 for IC, 0.884 for Zeff, 0.921 for combination of IC and Zeff, and 0.855 for attenuation. Predicted probability based on combination of IC and Zeff achieved sensitivity of 93.3% and specificity of 80.9% for NEC. Significant independent predictors (P<.05) for differentiating G3 NET and NEC were IC (1.3 vs 2.0 mg/mL; AUC=0.789) and attenuation (78.2 vs 90.3 HU; AUC=0.647), both measured in portal venous phase. Conclusion: Incorporation of DLCT-metrics improves differentiation of NET and NEC compared with conventional CT attenuation and qualitative features. Clinical Impact: DLCT may help select patients with pancreatic NENs for platinum-based chemotherapies.

Contrast-Enhanced Ultrasonography Findings Correlate with Pathologic Grades of Pancreatic Neuroendocrine Tumors

The correlation of sonographic findings with pathologic grades of pancreatic neuroendocrine tumors (PNETs) remains unclear. This study aimed to evaluate the usefulness of sonographic features in diagnosing the pathologic grade of PNETs. Conventional and contrast-enhanced ultrasonography findings of PNETs diagnosed by surgical pathology from July 2010 to June 2020 were retrospectively reviewed. Sonographic features were compared among three pathologic grades of PNETs according to the World Health Organization 2010 classification. Ordinal regression models were constructed to evaluate the usefulness of the sonographic features in diagnosing the pathologic grade of PNETs. This study enrolled 93 participants with PNETs: 50 grade 1, 31 grade 2 and 12 grade 3. Multivariate ordinal regression analysis suggested that tumor size ≥2 cm (odds ratio [OR], 0.110; 95% confidence interval [CI], 0.020-0.606; p = 0.011), dilation of the main pancreatic duct (OR, 0.103; 95% CI, 0.025-0.430; p = 0.002), hepatic metastases (OR, 0.250; 95% CI, 0.072-0.869; p = 0.029) and hyper-enhancement in arterial phase (OR, 4.676; 95% CI, 1.656-13.206; p = 0.004) were significantly associated with the pathologic grades of PNETs. The accuracy of the ordinal logistic regression model in identifying grade 1, 2 and 3 PNETs was 77.4%, 67.7% and 90.3%, respectively. The findings suggest that sonographic features, including tumor size, pancreatic duct dilation and hepatic metastasis, as well as the enhancement level in arterial phase, may help identify different pathologic grades of PNETs.

The value of MRI in identifying pancreatic neuroendocrine tumour G3 and carcinoma G3

AIM

To explore the magnetic resonance imaging (MRI) differences between pancreatic neuroendocrine tumour grade 3 (pNET-G3) and pancreatic neuroendocrine carcinoma grade 3 (pNEC-G3).

MATERIALS AND METHODS

Between 2009 and 2019, 31 patients underwent pNEN-G3 resection with preoperative MRI in two local hospitals in China. The 31 patients were assigned to a pNET-G3 group (n=13) or a pNEC-G3 group (n=18). The MRI findings between the groups were compared.

RESULTS

There was no statistically significant difference between the two groups in lesion size, clinical characteristics, or laboratory indexes. The lesions showed high or slightly higher signal on diffusion-weighted imaging and decreased apparent diffusion coefficient (ADC) values, which differed between the two groups (p=0.013). The difference between the groups regarding positive enhancement integral, arterial phase and portal phase signal enhancement ratio were statistically significant; however, the delayed phase signal enhancement ratio was not significantly different.

CONCLUSIONS

pNET-G3 and pNEC-G3 showed different characteristics on MRI. In particular, the ADC value and dynamic enhanced imaging could have an important role in distinguishing between the two.

Correlation between radiologic features on contrast-enhanced CT and pathological tumor grades in pancreatic neuroendocrine neoplasms

Contrast-enhanced computed tomography (CT) contributes to the increasing detection of pancreatic neuroendocrine neoplasms (PNENs). Nevertheless, its value for differentiating pathological tumor grades is not well recognized. In this report, we have conducted a retrospective study on the relationship between the 2017 World Health Organization (WHO) classification and CT imaging features in 94 patients. Most of the investigated features eventually provided statistically significant indicators for discerning PNENs G3 from PNENs G1/G2, including tumor size, shape, margin, heterogeneity, intratumoral blood vessels, vascular invasion, enhancement pattern in both contrast phases, enhancement degree in both phases, tumor-to-pancreas contrast ratio in both phases, common bile duct dilatation, lymph node metastases, and liver metastases. Ill-defined tumor margin was an independent predictor for PNENs G3 with the highest area under the curve (AUC) of 0.906 in the multivariable logistic regression and receiver operating characteristic curve analysis. The portal enhancement ratio (PER) was shown the highest AUC of 0.855 in terms of quantitative features. Our data suggest that the traditional contrast-enhanced CT still plays a vital role in differentiation of tumor grades and heterogeneity analysis prior to treatment.

Comparison between neuroendocrine carcinomas and well-differentiated neuroendocrine tumors of the pancreas using dynamic enhanced CT

OBJECTIVES

To identify CT features distinguishing neuroendocrine carcinomas (NECs) of pancreas from well-differentiated neuroendocrine tumors (NETs) according to the World Health Organization 2017 and 2019 classification systems.

METHODS

This retrospective study included 69 patients with pathologically confirmed pancreatic neuroendocrine neoplasms who underwent dynamic CT (17, 17, 18, and 17 patients for well-differentiated grade 1, 2, 3 NET and NEC, respectively). CT was used to perform qualitative analysis (component, homogeneity, calcification, peripancreatic infiltration, main pancreatic ductal dilatation, bile duct dilatation, intraductal extension, and vascular invasion) and quantitative analysis (interface between tumor and parenchyma [delta], arterial enhancement ratio [AER], portal enhancement ratio [PER], and dynamic enhancement pattern). Uni- and multivariate logistic regression analyses were performed to identify features indicating NEC. Optimal cutoff values for enhancement ratios were determined.

RESULTS

NECs demonstrated significantly higher frequencies of main pancreatic ductal dilatation, bile duct dilatation, vascular invasion, and significantly lower delta (i.e., lower conspicuity), AER, and PER than well-differentiated NET (p < 0.05). On multivariate analysis, PER was the only independent factor selected by the model for differentiation of NEC from well-differentiated NET (odds ratio, < 0.001; 95% confidence interval [CI], < 0.001-0.012). PER < 0.8 showed the sensitivity of 94.1% (95% CI, 71.3-99.9) and the specificity of 88.5% (95% CI, 76.6-95.6). When three significant CT features were combined, the sensitivity and specificity for diagnosing NEC were 88.2% and 88.5%, respectively.

CONCLUSIONS

Tumor-parenchyma enhancement ratio in portal phase is a useful CT feature to distinguish NECs from well-differentiated NETs. Combining qualitative and quantitative CT features may aid in achieving good diagnostic accuracy in the differentiation between NEC and well-differentiated NET.

KEY POINTS

• Neuroendocrine carcinoma of the pancreas should be distinguished from well-differentiated neuroendocrine tumor in line with the revised grading and staging system. • Neuroendocrine carcinoma of the pancreas can be differentiated from well-differentiated neuroendocrine tumor on dynamic CT based on assessment of the portal enhancement ratio, arterial enhancement ratio, tumor conspicuity, dilatation of the main pancreatic duct or bile duct, and vascular invasion. • Tumor-parenchyma enhancement ratio in portal phase of dynamic CT is a useful feature, which may help to distinguish neuroendocrine carcinoma from well-differentiated neuroendocrine tumor of the pancreas.

Contrast-enhanced harmonic endoscopic ultrasound using time-intensity curve analysis predicts pathological grade of pancreatic neuroendocrine neoplasm

PURPOSE

Histological grading is important for the treatment algorithm in pancreatic neuroendocrine neoplasms (PNEN). The present study examined the efficacy of contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) and time-intensity curve (TIC) analysis of PNEN diagnosis and grading.

METHODS

TIC analysis was performed in 30 patients using data obtained from CH-EUS, and a histopathological diagnosis was made via EUS-guided fine-needle aspiration or surgical resection. The TIC parameters were analyzed by dividing them into G1/G2 and G3/NEC groups. Then, patients were classified into non-aggressive and aggressive groups and evaluated.

RESULTS

Twenty-six patients were classified as G1/G2, and four as G3/NEC. From the TIC analysis, five parameters were obtained (I: echo intensity change, II: time for peak enhancement, III: speed of contrast, IV: decrease rate for enhancement, and V: enhancement ratio for node/pancreatic parenchyma). Three of these parameters (I, IV, and V) showed high diagnostic performance. Using the cutoff value obtained from the receiver-operating characteristic (ROC) analysis, the correct diagnostic rates of parameters I, IV, and V were 96.7%, 100%, and 100%, respectively, between G1/G2 and G3/NEC. A total of 21 patients were classified into the non-aggressive group, and nine into the aggressive group. Using the cutoff value obtained from the ROC analysis, the accurate diagnostic rates of I, IV, and V were 86.7%, 86.7%, and 88.5%, respectively, between the non-aggressive and aggressive groups.

CONCLUSION

CH-EUS and TIC analysis showed high diagnostic accuracy for grade diagnosis of PNEN. Quantitative perfusion analysis is useful to predict PNEN grade diagnosis preoperatively.

Competing risks and cause-specific mortality in patients with pancreatic neuroendocrine tumors

BACKGROUND AND OBJECTIVE

Currently, there are no competing risk analyses of cause-specific mortality in patients with pancreatic neuroendocrine tumors.

MATERIALS AND METHODS

We estimated a cumulative incidence function for cause-specific mortality. The first nomogram for predicting cause-specific mortality was constructed using a proportional subdistribution hazard model, validated using bootstrap cross-validation, and evaluated with decision curve analysis.

RESULTS

Sex, age, positive lymph node status, metastasis, surveillance, epidemiology, and end results historic stage, grade, and surgery strongly predicted cause-specific mortality. The discrimination performance of Fine-Gray models was evaluated using the c-index, which was 0.864. In addition, the calibration plot of the developed nomogram demonstrated good concordance between the predicted and actual outcomes. Decision curve analysis yielded a range of threshold probabilities (0.014-0.779) at which the clinical net benefit of the risk model was greater than that in hypothetical all-screening or no-screening scenarios.

CONCLUSION

Our nomogram allows selection of a patient population at high risk for cancer-specific mortality and thus facilitates the design of prevention trials for the affected population.

CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study

OBJECTIVE

To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs).

METHODS

One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use.

RESULTS

The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.

CONCLUSION

We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.

KEY POINTS

• Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.

Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade

PURPOSE

Grades of pancreatic neuroendocrine neoplasms (PNENs) are associated with the choice of treatment strategies. Texture analysis has been used in tumor diagnosis and staging evaluation. In this study, we aim to evaluate the potential ability of texture parameters in differentiation of PNENs grades.

MATERIALS AND METHODS

37 patients with histologically proven PNENs and underwent pretreatment dynamic contrast-enhanced computed tomography examinations were retrospectively analyzed. Imaging features and texture features at contrast-enhanced images were evaluated. Receiver operating characteristic curves were used to determine the cut-off values and the sensitivity and specificity of prediction.

RESULTS

There were significant differences in tumor margin, pancreatic duct dilatation, lymph nodes invasion, size, portal enhancement ratio (PER), arterial enhancement ratio (AER), mean grey-level intensity, kurtosis, entropy, and uniformity among G1, G2, and pancreatic neuroendocrine carcinoma (PNEC) G3 (p < 0.01). Similar results were found between pancreatic neuroendocrine tumors (PNETs) G1/G2 and PNEC G3. AER and PER showed the best sensitivity (0.86-0.94) and specificity (0.92-1.0) for differentiating PNEC G3 from PNETs G1/G2. Mean grey-level intensity, entropy, and uniformity also showed acceptable sensitivity (0.73-0.91) and specificity (0.85-1.0). Mean grey-level intensity was also showed acceptable sensitivity (91% to 100%) and specificity (82% to 91%) in differentiating PNET G1 from PNET G2.

CONCLUSIONS

Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.

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

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.

Association between pathologic grade and multiphase computed tomography enhancement in pancreatic neuroendocrine neoplasm

BACKGROUND AND AIM

Pancreatic neuroendocrine neoplasms (PanNENs) are rare diseases but gradually increasing in prevalence with different prognosis. Multiphase contrast-enhanced computed tomography (CT) is known as widely used imaging modality for the diagnosis of pancreatic tumors. We aimed to investigate whether CT enhancement pattern is associated with the pathologic tumor grade and can predict that of PanNEN.

METHODS

Ninety PanNEN patients who underwent multiphase enhanced CT before pathologic diagnosis were retrospectively reviewed. CT enhancement values at each phase were measured, and its relation with pathologic grade was assessed.

RESULTS

Ninety PanNENs included 62 G1 (68.9%), 21 G2 (23.3%), and 7 G3 (7.8%). The enhancement values of the early arterial phase were significantly different among three groups (G1 119.4 HU, G2 94.7 HU, and G3 64.8 HU; G1 vs G2, P = 0.043; G1 vs G3, P = 0.001; and G2 vs G3, P = 0.027). In the late arterial phase, there was a difference between grade 1/2 and 3 but no significant difference between grade 1 and grade 2 (G1 164.3 HU, G2 142.9 HU, and G3 94.1 HU; G1 vs G2, P = 0.804; G1 vs G3, P = 0.016; and G2 vs G3, P = 0.022). The enhancement value of the portal phase did not differ significantly among the three groups. Diagnostic ability of the early arterial enhancement value for the differentiation of the G1 (cutoff 109.5 HU, sensitivity 73.3%, and specificity 62.5%) was comparable with that of the tumor size (cutoff 20.5 mm, sensitivity 68.9%, and specificity 66.7%).

CONCLUSIONS

Computed tomography enhancement value at early arterial phase and its changing pattern can be a useful predictor for the differentiation of pathologic grade of PanNENs.