Role of diffusion-weighted MR imaging in predicting the grade of nonfunctional pancreatic neuroendocrine tumors

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.

Imaging Update for Hereditary Abdominopelvic Neuroendocrine Neoplasms

ABSTRACT

Neuroendocrine neoplasms have shown a linear increase in incidence and prevalence in recent decades, primarily due to improved cross-sectional imaging, expanded use of endoscopic procedures, and advanced genetic analysis. However, diagnosis of hereditary neuroendocrine tumors is still challenging because of heterogeneity in their presentation, the variety of tumor locations, and multiple associated syndromes. Radiologists should be familiar with the spectrum of these tumors and associated hereditary syndromes. Furthermore, as the assessment of multiple tumor elements such as morphology, biochemical markers, and presence of metastatic disease are essential for the treatment plan, conventional anatomic and functional imaging methods are fundamental in managing and surveilling these cases. Our article illustrates the role of different cross-sectional imaging modalities in diagnosing and managing various hereditary abdominopelvic neuroendocrine tumors.

Liver Imaging in Gastroenteropancreatic Neuroendocrine Neoplasms

ABSTRACT

The incidence of neuroendocrine neoplasms (NENs) has gradually increased over the past few decades with the majority of patients presenting with metastases on initial presentation. The liver is the most common site of initial metastatic disease, and the presence of liver metastasis is an independent prognostic factor associated with a negative outcome. Because NENs are heterogenous neoplasms with variable differentiation, grading, and risk of grade transformation over time, accurate diagnosis and management of neuroendocrine liver lesions are both important and challenging. This is particularly so with the multiple liver-directed treatment options available. In this review article, we discuss the diagnosis, treatment, and response evaluation of NEN liver metastases.

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.

Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms (PanNENs) based on CT radiomic features

OBJECTIVES

To predict tumor grade (G1 vs. G2/3), presence of distant metastasis (M+), metastatic lymph nodes (N+), and microvascular invasion (VI) of pancreatic neuroendocrine neoplasms (PanNEN) based on preoperative CT radiomic features (RFs), by applying a machine learning approach aimed to limit overfit.

METHODS

This retrospective study included 101 patients who underwent surgery for PanNEN; the entire population was split into training (n = 70) and validation cohort (n = 31). Based on a previously validated methodology, after tumor segmentation on contrast-enhanced CT, RFs were extracted from unenhanced CT images. In addition, conventional radiological and clinical features were combined with RFs into multivariate logistic regression models using minimum redundancy and a bootstrap-based machine learning approach. For each endpoint, models were trained and validated including only RFs (RF_model), and both (radiomic and clinicoradiological) features (COMB_model).

RESULTS

Twenty-five patients had G2/G3 tumor, 37 N+, and 14 M+ and 38 were shown to have VI. From a total of 182 RFs initially extracted, few independent radiomic and clinicoradiological features were identified. For M+ and G, the resulting models showed moderate to high performances: areas under the curve (AUC) for training/validation cohorts were 0.85/0.77 (RF_model) and 0.81/0.81 (COMB_model) for M+ and 0.67/0.72 and 0.68/0.70 for G. Concerning N+ and VI, only the COMB_model could be built, with poorer performance for N+ (AUC = 0.72/0.61) compared to VI (0.82/0.75). For all endpoints, the negative predictive value was good (≥ 0.75).

CONCLUSIONS

Combining few radiomic and clinicoradiological features resulted in presurgical prediction of histological characteristics of PanNENs. Despite the limited risk of overfit, external validations are warranted.

KEY POINTS

• Histology is the only tool currently available allowing characterization of PanNEN biological characteristics important for prognostic assessment; significant limitations to this approach exist. • Based upon preoperative contrast-enhanced CT images, a machine learning approach optimized to favor models' generalizability was successfully applied to train predictive models for tumor grading (G1 vs. G2/3), microvascular invasion, metastatic lymph nodes, and distant metastatic spread. • Moderate to high discriminative models (AUC: 0.67-0.85) based on few parameters (≤ 3) showing high negative predictive value (0.75-0.98) were generated and then successfully validated.

Pancreatic neuroendocrine neoplasms: a 2022 update for radiologists

Pancreatic neuroendocrine neoplasms (PaNENs) are a unique group of pancreatic neoplasms with a wide range of clinical presentations and behaviors. Given their heterogeneous appearance and increasing detection on cross-sectional imaging, it is essential that radiologists understand the variable presentation and distinctions PaNENs display compared to other pancreatic neoplasms. Additionally, some of these neoplasms may be hormonally functional, and it is imperative that radiologists be aware of the common clinical presentations of hormonally active PaNENs. Knowledge of PaNEN pathology and treatments may influence which imaging modality is optimal for each patient. Each imaging modality used for PaNENs has distinct advantages and disadvantages, particularly in different treatment settings. Thus, the focus of this manuscript is to provide an update for the radiologist on PaNEN pathology, imaging, and treatments.

New frontiers in imaging including radiomics updates for pancreatic neuroendocrine neoplasms

OBJECTIVE

To illustrate the applications of various imaging tools including conventional MDCT, MRI including DWI, CT & MRI radiomics, FDG & DOTATATE PET-CT for diagnosis, staging, grading, prognostication, treatment planning and assessing treatment response in cases of pancreatic neuroendocrine neoplasms (PNENs).

BACKGROUND

Gastroenteropancreatic neuroendocrine neoplasms (GEP NENs) are very diverse clinically & biologically. Their treatment and prognosis depend on staging and primary site, as well as histological grading, the importance of which is also reflected in the recently updated WHO classification of GEP NENs. Grade 3 poorly differentiated neuroendocrine carcinomas (NECs) are aggressive & nearly always advanced at diagnosis with poor prognosis; whereas Grades-1 and 2 well-differentiated neuroendocrine tumors (NETs) can be quite indolent. Grade 3 well-differentiated NETs represent a new category of neoplasm with an intermediate prognosis. Importantly, the evidence suggest grade heterogeneity can occur within a given tumor and even grade progression can occur over time. Emerging evidence suggests that several non-invasive qualitative and quantitative imaging features on CT, dual-energy CT (DECT), MRI, PET and somatostatin receptor imaging with new tracers, as well as texture analysis, may be useful to grade, prognosticate, and accurately stage primary NENs. Imaging features may also help to inform choice of treatment and follow these neoplasms post-treatment.

CONCLUSION

GEP NENs treatment and prognosis depend on the stage as well as histological grade of the tumor. Traditional ways of imaging evaluation for diagnosis and staging does not yet yield sufficient information to replace operative and histological evaluation. Recognition of important qualitative imaging features together with quantitative features and advanced imaging tools including functional imaging with DWI MRI, DOTATATE PET/CT, texture analysis with radiomics and radiogenomic features appear promising for more accurate staging, tumor risk stratification, guiding management and assessing treatment response.

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.

Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR

Background

Pancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images.

Materials and Methods

Totally 48 patients but 51 lesions with a pathological tumor grade were subdivided into low grade (G1) group and intermediate grade (G2) group. The ROI was manually segmented slice by slice in 3D-T1 weighted sequence with and without enhancement. Statistical differences of radiomic features between G1 and G2 groups were analyzed using the independent sample t-test. Logistic regression analysis was conducted to find better predictors in distinguishing G1 and G2 groups. Finally, receiver operating characteristic (ROC) was constructed to assess diagnostic performance of each model.

Results

No significant difference between G1 and G2 groups (P > 0.05) in non-enhanced 3D-T1 images was found. Significant differences in the arterial phase analysis between the G1 and the G2 groups appeared as follows: the maximum intensity feature (P = 0.021); the range feature (P = 0.039). Multiple logistic regression analysis based on univariable model showed the maximum intensity feature (P=0.023, OR = 0.621, 95% CI: 0.433-0.858) was an independent predictor of G1 compared with G2 group, and the area under the curve (AUC) was 0.695.

Conclusions

The maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.

Surgical management of insulinoma over three decades

BACKGROUND

This paper reports our experience of the perioperative management of patients with sporadic, non-malignant, pancreatic insulinoma.

METHODS

A retrospective monocentric cohort study was performed from January 1989 to July 2019, including all the patients who had been operated on for pancreatic insulinoma. The preoperative work-up, surgical management, and postoperative outcome were analyzed.

RESULTS

Eighty patients underwent surgery for sporadic pancreatic insulinoma, 50 of which were female (62%), with a median age of 50 (36-70) years. Preoperatively, the tumors were localized in 76 patients (95%). Computed tomography (CT) and magnetic resonance imaging allowed exact preoperative tumor localization in 76% of the patients (64-85 and 58-88 patients, respectively), increasing to 96% when endoscopic ultrasonography was performed. Forty-one parenchyma-sparing pancreatectomies (PSP) (including enucleation, caudal pancreatectomy, and uncinate process resection) and 39 pancreatic resections were performed. The mortality rate was 6% (n = 5), with a morbidity rate of 72%, including 24 severe complications (30%) and 35 pancreatic fistulas (44%). No differences were found between formal pancreatectomy and PSP in terms of postoperative outcome procedures. The surgery was curative in all the patients.

CONCLUSION

CT used in combination with endoscopic ultrasonography allows accurate localization of insulinomas in almost all patients. When possible, a parenchyma-sparing pancreatectomy should be proposed as the first-line surgical strategy.

Updated Trends in Imaging Practices for Pancreatic Neuroendocrine Tumors (PNETs): A Systematic Review and Meta-Analysis to Pave the Way for Standardization in the New Era of Big Data and Artificial Intelligence

Purpose

Medical imaging plays a central and decisive role in guiding the management of patients with pancreatic neuroendocrine tumors (PNETs). Our aim was to synthesize all recent literature of PNETs, enabling a comparison of all imaging practices.

Methods

based on a systematic review and meta-analysis approach, we collected; using MEDLINE, EMBASE, and Cochrane Library databases; all recent imaging-based studies, published from December 2014 to December 2019. Study quality assessment was performed by QUADAS-2 and MINORS tools.

Results

161 studies consisting of 19852 patients were included. There were 63 ‘imaging’ studies evaluating the accuracy of medical imaging, and 98 ‘clinical’ studies using medical imaging as a tool for response assessment. A wide heterogeneity of practices was demonstrated: imaging modalities were: CT (57.1%, n=92), MR (42.9%, n=69), PET/CT (13.3%, n=31), and SPECT/CT (9.3%, n=15). International imaging guidelines were mentioned in 2.5% (n=4/161) of studies. In clinical studies, imaging protocol was not mentioned in 30.6% (n=30/98) of cases and only mentioned imaging modality without further information in 63.3% (n=62/98), as compared to imaging studies (1.6% (n=1/63) of (p<0.001)). QUADAS-2 and MINORS tools deciphered existing biases in the current literature.

Conclusion

We provide an overview of the updated current trends in use of medical imaging for diagnosis and response assessment in PNETs. The most commonly used imaging modalities are anatomical (CT and MRI), followed by PET/CT and SPECT/CT. Therefore, standardization and homogenization of PNETs imaging practices is needed to aggregate data and leverage a big data approach for Artificial Intelligence purposes.

Diagnostic Performance of Diffusion-Weighted Imaging for Differentiating Malignant From Benign Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Systematic Review and Meta-Analysis

Objectives

To assess the diagnostic accuracy of diffusion-weighted imaging (DWI) in predicting the malignant potential in patients with intraductal papillary mucinous neoplasms (IPMNs) of the pancreas.

Methods

A systematic search of articles investigating the diagnostic performance of DWI for prediction of malignant potential in IPMNs was conducted from PubMed, Embase, and Web of Science from January 1997 to 10 February 2020. QUADAS-2 tool was used to evaluate the study quality. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratios (PLR), negative likelihood ratios (NLR), and their 95% confidence intervals (CIs) were calculated. The summary receiver operating characteristic (SROC) curve was then plotted, and meta-regression was also performed to explore the heterogeneity.

Results

Five articles with 307 patients were included. The pooled sensitivity and specificity of DWI were 0.74 (95% CI: 0.65, 0.82) and 0.94 (95% CI: 0.78, 0.99), in evaluating the malignant potential of IPMNs. The PLR was 13.5 (95% CI: 3.1, 58.7), the NLR was 0.27 (95% CI: 0.20, 0.37), and DOR was 50.0 (95% CI: 11.0, 224.0). The area under the curve (AUC) of SROC curve was 0.84 (95% CI: 0.80, 0.87). The meta-regression showed that the slice thickness of DWI (p = 0.02) and DWI parameter (p= 0.01) were significant factors affecting the heterogeneity.

Conclusions

DWI is an effective modality for the differential diagnosis between benign and malignant IPMNs. The slice thickness of DWI and DWI parameter were the main factors influencing diagnostic specificity.

Artificial intelligence: a critical review of current applications in pancreatic imaging

The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.

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.

Magnetic Resonance Imaging May Be Able to Identify the Origin of Neuroendocrine Tumor Liver Metastases

OBJECTIVES

The aim of the study was to discriminate hepatic metastases from pancreatic neuroendocrine tumors (pNET) and hepatic metastases from midgut neuroendocrine tumors (mNET) with magnetic resonance imaging (MRI).

METHODS

MRI examinations of 24 patients with hepatic metastases from pNET were quantitatively and qualitatively assessed by 2 blinded readers and compared to those obtained in 23 patients with hepatic metastases from mNET. Inter-reader agreement was calculated with kappa and intraclass correlation coefficient (ICC). Sensitivity, specificity, and accuracy of each variable for the diagnosis of hepatic metastasis from pNET were calculated. Associations between variables and primary tumor (i.e., pNET vs. mNET) were assessed by univariate and multivariate analyses. A nomogram was developed and validated using an external cohort of 20 patients with pNET and 20 patients with mNET.

RESULTS

Interobserver agreement was strong to perfect (k = 0.893-1) for qualitative criteria and excellent for quantitative variables (ICC: 0.9817-0.9996). At univariate analysis, homogeneity on T1-weighted images was the most discriminating variable for the diagnosis of pNET (OR: 6.417; p = 0.013) with greatest sensitivity (88%; 21/24; 95% CI: 68-97%). At multivariate analysis, tumor homogeneity on T1-weighted images (p = 0.007; OR: 17.607; 95% CI: 2.179-142.295) and target sign on diffusion-weighted images (p = 0.007; OR: 19.869; 95% CI: 2.305-171.276) were independently associated with pNET. Nomogram yielded a corrected AUC of 0.894 (95% CI: 0.796-0.992) for the diagnosis of pNET in the training cohort and 0.805 (95% CI: 0.662-0.948) in the validation cohort.

CONCLUSIONS

MRI provides qualitative features that can help discriminate between hepatic metastases from pNET and those from mNET.

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.

CT and MRI of pancreatic tumors: an update in the era of radiomics

Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.

Prospective Comparison of Reduced Field-of-View (rFOV) and Full FOV (fFOV) Diffusion-Weighted Imaging (DWI) in the Assessment of Insulinoma: Image Quality and Lesion Detection

RATIONALE AND OBJECTIVES

To prospectively compare the image quality (IQ) and lesion detection performance of reduced field-of-view (rFOV) and full FOV (fFOV) diffusion-weighted imaging (DWI) sequences in detecting insulinomas.

MATERIALS AND METHODS

From October 2017 to September 2018, 67 patients with suspected insulinomas were prospectively enrolled and underwent imaging with both types of DWI sequences. The slice thickness (4 mm) and slice gaps (1 mm) were the same for the two DWI sequences, and the TR/TE was 2235/56 ms for the rFOV sequence and 1892/63 ms for the fFOV sequence. Three radiologists independently assessed the imaging quality (IQ) subjectively with a 5-point scale and objectively with signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. The IQ scores, CNR, SNR, lesion detection rates, and ADC values were compared. Receiver operating characteristic curves were generated, and the area under the curve (AUC) was used to compare the diagnostic performance.

RESULTS

Fifty patients were tumor positive, with 65 tumors (size: 1.31 ± 0.77 cm, range: 0.6-5.8 cm). The IQ score, SNR, and CNR were significantly higher for rFOV DWI than for fFOV DWI (IQ: 3.64 ± 0.487 vs 3.310 ± 0.577, SNR: 22.520 ± 8.690 vs 10.284 ± 3.321, CNR: 3.454 ± 2.642 vs 1.327 ± 2.801, and all p < 0.05). For lesions less than 1.5 cm (n = 55), the lesion detection rates of the rFOV were statistically improved compared to those of the fFOV (90.7% vs. 75.9%, p = 0.039). The sensitivity of lesion detection was significantly improved with the rFOV-DWI sequences compared to that with the fFOV-DWI sequences (0.924 vs. 0.773, p = 0.013). The ADC values of the two DWI sequences were consistent for insulinomas and normal parenchyma.

CONCLUSION

Considering the improvements in overall IQ and lesion detection and the consistency of ADC measurements, we suggest that rFOV DWI is a reliable auxiliary alternative to fFOV DWI for clinical practice in the detection of pancreatic insulinomas.

Differentiating Giant Cell Glioblastoma from Classic Glioblastoma With Diffusion-Weighted Imaging

BACKGROUND

Differential diagnosis of giant cell glioblastoma (GC) and classic glioblastoma (GBM) using conventional radiological modalities is difficult. This study aimed to use diffusion-weighted imaging (DWI) to distinguish GC from GBM and thereby improve the accuracy of preoperative assessment of patients with GB.

METHODS

The clinical, magnetic resonance imaging, and pathologic data of 12 patients with GC and 21 patients with GBM were retrospectively analyzed. Independent sample t tests were used to compare the minimum apparent diffusion coefficient (ADC_min) and the normalized apparent diffusion coefficients (nADC) of the 2 tumor types. Receiver operating curve (ROC) analysis was used to assess the diagnostic efficacy of ADC_min and nADC values.

RESULTS

Compared with that of the classic GBM group, the ADC_min (0.98 ± 0.14 vs. 0.80 ± 0.19×10^-3 mm²/second, P = 0.007) and nADC (1.42 ± 0.25 vs. 1.17 ± 0.25, P = 0.011) of the GC group were significantly higher. ROC curve analysis showed that the maximum area under the curve of ADC_min and nADC were 0.800 ± 0.080 and 0.778 ± 0.082, respectively. The sensitivity, specificity, and accuracy distinguishing GC and classic GBM was best (83.33%, 76.19%, and 78.79%, respectively) when ADC_min = 0.84×10^-3 mm²/second (maximum area under the ROC, 0.800). Its positive and negative predictive values under this condition were 88.89% and 66.67%, respectively.

CONCLUSIONS

By distinguishing GC from classic GBM, the ADC_min parameter of DWI can improve the accuracy of the preoperative differential diagnosis of the 2 tumor types.

Multiparametric magnetic resonance imaging of parotid tumors: A systematic review

PURPOSE

The purpose of this systematic review was to provide an overview of the contribution of multiparametric magnetic resonance imaging (MRI) in the diagnosis of parotid tumors (PT) and recommendations based on current evidences.

MATERIAL AND METHODS

We performed a retrospective systematic search of PubMed, EMBASE, and Cochrane Library databases from inception to January 2020, using the keywords "magnetic resonance imaging" and "salivary gland neoplasms".

RESULTS

The initial search returned 2345 references and 90 were deemed relevant for this study. A total of 54 studies (60%) reported the use of diffusion-weighted imaging (DWI) and 28 studies (31%) the use of dynamic contrast-enhanced (DCE) imaging. Specific morphologic signs of frequent benign PT and suggestive signs of malignancy on conventional sequences were reported in 37 studies (41%). DWI showed significant differences in apparent diffusion coefficient (ADC) values between benign and malignant PT, and especially between pleomorphic adenomas and malignant PT, with cut-off ADC values between 1.267×10^-3mm²/s and 1.60×10^-3mm²/s. Perfusion curves obtained with DCE imaging allowed differentiating among pleomorphic adenomas, Warthin's tumors, malignant PT and cystic lesions. The combination of morphological MRI sequences, DCE imaging and DWI helped increase the diagnostic accuracy of MRI.

CONCLUSION

Multiparametric MRI, including morphological MRI sequences, DWI and DCE imaging, is the imaging modality of choice for the characterization of focal PT and provides features that are highly suggestive of a specific diagnosis.

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.

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.

Correlation Between Apparent Diffusion Coefficient Value on MRI and Histopathologic WHO Grades of Neuroendocrine Tumors

Background

The correlation of diffusion-weighted MRI and tumor aggressiveness has been established for different tumor types, which leads to the question if it could also apply for neuroendocrine tumors (NET).

Purpose

To investigate the possible correlation between apparent diffusion coefficient (ADC) value on magnetic resonance imaging (MRI) and histopathologic WHO-grades of NET.

Material and Methods

Electronic patient records from patients presented at the multidisciplinary neuro-endocrine tumor board between November 2017 and April 2019 were retrospectively reviewed. Patients with both available MR imaging (primary tumor or metastasis) and known WHO tumor grade were included (n = 47). Average and minimum ADC values (avgADC; minADC) were measured by drawing a freehand ROI excluding only the outermost border of the lesion. The largest axial size (primary tumor) or most clearly delineated lesion (metastasis) was used.

Results

Forty seven patients met the inclusion criteria (mean age 59 ± 12 SD; 24F/23M). Twenty one patients (45%) were diagnosed with WHO G1 tumor, 17 seventeen with G2 (36%) and nine with G3 (19%) tumor. Twenty eight primary tumors and 19 metastases were measured. A significant difference was found between low-grade (G1+G2) and high-grade (G3) tumors (Mann-Whitney; avgADC: p < 0,001; minADC: p = 0,001). There was a moderate negative correlation between WHO-grade and avgADC/minADC (Spearman; avgADC: -0,606; 95% CI [-0,773; -0,384]; minADC: -0,581; 95% CI [-0.759; -0.353]).

Conclusion

Our data show a significant difference in both average and minimum ADC values on MRI between low and high grade NET. A moderate negative correlation was found between histopathologic WHO grade and ADC value.

Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study

PURPOSE

The World Health Organization (WHO) grading system of pancreatic neuroendocrine tumor (PNET) plays an important role in the clinical decision. The rarity of PNET often negatively affects the radiological application of deep learning algorithms due to the low availability of radiological images. We tried to investigate the feasibility of predicting WHO grades of PNET on contrast-enhanced magnetic resonance (MR) images by deep learning algorithms.

MATERIALS AND METHODS

Ninety-six patients with PNET underwent preoperative contrast-enhanced MR imaging. Fivefold cross-validation was used in which five iterations of training and validation were performed. Within every iteration, on the training set augmented by synthetic images generated from generative adversarial network (GAN), a convolutional neural network (CNN) was trained and its performance was evaluated on the paired internal validation set. Finally, the trained CNNs from cross-validation and their averaged counterpart were separately assessed on another ten patients from a different external validation set.

RESULTS

Averaging the results across the five iterations in the cross-validation, for the CNN model, the average accuracy was 85.13% ± 0.44% and micro-average AUC was 0.9117 ± 0.0053. Evaluated on the external validation set, the average accuracy of the five trained CNNs ranges between 79.08 and 82.35%, and the range of micro-average AUC was between 0.8825 and 0.8932. The average accuracy and micro-average AUC of the averaged CNN were 81.05% and 0.8847, respectively.

CONCLUSION

Synthetic images generated from GAN could be used to alleviate the difficulty of radiological image collection for uncommon disease like PNET. With the help of GAN, the CNN showed the potential to predict the WHO grades of PNET on contrast-enhanced MR images.

Diagnostic Performance of Apparent Diffusion Coefficient for Prediction of Grading of Pancreatic Neuroendocrine Tumors: A Systematic Review and Meta-analysis

OBJECTIVES

The aim of this study was to evaluate the diagnostic value of apparent diffusion coefficient (ADC) for the World Health Organization grade of pancreatic neuroendocrine tumors (pNETs).

METHODS

The MEDLINE, Google Scholar, PubMed, and Embase databases were searched to identify relevant original articles investigating the ADC value in predicting the grade of pNETs. The pooled sensitivity (SE), specificity (SP), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated by using random effects models. Subgroup analysis was performed to discover heterogeneity effects.

RESULTS

Nine studies with 386 patients met our inclusion criteria. For identifying G1 from G2/3, the pooled SE, SP, PLR, NLR, and area under the curve of the summary receiver operating characteristic curve were 0.84 (95% confidence interval [95% CI], 0.73-0.91), 0.87 (95% CI, 0.72-0.94), 6.3 (95% CI, 2.7-14.6), 0.19 (95% CI, 0.10-0.34), and 0.91 (95% CI, 0.89-0.94), respectively. The summary estimates for ADC in distinguishing G3 from G1/2 were as follows: SE, 0.93 (95% CI, 0.66-0.99); SP, 0.92 (95% CI, 0.86-0.95); PLR, 11.1 (95% CI, 6.6-18.6); NLR, 0.08 (95% CI, 0.01-0.45); and area under the curve, 0.92 (95% CI, 0.85-0.96).

CONCLUSIONS

Diffusion-weighted imaging is a reliable tool for predicting the grade of pNETs, especially for G3. Moreover, the combination of 3.0-T device and higher b value can slightly help improve SE and SP.

MRI-based nomogram estimates the risk of recurrence of primary nonmetastatic pancreatic neuroendocrine tumors after curative resection

BACKGROUND

Accurate estimation of the recurrence of pancreatic neuroendocrine tumors help with prognosis, guide follow-up, and avoid futile treatments.

PURPOSE

To investigate whether MRI features could preoperatively estimate the recurrence of pancreatic neuroendocrine tumors (PNETs) and to refine a novel prognostic model through developing a nomogram incorporating various MRI features.

STUDY TYPE

Retrospective.

POPULATION

In all, 81 patients with clinicopathologically confirmed nonmetastatic PNETs.

FIELD STRENGTH/SEQUENCES

1.5 T MR, including T₁ -weighted, T₂ -weighted, and diffusion-weighted imaging sequences.

ASSESSMENT

Qualitative and quantitative MRI features of PNET were assessed by three experienced radiologists.

STATISTICAL TESTS

Uni- and multivariable analyses for recurrence-free survival (RFS) were evaluated using a Cox proportional hazards model. The MRI-based nomogram was then designed based on multivariable logistic analysis in our study and the performance of the nomogram was validated according to C-index, calibration, and decision curve analyses.

RESULTS

MRI features, including tumor size (hazard ratio [HR]: 14.131; P = 0.034), enhancement pattern (HR: 21.821, P = 0.032), and the apparent diffusion coefficient (ADC) values (HR: 0.055, P = 0.038) were significant independent predictors of RFS at multivariable analysis. The performance of the nomogram incorporating various MRI features (with a C-index of 0.910) was improved compared with that based on tumor size, enhancement pattern, and ADC alone (with C-index values of 0.672, 0.851, and 0.809, respectively). The calibration curve of the nomogram exhibited perfect consistency between estimation and observation at 0.5, 1, and 2 years after surgery. The decision curve showed that a nomogram incorporating three features had more favorable clinical predictive usefulness than any single feature.

DATA CONCLUSION

MRI features can be considered effective recurrence predictors for PNETs after surgery. The preliminary nomogram incorporating various MRI features could assess the risk of recurrence in PNETs and may be used to optimize individual treatment strategies.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:397-409.

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.