Pancreatic neuroendocrine tumors: correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade

Improvements and future perspective in diagnostic tools for neuroendocrine neoplasms

INTRODUCTION

Neuroendocrine neoplasms (NENs) represent a complex group of tumors arising from neuroendocrine cells, characterized by heterogeneous behavior and challenging diagnostics. Despite advancements in medical technology, NENs present a major challenge in early detection, often leading to delayed diagnosis and variable outcomes. This review aims to provide an in-depth analysis of current diagnostic methods as well as the evolving and future directions of diagnostic strategies for NENs.

AREA COVERED

The review extensively covers the evolution of diagnostic tools for NENs, from traditional imaging and biochemical tests to advanced genomic profiling and next-generation sequencing. The emerging role of technologies such as artificial intelligence, machine learning, and liquid biopsies could improve diagnostic precision, as could the integration of imaging modalities such as positron emission tomography (PET)/magnetic resonance imaging (MRI) hybrids and innovative radiotracers.

EXPERT OPINION

Despite progress, there is still a significant gap in the early diagnosis of NENs. Bridging this diagnostic gap and integrating advanced technologies and precision medicine are crucial to improving patient outcomes. However, challenges such as low clinical awareness, limited possibility of noninvasive diagnostic tools and funding limitations for rare diseases like NENs are acknowledged.

Volumetric apparent diffusion coefficient histogram analysis in term neonatal asphyxia treated with hypothermia

OBJECTIVES

Our aim is to estimate the long-term neurological sequelae and prognosis in term neonatal asphyxia treated with hypothermia via volumetric apparent diffusion coefficient (ADC) map histogram analysis (HA).

METHODS

Brain MRI studies of 83 term neonates with asphyxia who received whole-body hypothermia treatment and examined between postnatal (PN) fourth and sixth days were retrospectively re-evaluated by 2 radiologists. Volumetric HA was performed for the areas frequently affected in deep and superficial asphyxia (thalamus, lentiform nucleus, posterior limb of internal capsule, corpus callosum forceps major, and perirolandic cortex-subcortical white matter) on ADC map. The quantitative ADC values were obtained separately for each region. Qualitative-visual (conventional) MRI findings were also re-evaluated. Neonates were examined neurodevelopmentally according to the Revised Brunet-Lezine scale. The distinguishability of long-term neurodevelopmental outcomes was statistically investigated.

RESULTS

With HA, the adverse neurodevelopmental outcomes could only be distinguished from mild-moderated impairment and normal development at the thalamus with 10th percentile ADC (P = .02 and P = .03, respectively) and ADCmin (P = .03 and P = .04, respectively). Also with the conventional MRI findings, adverse outcome could be distinguished from mild-moderated impairment (P = .04) and normal development (P = .04) via cytotoxic oedema of the thalamus, corpus striatum, and diffuse cerebral cortical.

CONCLUSION

The long-term adverse neurodevelopmental outcomes in newborns with asphyxia who received whole-body hypothermia treatment can be estimated similarly with volumetric ADC-HA and the conventional assessment of the ADC map.

ADVANCES IN KNOWLEDGE

This study compares early MRI ADC-HA with neurological sequelae in term newborns with asphyxia who received whole-body hypothermia treatment. We could not find any significant difference in predicting adverse neurological sequelae between the visual-qualitative evaluation of the ADC map and HA.

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics

Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.

The association between pain and WHO grade of pancreatic neuroendocrine neoplasms: A multicenter study

BACKGROUND

Abdominal or back pain is a common symptom in pancreatic diseases. However, the role of pain in pancreatic neuroendocrine neoplasm (PNENs) has not been clarified.

OBJECTIVE

In this study, we aimed to show the association between the pain and the grade of PNENs.

METHODS

A total of 186 patients with pathologically confirmed PNENs were included in this study. Clinical features and histological or radiological findings (size, location, and vascular invasion and local organs invasion and distal metastasis) were collected. Logistic regression analyses were used to show the association between pain and grade of PNENs. Nomogram was developed based on associated factors to predict the higher grade of PNENs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of size and nomogram model.

RESULTS

The prevalence of pain in the cohort was 30.6% (n= 57). The vascular invasion and G3 PNENs were more common in the pain group (P= 0.02, P< 0.01). The tumor size was larger and incident of higher grade of PNENs was higher in the pain group than the non-pain group (p< 0.01). Age, pain and size were independent risk factors for G2/G3 or G3 PNENs. The odds ratio was 3.03 (95% CI: 1.67-7.91) and 3.32 (95% CI: 1.42-7.79) for pain, respectively. The nomogram model was developed to predict the G2/G3 or G3 PNENs. The area under the curve (AUC) of the nomogram model was 0.84 (95% CI, 0.77-0.91) in predicting the G2/G3 PNENs, and was 0.84 (95% CI, 0.78-0.91) in predicting the G3 PNENs.

CONCLUSION

Abdominal or back pain is associated with the grade of PNENs. The nomograms based on clinical features may be a powerful numerical tool for predicting the grade of PNENs.

MRI features and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer for differentiating epidermal growth factor receptor mutation status

AIM

To explore the utility of magnetic resonance imaging (MRI) characteristics and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer (NSCLC) in the differentiation of epidermal growth factor receptor (EGFR) mutation status.

MATERIALS AND METHODS

Forty-eight patients with brain metastases from NSCLC were enrolled in this retrospective study. Patients were subtyped into EGFR mutation (23 cases) and wild-type (25 cases) groups. Whole-lesion histogram metrics were derived from the apparent diffusion coefficient (ADC) maps, and imaging features were evaluated according to conventional MRI. Student's t-test or Mann-Whitney U-test, chi-squared test, and receiver operating characteristic (ROC) curve analysis were performed to discriminate the two groups and to determine the diagnostic efficacy of ADC histogram parameters.

RESULTS

EGFR mutation group had more multiple brain metastases, less peritumoural brain oedema (PTBO), and lower peritumoural brain oedema index (PTBO-I) than EGFR wild-type group (all p<0.05). In addition, 90th and 75th percentiles of ADC and maximum ADC in the EGFR mutation group were significantly higher than in the EGFR wild-type group (all p<0.05). Ninetieth percentile of ADC had the highest area under the curve (AUC; 0.711), and it was found to outperform 75th percentile of ADC (AUC, 0.662; p=0.039) and maximum ADC (AUC, 0.681).

CONCLUSIONS

Whole-lesion ADC histogram analysis and MRI features of brain metastasis from NSCLC are expected to be potential biomarkers to non-invasively differentiate the EGFR mutation status.

Correlation between ADC Histogram-Derived Metrics and the Time to Metastases in Resectable Pancreatic Adenocarcinoma

Background: A non-invasive method to improve the prognostic stratification would be clinically beneficial in patients with resectable pancreatic adenocarcinoma (PDAC). The aim of this study was to correlate conventional magnetic resonance (MR) features and the metrics derived from the histogram analysis of apparent diffusion coefficient (ADC) maps, with the risk and the time to metastases (TTM) after surgery in patients with PDAC. Methods: pre-operative MR examinations of 120 patients were retrospectively analyzed. Patients were grouped according to the presence (M+) or absence (M−) of metastases during follow-up. Conventional MR features and histogram-derived metrics were compared between M+ and M− patients using the Fisher’s or Mann−Whitney tests; receiver operating characteristic (ROC) curves were constructed for the features that showed a significant difference between groups. A Cox regression analysis was performed to identify the features with a significant effect on the TTM, and Kaplan−Meier curves were constructed for significant features. Results: 68.3% patients developed metastases over a mean follow-up time of 29 months (range, 3−54 months). ADC skewness and kurtosis were significantly higher in M+ than in M− patients (p < 0.001). Skewness had a significant effect on the risk of metastases (hazard ratio—HR = 5.22, p < 0.001). Patients with an ADC skewness ≥0.23 had a significantly shorter TTM than those with a skewness <0.22 (11.7 vs. 30.8 months, p < 0.001). Conclusions: pre-operative histogram analysis of ADC maps provides parameters correlated to the metastatic potential of PDAC. Higher ADC skewness seems to be associated with a significantly shorter TTM in patients with resectable PDAC.

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.

GEP-NET radiomics: a systematic review and radiomics quality score assessment

OBJECTIVE

The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence.

METHODS

PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data.

RESULTS

In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%.

CONCLUSION

Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce.

KEY POINTS

• The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.

Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms

Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis, hormonal syndromes produced, biological behavior and consequently, in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies. Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question. Also, with the increased availability of cross sectional imaging, these neoplasms are increasingly being detected incidentally in routine radiology practice. This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms, along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons, mostly in the research stage.

MRI-based Whole-Tumor Radiomics to Classify the Types of Pediatric Posterior Fossa Brain Tumor

BACKGROUND

Differential diagnosis between medulloblastoma (MB), ependymoma (EP) and astrocytoma (PA) is important due to differing medical treatment strategies and predicted survival. The aim of this study was to investigate non-invasive MRI-based radiomic analysis of whole tumors to classify the histologic tumor types of pediatric posterior fossa brain tumor and improve the accuracy of discrimination, using a random forest classifier.

METHODS

MRI images of 99 patients, with 59 MBs, 13 EPs and 27 PAs histologically confirmed by surgery and pathology before treatment, were included in this retrospective study. Registration was performed between the three sequences, and high- throughput features were extracted from manually segmented tumors on MR images of each case. The forest-based feature selection method was adopted to select the top ten significant features. Finally, the results were compared and analyzed according to the classification.

RESULTS

The top ten contributions according to the classifier of wavelet features all came from the ADC sequence. The random forest classifier achieved 100% accuracy on the training data and validated the best accuracy (0.938): sensitivity = 1.000, 0.948 and 0.808, specificity = 0.952, 0.926 and 1.000 for EP, MB and PA, respectively.

CONCLUSION

A random forest classifier based on the ADC sequence of the whole tumor provides more quantitative information than TIWI and T2WI in differentiating pediatric posterior fossa brain tumors. In particular, the histogram percentile value showed great superiority, which added diagnostic value in pediatric neuro-oncology.

Prediction of overall survival in patients with pancreatic ductal adenocarcinoma: histogram analysis of ADC value and correlation with pathological intratumoral necrosis

BACKGROUND

To evaluate the utility of histogram analysis (HA) of apparent diffusion coefficient (ADC) values to predict the overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC) and to correlate with pathologically evaluated massive intratumoral necrosis (MITN).

MATERIALS AND METHODS

Thirty-nine patients were included in this retrospective study with surgically resected PDAC who underwent preoperative magnetic resonance imaging. Twelve patients received neoadjuvant chemotherapy. HA on the ADC maps were performed to obtain the tumor HA parameters. Using Cox proportional regression analysis adjusted for age, time-dependent receiver-operating-characteristic (ROC) curve analysis, and Kaplan-Meier estimation, we evaluated the association between HA parameters and OS. The association between prognostic factors and pathologically confirmed MITN was assessed by logistic regression analysis.

RESULTS

The median OS was 19.9 months. The kurtosis (P < 0.001), entropy (P = 0.013), and energy (P = 0.04) were significantly associated with OS. The kurtosis had the highest area under the ROC curve (AUC) for predicting 3-year survival (AUC 0.824) among these three parameters. Between the kurtosis and MITN, the logistic regression model revealed a positive correlation (P = 0.045). Lower survival rates occurred in patients with high kurtosis (cutoff value > 2.45) than those with low kurtosis (≤ 2.45) (P < 0.001: 1-year survival rate, 75.2% versus 100%: 3-year survival rate, 14.7% versus 100%).

CONCLUSIONS

HA derived kurtosis obtained from tumor ADC maps might be a potential imaging biomarker for predicting the presence of MITN and OS in patients with PDAC.

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.

Imaging of Pancreatic Neuroendocrine Neoplasms

Pancreatic neuroendocrine neoplasms (panNENs) represent the second most common pancreatic tumors. They are a heterogeneous group of neoplasms with varying clinical expression and biological behavior, from indolent to aggressive ones. PanNENs can be functioning or non-functioning in accordance with their ability or not to produce metabolically active hormones. They are histopathologically classified according to the 2017 World Health Organization (WHO) classification system. Although the final diagnosis of neuroendocrine tumor relies on histologic examination of biopsy or surgical specimens, both morphologic and functional imaging are crucial for patient care. Morphologic imaging with ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) is used for initial evaluation and staging of disease, as well as surveillance and therapy monitoring. Functional imaging techniques with somatostatin receptor scintigraphy (SRS) and positron emission tomography (PET) are used for functional and metabolic assessment that is helpful for therapy management and post-therapeutic re-staging. This article reviews the morphological and functional imaging modalities now available and the imaging features of panNENs. Finally, future imaging challenges, such as radiomics analysis, are illustrated.

Whole-lesion histogram analysis of mono-exponential and bi-exponential diffusion-weighted imaging in differentiating lung cancer from benign pulmonary lesions using 3 T MRI

AIM

To investigate whether whole-lesion histogram analysis of apparent diffusion coefficient (ADC) values derived from mono-exponential and bi-exponential diffusion-weighted imaging (DWI) can differentiate lung cancer from benign pulmonary lesions.

MATERIALS AND METHODS

Thirty-two patients with lung cancer and 17 patients with benign pulmonary lesions were included retrospectively. All patients underwent DWI before surgery or biopsy. ADC histogram parameters, including mean, percentile values (10^th and 90^th), kurtosis, and skewness, were calculated independently by two radiologists. The histogram parameters were compared between patients with lung cancer and benign lesions. Receiver operating characteristic curves were constructed to evaluate the diagnostic performance.

RESULTS

The ADC_Mean, ADC_10th, D_Mean, D_10th were significantly lower in lung cancer (1.187 ± 0.144 × 10^-3; 0.440 ± 0.062 × 10^-3; 1.068 ± 0.108 × 10^-3; and 0.422 ± 0.049 × 10^-3 mm/s) compared to benign lesions (1.418 ± 0.274 × 10^-3; 0.555 ± 0.113 × 10^-3; 1.216 ± 0.149 × 10^-3; and 0.490 ± 0.044 × 10^-3 mm/s; p<0.05). The ADC_Skewness and D_Skewness were significantly different between lung cancer (2.35 ± 0.72; 2.58 ± 1.14) and benign lesions (1.85 ± 0.54; 1.59 ± 1.47; p<0.05). D_10th was robust in differentiating lung cancer from benign lesions. Using 0.453 × 10^-3 mm/s as the optimal threshold, the sensitivity, specificity, and accuracy of D_10th were 78.12%, 82.35%, and 79.6%, respectively.

CONCLUSION

Whole-lesion histogram analysis of ADC values derived by mono-exponential and bi-exponential DWI using 3 T magnetic resonance imaging helps distinguish lung cancer from benign pulmonary lesions.

Correlation between contrast enhancement, standardized uptake value (SUV), and diffusion restriction (ADC) with tumor grading in patients with therapy-naive neuroendocrine neoplasms using hybrid 68Ga-DOTATOC PET/MRI

OBJECTIVES

To investigate a correlation between ⁶⁸Ga-DOTATOC PET/MR imaging parameters such as arterial and venous contrast enhancement, diffusion restriction, and maximum standardized uptake value (SUVmax) with histopathological tumor grading in patients with neuroendocrine neoplasms (NEN).

MATERIAL AND METHODS

A total of 26 patients with newly diagnosed, therapy-naive neuroendocrine neoplasms (NEN) were enrolled in this prospective study and underwent ⁶⁸Ga-DOTATOC PET/MRI. Images were evaluated regarding NEN lesion number and location, predominant tumor signal intensity on precontrast T1w and T2w images and on postcontrast arterial and portal venous phase T1w images, apparent diffusion coefficient (ADC) and SUVmax. Histopathological tumor grading was assessed and related to PET/MRI features using Pearson's correlation coefficient and Fisher's exact t-test. A binary logistic regression analysis was performed to evaluate a potential relation with an aggressive tumor biology and odds ratios (OR) were calculated.

RESULTS

There was a moderate negative correlation between arterial contrast enhancement and tumor grading (r=-0.35, p = 0.005), while portal venous enhancement showed a weak positive correlation with the Ki-67 index (r = 0.28, p = 0.008) and a non-significant positive correlation with tumor grading (r = 0.19, p = 0.063). Features that were significantly associated with an aggressive tumor biology were the presence of liver metastases (OR 2.6, p = 0.042), T1w hyperintensity in comparison to muscle (OR 12.7, p = 0.0001), arterial phase hyperenhancement (OR 1.4, p = 0.001), diffusion restriction (OR 2.8, p = 0.02) and SUVmax above the hepatic level (OR 7.0, p = 0.001).

CONCLUSION

The study reveals that PET/MRI features might be useful for prediction of NEN grading and thus provide a preliminary assessment of tumor aggressiveness.

Textural analysis of hybrid DOTATOC-PET/MRI and its association with histological grading in patients with liver metastases from neuroendocrine tumors

AIMS

Neuroendocrine tumors (NETs) are known to overexpress somatostatin receptors (SSTR), which can be visualized by DOTATOC-PET. Reduced SSTR expression on the other hand may indicate dedifferentiation. The aim of this retrospective study was to assess, if conventional PET parameters and textural features (TF) derived from simultaneous PET and MRI including apparent diffusion coefficient (ADC) are associated with the proliferative activity of NETs, potentially allowing non-invasive tumor grading.

METHODS

Our institutional database was screened for patients with NET and liver metastases >1 cm. We assessed conventional PET parameters, such as maximum and mean standardized uptake value and more elaborate TF parameters from PET and ADC-MRI (including entropy and homogeneity) from up to the five largest liver lesions per patient. The association of proliferative activity as measured by Ki67-/MIB1-index with the aforementioned parameters was analyzed.

RESULTS

One hundred patients with NET/NECs were eligible with a Ki67-index ranging from <1% to 30%. Overall, 304 liver lesions were analyzed. Conventional PET parameters, entropy, homogeneity of PET and ADC maps differed significantly between G1 and G2 NETs. However, Spearman's test showed a weak association (r = -0.23 to 0.31).

DISCUSSION

In our study cohort, conventional PET parameters and TF of PET and ADC-MRI showed only a weak correlation with Ki67. This indicates that in patients with a Ki67-index of up to 30% TF analysis of combined PET/MRI may not be reliably used for accurate non-invasive tumor grading. On the other hand, DOTATOC-PET might be a suitable staging tool in some higher grade NET/NECs.

Pancreatic neuroendocrine tumours: spectrum of imaging findings

Pancreatic neuroendocrine tumours (pNETs) are rare and heterogeneous group of neoplasms presenting with a wide variety of symptoms and biological behaviour, from indolent to aggressive ones. pNETs are stratified into functional or non-functional, because of their ability to produce metabolically active hormones. pNETs can be an isolate phenomenon or a part of a hereditary syndrome like von Hippel-Lindau syndrome or neurofibromatosis-1. The incidence has increased in the last years, also because of the improvement of cross-sectional imaging. Computed tomography (CT), magnetic resonance imaging (MRI) and functional imaging are the mainstay imaging modalities used for tumour detection and disease extension assessment, due to easy availability and better contrast/spatial resolution. Radiological imaging plays a fundamental role in detection, characterization and surveillance of pNETs and is involved in almost every stage of patients' management. Moreover, with specific indications and techniques, interventional radiology can also play a role in therapeutic management. Surgery is the treatment of choice, consisting of either partial pancreatectomy or enucleation of the primary tumour. This article reviews the radiologic features of different pNETs as well as imaging mimics, in order to help radiologists to avoid potential pitfalls, to reach the correct diagnosis and to support the multidisciplinary team in establishing the right treatment.

Correlation of MR features and histogram-derived parameters with aggressiveness and outcomes after resection in pancreatic ductal adenocarcinoma

PURPOSE

To evaluate MR-derived histogram parameters in predicting aggressiveness and surgical outcomes in patients with PDAC, by correlating them to pathological features, recurrence-free survival (RFS), and overall survival (OS).

METHODS

Pre-operative MR examinations of 103 patients with PDAC between July 2014 and September 2018 were retrospectively analyzed. Morphologic features and whole-tumor histogram-derived parameters were correlated to pathological features using Fisher's exact or Mann-Whitney U tests and receiver operating characteristic (ROC) curves were constructed for significant parameters. Cox regression analysis and Kaplan-Meier curves were used to determine the association of clinical-pathological variables, morphological features, and histogram-derived parameters with RFS and OS.

RESULTS

T1_entropy, ADC_entropy, T2_kurtosis, and ADC_uniformity had the highest area under the curve (AUC) for prediction of vascular infiltration, nodal metastases, microscopic vascular invasion, and peripancreatic fat invasion (.657, .742, .760, and .818, respectively). Poor tumor differentiation (P = 0.002, hazard ratio-HR = 4.08), nodal ratio (P = 0.034, HR 6.95), and ADC_maximum (P = 0.021, HR 1.01) were significant predictors of RFS. Poor tumor differentiation (P = 0.05, HR 2.82), ADC_uniformity (P = 0.02, HR 3.32), and arterial_entropy (P = 0.02, HR 6.84) were the only significant predictors of death; patients with higher arterial_entropy had significantly shorter OS than patients who did not meet this criterion (P = 0.02; median OS 24 vs 31 months).

CONCLUSION

Histogram-derived parameters may predict adverse pathological features in PDACs. High arterial_entropy seems to be associated with short OS after surgery in patients with PDAC.

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.

Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps

PURPOSE

To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2-3) in patients with pancreatic neuroendocrine tumor (PNET).

MATERIALS AND METHODS

Thirty-three PNETs (22 G1 and 11 G2-3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs).

RESULTS

G2-G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively.

CONCLUSION

Texture analysis may aid in differentiating between G1 and G2-3 PNET.

Diffusion-Weighted MRI Predicts Lymph Node Metastasis and Tumor Aggressiveness in Resectable Pancreatic Neuroendocrine Tumors

OBJECTIVES

The aim of this study was to identify whether diffusion-weighted magnetic resonance imaging (DW-MRI) can predict the malignant behavior of preoperative well-differentiated pancreatic neuroendocrine tumors (PanNETs).

METHOD

Forty patients with PanNETs who underwent pancreatectomy were enrolled in this study. The apparent diffusion coefficient (ADC) values were measured. Clinicopathological factors were compared in patients with high ADC and low ADC values and in patients with and without lymph node metastasis (LNM).

RESULT

The low ADC group was significantly associated with higher Ki-67 index, higher mitotic count, larger tumor size, higher rate of LNM, and venous invasion. In patients with low ADC values, the incidence of LNMs was 33.3%. In patients with high ADC values, there were no patients with LNM being 0%. A significant negative correlation was found between the mean ADC values and the Ki-67 index and between the mean ADC values and the mitotic count. In multivariate analysis, neural invasion and mean ADC values ≤ 1458 were independent predictors of LNM.

CONCLUSION

ADC values obtained using DW-MRI in the preoperative assessment of patients with PanNETs might be a useful predictor of malignant potential, especially LNM.

Liver Tumor Burden in Pancreatic Neuroendocrine Tumors: CT Features and Texture Analysis in the Prediction of Tumor Grade and 18F-FDG Uptake

Pancreatic neuroendocrine tumors (p-NETs) are a rare group of neoplasms that often present with liver metastases. Histological characteristics, metabolic behavior, and liver tumor burden (LTB) are important prognostic factors. In this study, the usefulness of texture analysis of liver metastases in evaluating the biological aggressiveness of p-NETs was assessed. Fifty-six patients with liver metastases from p-NET were retrospectively enrolled. Qualitative and quantitative CT features of LTB were evaluated. Histogram-derived parameters of liver metastases were calculated and correlated with the tumor grade (G) and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) standardized uptake value (SUV). Arterial relative enhancement was inversely related with G (−0.37, p = 0.006). Different metastatic spread patterns of LTB were not associated with histological grade. Arterial_entropy was significantly correlated to G (−0.368, p = 0.038) and to Ki67 percentage (−0.421, p = 0.018). The ROC curve for the Arterial_entropy reported an area under the curve (AUC) of 0.736 (95% confidence interval 0.545–0.928, p = 0.035) in the identification of G1–2 tumors. Arterial_uniformity values were correlated to G (0.346, p = 0.005) and Ki67 levels (0.383, p = 0.033). Arterial_entropy values were directly correlated with the SUV (0.449, p = 0.047) which was inversely correlated with Arterial_uniformity (−0.499, p = 0.025). Skewness and kurtosis reported no significant correlations. In conclusion, histogram-derived parameters may predict adverse histological features and metabolic behavior of p-NET liver metastases.

MRI of the Pancreas

MRI has played a critical role in the evaluation of patients with pancreatic pathologies, from screening of patients at high risk for pancreatic cancer to the evaluation of pancreatic cysts and indeterminate pancreatic lesions. The high mortality associated with pancreatic adenocarcinomas has spurred much interest in developing effective screening tools, with MRI using magnetic resonance cholangiopancreatography (MRCP) playing a central role in the hopes of identifying cancers at earlier stages amenable to curative resection. Ongoing efforts to improve the resolution and robustness of imaging of the pancreas using MRI may thus one day reduce the mortality of this deadly disease. However, the increasing use of cross-sectional imaging has also generated a concomitant clinical conundrum: How to manage incidental pancreatic cystic lesions that are found in over a quarter of patients who undergo MRCP. Efforts to improve the specificity of MRCP for patients with pancreatic cysts and with indeterminate pancreatic masses may be achieved with continued technical advances in MRI, including diffusion-weighted and T₁ -weighted dynamic contrast-enhanced MRI. However, developments in quantitative MRI of the pancreas remain challenging, due to the small size of the pancreas and its upper abdominal location, adjacent to bowel and below the diaphragm. Further research is needed to improve MRI of the pancreas as a clinical tool, to positively affect the lives of patients with pancreatic abnormalities. This review focuses on various MR techniques such as MRCP, quantitative imaging, and dynamic contrast-enhanced imaging and their clinical applicability in the imaging of the pancreas, with an emphasis on pancreatic malignant and premalignant lesions. Level of Evidence 5 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:347-359.

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.

Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy

PURPOSE

To evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy.

METHODS

Forty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics' diagnostic performance for lesion histology, T-stage, N-stage, and grade.

RESULTS

Whole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers (P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean_0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean_10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean_25-50) (AUC = 0.829-0.829). For mean_10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10^-3 mm²/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N₁ than N₀ for both readers (P ≤ .043) were mean_0-10 (AUC = 0.789-0.822) and mean_10-25 (AUC = 0.800-0.822). For mean_10-25 (metric with highest AUC for identifying N₀), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage (P > .195) or grade (P > .215).

CONCLUSION

Volumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean_10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.

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.

Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance

Purpose

The purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance.

Patients and methods

PNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher’s exact tests or Kruskal–Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades.

Results

There were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703–0.989.

Conclusion

Our data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.

CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms

To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors.

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.

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.

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.

A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors

PURPOSE

The purpose of this study is to develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (pNET).Experimental Design: A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n = 51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction. The Mann-Whitney U test and least absolute shrinkage and selection operator regression were applied for feature selection and radiomics signature construction. A combined nomogram model was developed by incorporating the radiomics signature with clinical factors. The association between the nomogram model and the Ki-67 index and rate of nuclear mitosis were also investigated respectively. The utility of the proposed model was evaluated using the ROC, area under ROC curve (AUC), calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was used for survival analysis.

RESULTS

An eight-feature-combined radiomics signature was constructed as a tumor grade predictor. The nomogram model combining the radiomics signature with clinical stage showed the best performance (training set: AUC = 0.907; validation set: AUC = 0.891). The calibration curve and DCA demonstrated the clinical usefulness of the proposed nomogram. A significant correlation was observed between the developed nomogram and Ki-67 index and rate of nuclear mitosis, respectively. The KM analysis showed a significant difference between the survival of predicted grade 1 and grade 2/3 groups (P = 0.002).

CONCLUSIONS

The combined nomogram model developed could be useful in differentiating grade 1 and grade 2/3 tumor in patients with pNETs.

The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis

BACKGROUND

Imaging findings for pancreatic neuroendocrine carcinoma (PNEC) and pancreatic ductal adenocarcinoma (PDAC) often overlap. The aim of this study was to demonstrate the value of computed tomography (CT) imaging features and texture analysis to differentiate PNEC from PDAC.

METHODS

Twenty-eight patients with pathologically-proved PDAC and 14 patients with PNEC were included in this study. CT imaging findings, including tumor boundary, size, enhancement degree, duct dilatation and parenchymal atrophy were used to compare PDAC and PNEC. CT texture features were extracted from CT images at the arterial and portal phases.

RESULTS

More PNEC than PDAC had well-defined margins (57.1% vs 25.0%, p = 0.04). Parenchymal atrophy was more common in PDAC than in PNEC (67.9% vs 28.1%, p = 0.02). CT attenuation values (HU) and contrast ratios of PNEC inthe arterial and portal phases were higher than those of PDAC (p < 0.05 or 0.01). Entropy was lower and uniformity was higher in PNEC compare to PDAC at the arterial phase (p < 0.05). Contrast ratio showed the highest area under curve (AUC) for differentiating PNEC from PDAC (AUC = 0.98-0.99). Entropy and uniformity also showed an acceptable AUC (0.71-0.72).

CONCLUSIONS

Our data indicate that CT imaging features, including tumor margin, enhanced degree and parenchymal atrophy, as well as texture parameters can aid in the differentiation of PNEC from PDAC.

Volumetric Apparent Diffusion Coefficient Histogram Analysis in Differentiating Intrahepatic Mass-Forming Cholangiocarcinoma From Hepatocellular Carcinoma

BACKGROUND

Accurate differentiation between intrahepatic mass-forming cholangiocarcinoma (IMCC) and hepatocellular carcinoma (HCC) is needed because treatment and prognosis differ significantly.

PURPOSE

To explore whether volumetric apparent diffusion coefficient (ADC) histogram analysis can provide additional value to dynamic enhanced MRI in differentiating IMCC from HCC.

STUDY TYPE

Retrospective.

POPULATION

In all, 131 patients with pathologically proven IMCC (n = 33) or HCC (n = 98).

FIELD STRENGTH/SEQUENCE

3.0T MRI/conventional T₁ -weighted imaging (T₁ WI), T₂ WI, and diffusion-weighted imaging (DWI) with b value of 800 sec/mm² , dynamic enhanced MRI with gadobenate dimeglumine.

ASSESSMENT

Dynamic enhanced MR images were analyzed by two independent reviewers using a five-point scale to determine the diagnosis. Volumetric ADC assessments were performed independently by two radiologists to obtain different histogram parameters for each lesion. Quantitative histogram parameters were compared between the IMCC group and HCC group. Diagnostic performance of dynamic enhanced MRI, volumetric ADC histogram analysis, and the combination of both were analyzed.

STATISTICAL TESTS

Intraclass correlation coefficient (ICC) analysis, independent Student's t-test, or Mann-Whitney U-test, receiver operator characteristic (ROC) curves analysis, and McNemar test.

RESULTS

The sensitivity and specificity for dynamic enhanced MRI to differentiate IMCC from HCC were 82.1% and 82.6%, respectively. For all volumetric ADC histogram parameters, the 75th percentile ADC (ADC_75% ) had the highest AUC (0.791) in differentiating IMCC from HCC, with sensitivity and specificity of 69.7% and 77.6%, respectively. When combining dynamic enhanced MRI with ADC_75% , the sensitivity and specificity were 82.1% and 91.9%, respectively. Compared to dynamic enhanced MRI alone, the specificity for combined dynamic enhanced MRI and ADC_75% was significantly increased (P = 0.008).

DATA CONCLUSION

Volumetric ADC histogram analysis provides additional value to dynamic enhanced MRI in differentiating IMCC from HCC.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:975-983.

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.

Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?

OBJECTIVES

To evaluate MRI derived whole-tumour histogram analysis parameters in predicting pancreatic neuroendocrine neoplasm (panNEN) grade and aggressiveness.

METHODS

Pre-operative MR of 42 consecutive patients with panNEN >1 cm were retrospectively analysed. T1-/T2-weighted images and ADC maps were analysed. Histogram-derived parameters were compared to histopathological features using the Mann-Whitney U test. Diagnostic accuracy was assessed by ROC-AUC analysis; sensitivity and specificity were assessed for each histogram parameter.

RESULTS

ADC_entropy was significantly higher in G2-3 tumours with ROC-AUC 0.757; sensitivity and specificity were 83.3 % (95 % CI: 61.2-94.5) and 61.1 % (95 % CI: 36.1-81.7). ADC_kurtosis was higher in panNENs with vascular involvement, nodal and hepatic metastases (p= .008, .021 and .008; ROC-AUC= 0.820, 0.709 and 0.820); sensitivity and specificity were: 85.7/74.3 % (95 % CI: 42-99.2 /56.4-86.9), 36.8/96.5 % (95 % CI: 17.2-61.4 /76-99.8) and 100/62.8 % (95 % CI: 56.1-100/44.9-78.1). No significant differences between groups were found for other histogram-derived parameters (p >.05).

CONCLUSIONS

Whole-tumour histogram analysis of ADC maps may be helpful in predicting tumour grade, vascular involvement, nodal and liver metastases in panNENs. ADC_entropy and ADC_kurtosis are the most accurate parameters for identification of panNENs with malignant behaviour.

KEY POINTS

• Whole-tumour ADC histogram analysis can predict aggressiveness in pancreatic neuroendocrine neoplasms. • ADC entropy and kurtosis are higher in aggressive tumours. • ADC histogram analysis can quantify tumour diffusion heterogeneity. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information for prognostication.

Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: Comparison with ER-positive subtype

Purpose

To investigate associations between the clinicopathologic features and MRI features of triple-negative breast cancer (TNBC) and ER-positive breast cancer (BC) via apparent diffusion coefficient (ADC) histogram analysis.

Materials and methods

In this study, 221 breast cancer patients with pre-operative MRI performed from August 2009 to March 2015 were included in a retrospective analysis. All patients had a pathologically confirmed diagnosis of invasive carcinoma and were grouped into ER-positive (149) or triple-negative (72) subtypes. DWI rim sign and various ADC parameters (mean; mode; 25, 50, and 75 percentiles; skewness; and kurtosis) between ER-positive and TNBC were compared using whole-lesion ADC histogram analysis. Univariate and multivariate regression analyses were used for statistical comparison.

Results

DWI rim signs were detected in 42.3% and 41.7% of ER-positive subtype and TNBC, respectively (P = 0.931). TNBC had poorer histologic grade (P<0.001) and higher Ki-67 expression (P <0.001) than ER-positive subtype BC. TNBC displayed higher ADC parameters (mean, mode, 50th & 75th percentiles, kurtosis on univariate analysis, all P<0.001; only kurtosis on multivariate anaylsis; P<0.001) than ER-positive subtype BC. TNBC had significantly more recurrence events than ER-positive subtype BC on univarate analysis (9.7% (7/72) vs. 2.7% (4/149), P = 0.035).

Conclusion

Poorer clinicopathologic outcomes were found in TNBC. Whole-lesion ADC histogram analysis revealed ADC kurtosis to be higher in TNBC than ER-positive subtype BC.

Pancreatic neuroendocrine tumor: Correlations between MRI features, tumor biology, and clinical outcome after surgery

PURPOSE

To assess which magnetic resonance imaging (MRI) features are associated with pNETs (pancreatic neuroendocrine tumors) grade based on the WHO classification, as well as identify MRI features related to disease progression after surgery.

MATERIALS AND METHODS

In this Institutional Review Board (IRB)-approved study, 1.5T and 3.0T MRI scans of 80 patients with surgically verified pNETs were assessed. The images were evaluated for tumor location; size; pattern; predominant signal intensity on precontrast T₁ - and T₂ -weighted images, as well as on postcontrast arterial and portal venous phase T₁ -weighted sequences; presence of pancreatic duct dilatation; pancreatic atrophy; restricted diffusion; vascular involvement by the tumor; extrapancreatic tumor spread; and synchronous liver metastases. Tumors were graded based on the WHO classification and patients were followed-up with computed tomography (CT) or MRI after surgical resection. Data were analyzed with Student's t and chi-square tests, logistic regression, and Kaplan-Meier curves.

RESULTS

The MRI features that were associated with aggressive tumors were: size >2.0 cm (odds ratio [OR] = 4.8, P = 0.002), "T₂ nonbright lesions" on T₂ -weighted images (OR = 4.6, P = 0.008), presence of pancreatic ductal dilatation (OR = 4.9, P = 0.024), and restricted diffusion within the lesion (OR = 4.9, P = 0.013). Differences in progression-free survival distribution were found for patients whose pNETs were associated with the following MRI features: size >2.0 cm (χ² (1) = 6.0, P = 0.014), "nonbright lesions" on T₂ -weighted images (χ² (1) = 6.8, P = 0.009), and presence of pancreatic duct dilatation (χ² (1) = 10.9, P = 0.001).

CONCLUSION

MRI features can be used to assess pNETs aggressiveness and identify patients at risk for early disease progression after surgical resection.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:425-432.

Pancreatic tumors in children and young adults with tuberous sclerosis complex

BACKGROUND

Pancreatic neuroendocrine tumors are not included in the diagnostic criteria for tuberous sclerosis complex, although an association has been described.

OBJECTIVE

To investigate the association of pancreatic neuroendocrine tumor in children and young adults with tuberous sclerosis complex and define MRI characteristics of the tumor.

MATERIALS AND METHODS

We retrospectively evaluated the abdominal MRI scans of 55 children and young adults with tuberous sclerosis complex for the presence of a pancreatic mass. The scans were performed over a period of 7 years to monitor renal pathology. We obtained each patient's clinical history and treatment protocol from the hospital's electronic medical records.

RESULTS

A solid pancreatic mass was identified in 5/55 (9%, 95% confidence interval [CI] 3-20%) patients (4 male) with a mean age of 12.6 years. Four of the lesions were located in the pancreatic tail and one in the pancreatic body. All of the lesions were solid, ovoid and well demarcated, with a mean diameter of 3.1 cm. The masses uniformly demonstrated T1 and T2 prolongation, but their diffusion behavior and post-contrast enhancement varied. The two surgically resected lesions were synaptophysin (+) non-functional pancreatic neuroendocrine tumors on pathology. Two of the patients who did not have surgery were treated with everolimus; one of the lesions has shown interval decrease in size and the other has remained stable.

CONCLUSION

Pancreatic tumor is relatively common in children and young adults with tuberous sclerosis complex.

Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: Comparison with ER-positive subtype

PURPOSE

To investigate associations between the clinicopathologic features and MRI features of triple-negative breast cancer (TNBC) and ER-positive breast cancer (BC) via apparent diffusion coefficient (ADC) histogram analysis.

MATERIALS AND METHODS

In this study, 221 breast cancer patients with pre-operative MRI performed from August 2009 to March 2015 were included in a retrospective analysis. All patients had a pathologically confirmed diagnosis of invasive carcinoma and were grouped into ER-positive (149) or triple-negative (72) subtypes. DWI rim sign and various ADC parameters (mean; mode; 25, 50, and 75 percentiles; skewness; and kurtosis) between ER-positive and TNBC were compared using whole-lesion ADC histogram analysis. Univariate and multivariate regression analyses were used for statistical comparison.

RESULTS

DWI rim signs were detected in 42.3% and 41.7% of ER-positive subtype and TNBC, respectively (P = 0.931). TNBC had poorer histologic grade (P<0.001) and higher Ki-67 expression (P <0.001) than ER-positive subtype BC. TNBC displayed higher ADC parameters (mean, mode, 50th & 75th percentiles, kurtosis on univariate analysis, all P<0.001; only kurtosis on multivariate anaylsis; P<0.001) than ER-positive subtype BC. TNBC had significantly more recurrence events than ER-positive subtype BC on univarate analysis (9.7% (7/72) vs. 2.7% (4/149), P = 0.035).

CONCLUSION

Poorer clinicopathologic outcomes were found in TNBC. Whole-lesion ADC histogram analysis revealed ADC kurtosis to be higher in TNBC than ER-positive subtype BC.

Additional value of hybrid SPECT/CT systems in neuroendocrine tumors, adrenal tumors, pheochromocytomas and paragangliomas

The aim of this review was to evaluate the potential advantages of SPECT/CT hybrid imaging in the management of neuroendocrine tumors, adrenal tumors, pheochromocytomas and paragangliomas. From the collected data, the superiority of fused images was observed as providing both functional/molecular and morphological imaging compared to planar imaging. This provided an improvement in diagnostic imaging, with significant advantages as regards: (1) precise locating of the lesions; (2) an improvement in characterization of the findings, resulting higher specificity, improved sensitivity, and overall greater accuracy, (3) additional anatomical information derived from the CT component; (4) CT-based attenuation correction and potential for volumetric dosimetry calculations, and (5) improvement on the impact on patient management (e.g. in better defining treatment plans, in shortening surgical operating times). It can be concluded that SPECT/CT hybrid imaging provides the nuclear medicine physician with a powerful imaging modality in comparison to planar imaging, providing essential information about the location of lesions, and high quality homogeneous images.

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.

Radiological indeterminate vestibular schwannoma and meningioma in cerebellopontine angle area: differentiating using whole-tumor histogram analysis of apparent diffusion coefficient

PURPOSE

To assess the role of whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating radiological indeterminate vestibular schwannoma (VS) from meningioma in cerebellopontine angle (CPA).

MATERIALS AND METHODS

Diffusion-weighted (DW) images (b = 0 and 1000 s/mm²) of pathologically confirmed and radiological indeterminate CPA meningioma (CPAM) (n = 27) and VS (n = 12) were retrospectively collected and processed with mono-exponential model. Whole-tumor regions of interest were drawn on all slices of the ADC maps to obtain histogram parameters, including the mean ADC (ADC_mean), median ADC (ADC_median), 10th/25th/75th/90th percentile ADC (ADC₁₀, ADC₂₅, ADC₇₅ and ADC₉₀), skewness and kurtosis. The differences of ADC histogram parameters between CPAM and VS were compared using unpaired t-test. Multiple receiver operating characteristic (ROC) curves analysis was used to determine and compare the diagnostic value of each significant parameter.

RESULTS

Significant differences were found on the ADC_mean, ADC_median, ADC₁₀, ADC₂₅, ADC₇₅ and ADC₉₀ between CPAM and VS (all p values < 0.001), while no significant difference was found on kurtosis (p = 0.562) and skewness (p = 0.047). ROC curves analysis revealed, a cut-off value of 1.126 × 10^-3 mm²/s for the ADC₉₀ value generated highest area under curves (AUC) for differentiating CPAM from VS (AUC, 0.975; sensitivity, 100%; specificity, 88.9%).

CONCLUSIONS

Histogram analysis of ADC maps based on whole tumor can be a useful tool for differentiating radiological indeterminate CPAM from VS. The ADC₉₀ value was the most promising parameter for differentiating these two entities.

Gastroenteropancreatic neuroendocrine tumours (GEP-NET) - Imaging and staging

Detection of gastroenteropancreatic neuroendocrine tumours (GEP-NETs) and monitoring of treatment response relies mainly on morphological imaging such as computed tomography (CT) and magnetic resonance imaging (MRI). Molecular imaging techniques also in combination with CT (hybrid imaging) greatly benefit patient management, including better localization of occult tumours and better staging. Somatostatin receptor scintigraphy (SRS) and somatostatin receptor (SSTR) positron emission tomography (PET) play a central role in the diagnostic work-up of patients with well-differentiated GEP-NETs. SSTR PET/CT is superior to SRS and should be used whenever available. (18)F-DOPA and (18)F-FDG PET/CT is inferior to SSTR PET/CT at least in patients with well-differentiated GEP-NETs. Both SSTR PET/CT and SRS have limitations, such as relatively low detection rate of benign insulinomas, poorly differentiated GEP-NETs and liver metastases. New innovations such as SSTR PET/MRI, radiolabelled SSTR antagonists and glucagon-like peptide-1 receptor (GLP-1R) agonists might further improve imaging of GEP-NETs.