Intravoxel incoherent motion diffusion-weighted imaging of pancreatic neuroendocrine tumors: prediction of the histologic grade using pure diffusion coefficient and tumor size. Invest Radiol. 2014;49:396-402. [PMID: 24500090 DOI: 10.1097/rli.0000000000000028]

An Illustrated Review of the Recent 2019 World Health Organization Classification of Neuroendocrine Neoplasms: A Radiologic and Pathologic Correlation

ABSTRACT

Recent advances in molecular pathology and an improved understanding of the etiology of neuroendocrine neoplasms (NENs) have given rise to an updated World Health Organization classification. Since gastroenteropancreatic NENs (GEP-NENs) are the most common forms of NENs and their incidence has been increasing constantly, they will be the focus of our attention. Here, we review the findings at the foundation of the new classification system, discuss how it impacts imaging research and radiological practice, and illustrate typical and atypical imaging and pathological findings. Gastroenteropancreatic NENs have a highly variable clinical course, which existing classification schemes based on proliferation rate were unable to fully capture. While well- and poorly differentiated NENs both express neuroendocrine markers, they are fundamentally different diseases, which may show similar proliferation rates. Genetic alterations specific to well-differentiated neuroendocrine tumors graded 1 to 3 and poorly differentiated neuroendocrine cancers of small cell and large-cell subtype have been identified. The new tumor classification places new demands and creates opportunities for radiologists to continue providing the clinically most relevant report and on researchers to design projects, which continue to be clinically applicable.

Advancements in Neuroendocrine Neoplasms: Imaging and Future Frontiers

Neuroendocrine neoplasms (NENs) are a diverse group of tumors with varying clinical behaviors. Their incidence has risen due to increased awareness, improved diagnostics, and aging populations. The 2019 World Health Organization classification emphasizes integrating radiology and histopathology to characterize NENs and create personalized treatment plans. Imaging methods like CT, MRI, and PET/CT are crucial for detection, staging, treatment planning, and monitoring, but each of them poses different interpretative challenges and none are immune to pitfalls. Treatment options include surgery, targeted therapies, and chemotherapy, based on the tumor type, stage, and patient-specific factors. This review aims to provide insights into the latest developments and challenges in NEN imaging, diagnosis, and management.

Quantitative Magnetic Resonance Imaging for the Pancreas: Current Status

ABSTRACT

Magnetic resonance imaging (MRI) is important for evaluating pancreatic disorders, and anatomical landmarks play a major role in the interpretation of results. Quantitative MRI is an effective diagnostic modality for various pathologic conditions, as it allows the investigation of various physical parameters. Recent advancements in quantitative MRI techniques have significantly improved the accuracy of pancreatic MRI. Consequently, this method has become an essential tool for the diagnosis, treatment, and monitoring of pancreatic diseases. This comprehensive review article presents the currently available evidence on the clinical utility of quantitative MRI of the pancreas.

Prediction of the lymphatic, microvascular, and perineural invasion of pancreatic neuroendocrine tumors using preoperative magnetic resonance imaging

BACKGROUND

Significant correlation between lymphatic, microvascular, and perineural invasion (LMPI) and the prognosis of pancreatic neuroendocrine tumors (PENTs) was confirmed by previous studies. There was no previous study reported the relationship between magnetic resonance imaging (MRI) parameters and LMPI.

AIM

To determine the feasibility of using preoperative MRI of the pancreas to predict LMPI in patients with non-functioning PENTs (NFPNETs).

METHODS

A total of 61 patients with NFPNETs who underwent MRI scans and lymphadenectomy from May 2011 to June 2018 were included in this retrospective study. The patients were divided into group 1 (n = 34, LMPI negative) and group 2 (n = 27, LMPI positive). The clinical characteristics and qualitative MRI features were collected. In order to predict LMPI status in NF-PNETs, a multivariate logistic regression model was constructed. Diagnostic performance was evaluated by calculating the receiver operator characteristic (ROC) curve with area under ROC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.

RESULTS

There were significant differences in the lymph node metastasis stage, tumor grade, neuron-specific enolase levels, tumor margin, main pancreatic ductal dilatation, common bile duct dilatation, enhancement pattern, vascular and adjacent tissue involvement, synchronous liver metastases, the long axis of the largest lymph node, the short axis of the largest lymph node, number of the lymph nodes with short axis > 5 or 10 mm, and tumor volume between two groups (P < 0.05). Multivariate analysis showed that tumor margin (odds ratio = 11.523, P < 0.001) was a predictive factor for LMPI of NF-PNETs. The area under the receiver value for the predictive performance of combined predictive factors was 0.855. The sensitivity, specificity, PPV, NPV and accuracy of the model were 48.1% (14/27), 97.1% (33/34), 97.1% (13/14), 70.2% (33/47) and 0.754, respectively.

CONCLUSION

Using preoperative MRI, ill-defined tumor margins can effectively predict LMPI in patients with NF-PNETs.

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.

The diagnostic value of intravoxel incoherent motion diffusion-weighted imaging for distinguishing nonhypervascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas

PURPOSE

To primarily evaluate the diagnostic performance of the monoexponential and intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI) models for differentiating between nonhypervascular pancreatic neuroendocrine tumors (PNETs) and pancreatic ductal adenocarcinomas (PDACs).

METHODS

63 patients with PNETs (35 nonhypervascular PNETs and 28 hypervascular PNETs) and 164 patients with PDACs were retrospectively enrolled in the study and underwent multiple b-value DWI. Intraobserver and interobserver reliabilities of DWI parameters were assessed by using the intraclass correlation coefficient (ICC). The parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) of nonhypervascular PNETs were compared with PDACs and hypervascular PNETs using the independent sample t test or the Mann-Whitney U test. The diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis.

RESULTS

All DWI parameters values showed good to excellent intra- and interobserver agreements (ICC = 0.743-0.873). Nonhypervascular PNETs had significantly lower ADC and D, but significantly higher f than PDACs (P = 0.005, P < 0.001 and P < 0.001, respectively). ADC, D and f of nonhypervascular PNETs were lower than hypervascular PNETs (P = 0.001, <0.001 and 0.093, respectively). D* of nonhypervascular PNETs showed no statistically significant differences with PDACs and hypervascular PNETs (P = 0.809 and 0.420). D showed a higher area under the curve (AUC), followed by ADC and f (AUC = 0.885, 0.665 and 0.740, respectively) in differentiating nonhypervascular PNETs from PDACs.

CONCLUSION

Monoexponential and IVIM diffusion models are valuable to differentiate nonhypervascular PNETs from PDACs. D showed better performance than f and ADC.

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.

Characterization and correction of cardiovascular motion artifacts in diffusion-weighted imaging of the pancreas

PURPOSE

To assess the effects of cardiovascular-induced motion on conventional DWI of the pancreas and to evaluate motion-robust DWI methods in a motion phantom and healthy volunteers.

METHODS

3T DWI was acquired using standard monopolar and motion-compensated gradient waveforms, including in an anatomically accurate pancreas phantom with controllable compressive motion and healthy volunteers (n = 8, 10). In volunteers, highly controlled single-slice DWI using breath-holding and cardiac gating and whole-pancreas respiratory-triggered DWI were acquired. For each acquisition, the ADC variability across volunteers, as well as ADC differences across parts of the pancreas were evaluated.

RESULTS

In motion phantom scans, conventional DWI led to biased ADC, whereas motion-compensated waveforms produced consistent ADC. In the breath-held, cardiac-triggered study, conventional DWI led to heterogeneous DW signals and highly variable ADC across the pancreas, whereas motion-compensated DWI avoided these artifacts. In the respiratory-triggered study, conventional DWI produced heterogeneous ADC across the pancreas (head: 1756 ± 173 × 10^-6 mm² /s; body: 1530 ± 338 × 10^-6 mm² /s; tail: 1388 ± 267 × 10^-6 mm² /s), with ADCs in the head significantly higher than in the tail (P < .05). Motion-compensated ADC had lower variability across volunteers (head: 1277 ± 102 × 10^-6 mm² /s; body: 1204 ± 169 × 10^-6 mm² /s; tail: 1235 ± 178 × 10^-6 mm² /s), with no significant difference (P ≥ .19) across the pancreas.

CONCLUSION

Cardiovascular motion introduces artifacts and ADC bias in pancreas DWI, which are addressed by motion-robust DWI.

Radiological Imaging of Gastro-Entero-Pancreatic Neuroendocrine Tumors. The Review of Current Literature Emphasizing the Diagnostic Value of Chosen Imaging Methods

Despite development of radiologic imaging, detection and follow-up of neuroendocrine neoplasms (NENs) still pose a diagnostic challenge, due to the heterogeneity of NEN, their relatively long-term growth, and small size of primary tumor. A set of information obtained by using different radiological imaging tools simplifies a choice of the most appropriate treatment method. Moreover, radiological imaging plays an important role in the assessment of metastatic lesions, especially in the liver, as well as, tumor response to treatment. This article reviews the current, broadly in use imaging modalities which are applied to the diagnosis of GEP-NETs, (the most common type of NENs) and put emphasis on the strengths and limitations of each modality.

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.

Histological Grade of Meningioma: Prediction by Intravoxel Incoherent Motion Histogram Parameters

RATIONALE AND OBJECTIVES

To evaluate the usefulness of intravoxel incoherent motion (IVIM) histogram analysis for differentiating low-grade meningiomas (LGMs) and high-grade meningiomas (HGMs).

MATERIALS AND METHODS

Fifty-nine patients with pathologically confirmed meningiomas (45 LGMs and 14 HGMs) underwent IVIM MR imaging. Maps of IVIM parameters (perfusion fraction, f; true diffusion coefficient, D; and pseudo diffusion coefficient, D*), as well as of the apparent diffusion coefficient (ADC), were generated. Histogram analysis was performed using parametric values from all voxels in regions-of-interest manually drawn to encompass the whole tumor. The histogram results of ADC and IVIM parameters were compared using the Mann-Whitney U test. Area under the receiver operating characteristic curve (AUC) values were generated to evaluate how well each parameter could differentiate LGMs from HGMs. Spearman's rank correlation coefficients were used to evaluate correlations between histogram parameters and Ki-67 expression.

RESULTS

Compared to LGM, HGM showed significantly higher standard deviation (SD), variance, and coefficient of variation (CV) of ADC (p< 0.006-0.028; AUC, 0.693-0.748), D (p< 0.004-0.032; AUC, 0.670-0.752), and significantly higher CV of f (p< 0.005-0.024; AUC = 0.737). Means and percentiles of ADC and IVIM parameters did not differ significantly between LGM and HGM. Significant positive correlations were identified between Ki-67 and histogram parameters of ADC (SD, variance, kurtosis, skewness, and CV) and D (SD, variance, kurtosis, and CV), whereas no significant correlation with Ki-67 was shown for mean or percentiles of ADC and IVIM parameters.

CONCLUSION

Heterogeneity histogram parameters of ADC, D, and f may be useful for differentiating LGMs from HGMs.

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.

The added value of intravoxel incoherent motion diffusion weighted imaging parameters in differentiating high-grade pancreatic neuroendocrine neoplasms from pancreatic ductal adenocarcinoma

The aim of the present study was to investigate the potential significance of intravoxel incoherent motion (IVIM)-diffusion weighted imaging (DWI) in differentiating high-grade pancreatic neuroendocrine neoplasms (pNENs) from pancreatic ductal adenocarcinoma (PDAC). A total of 50 patients, including 37 patients with PDAC and 13 patients with high-grade pNENs, underwent pancreatic multiple b-values DWI with 15 b-values including 0, 10, 20, 40, 60, 80, 100, 150, 200, 400, 800, 1,000, 1,200, 1,500 and 2,000 sec/mm². Standard apparent diffusion coefficient (ADC_standard) and IVIM parameter [slow apparent diffusion coefficient (D_slow), fast apparent diffusion coefficient (D_fast), fraction of fast apparent diffusion coefficient (ƒ)] values of PDAC and pNENs were compared. P<0.05 was considered to indicate a statistically significant difference. Receiver operating characteristics analysis was performed in order to evaluate the diagnostic potential of IVIM parameters for differentiating high-grade pNENs from PDAC. D_slow of pNENs was significantly lower compared with that of PDAC (0.460 vs. 0.579×10⁻³ mm²/sec; P=0.001). D_fast of pNENs was significantly higher compared with that of PDAC (13.361 vs. 4.985×10⁻³ mm²/sec; P<0.001). Area under the curve of D_slow, D_fast and combined D_slow and D_fast was 0.793, 0.863 and 0.885 respectively. The specificity and sensitivity of D_slow≤0.472×10⁻³ mm²/sec were 97.3 and 53.9%, respectively, for differentiating high-grade pNENs from PDAC. The specificity and sensitivity of D_fast >9.58×10⁻³ mm²/sec were 91.9 and 69.2%, respectively, for differentiating high-grade pNENs from PDAC. When D_slow and D_fast were combined, the specificity and sensitivity for differentiating high-grade pNENs from PDAC were 76.9 and 100%, respectively. Taken together, these results indicated that the diffusion-associated parameter D_slow and the perfusion-associated parameter D_fast of IVIM-DWI may differentiate high-grade pNENs from PDAC with high diagnostic accuracy, and that IVIM-DWI may be a valuable biomarker in differentiating pancreatic neoplasms.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

KEY POINTS

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

What Is New in the 2017 World Health Organization Classification and 8th American Joint Committee on Cancer Staging System for Pancreatic Neuroendocrine Neoplasms?

The diagnosis and management of pancreatic neuroendocrine neoplasms (NENs) have evolved significantly in recent years. There are several diagnostic and therapeutic challenges and controversies regarding the management of these lesions. In this review, we focus on the recent significant changes and controversial issues regarding the diagnosis and management of NENs and discuss the role of imaging in the multidisciplinary team approach.

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.

Functional imaging in pancreatic disease

In addition to the classical morphological evaluation of pancreatic disease, the constant technological advances in imaging techniques based fundamentally on computed tomography and magnetic resonance imaging have enabled the quantitative functional and molecular evaluation of this organ. In many cases, this imaging-based information results in substantial changes to patient management and can be a fundamental tool for the development of biomarkers. The aim of this article is to review the role of emerging functional and molecular techniques based on computed tomography and magnetic resonance imaging in the evaluation of pancreatic disease.

Increased diffusion and decreased perfusion in epiphyses of femoral heads detected via intravoxel incoherent motion after closed reduction in children with developmental dysplasia of the hip

Background Patients with developmental dysplasia of the hip (DDH) may have decreased blood supply to the femoral heads. Finding a non-invasive method to evaluate whether the femoral heads in patients with DDH are ischemic is paramount for orthopedic surgeons. Purpose To identify whether parameters reflecting perfusion and diffusion in intravoxel incoherent motion (IVIM) sequences can be used to assess ischemia in femoral heads in patients with DDH after closed reduction. Material and Methods Twenty-eight patients with DDH who had undergone closed reduction were enrolled. IVIM data were acquired using a 3-T magnetic resonance scanner, regions of interest were placed on the epiphyses; ADC_slow, ADC_fast, f, and ADC_fast×f were measured. A Mann-Whitney U test was performed to compare ADC_slow, ADC_fast, f, and ADC_fast×f between the lesion and control sides. Receiver operating characteristic curves were generated with respective cut-off values. The lesion sides were classified based on the International Hip Dysplasia Institute (IHDI) classification. ADC_slow, ADC_fast, f, and ADC_fast×f were compared among the groups. Results ADC_slow was higher and ADC_fast, f, and ADC_fast×f were lower on the lesion sides ( P = 0.000-0.002). The optimal cut-off value for ADC_fast×f, ADC_fast, ADC_slow, and f were 0.030, 0.626, 0.000251, and 0.636, respectively. Higher IHDI classification scores on the lesion side were associated with lower ADC_fast, f, and ADC_fast×f, and higher ADC_slow values. Conclusion IVIM is a promising method to investigate the perfusion and diffusion of epiphyses of femoral heads.

Meta-analysis of intravoxel incoherent motion magnetic resonance imaging in differentiating focal lesions of the liver

INTRODUCTION

Accurate detection and characterization of focal liver lesions, including differentiation between malignant and benign lesions, are particularly important. The objective of this meta-analysis was to evaluate the parameters of intravoxel incoherent motion (IVIM), including apparent diffusion coefficient (ADC), pure molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (f) in differentiating focal liver lesions.

METHODS

IVIM method employed for focal liver lesion and the quality assessment of diagnostic studies were evaluated. Standardized mean differences and 95% confidence intervals were calculated. The heterogeneity was quantified with the I statistic.

RESULTS

The difference between groups was analyzed according to the I values from 6 different studies using fixed effects or random effects models. Significant differences in ADC (P < .001) and D (P < .001) were observed between benign and malignant lesions. Moreover, significant differences in ADC (P < .001), D (P < .001), and f (P = .01) were found between hemangioma and hepatocellular carcinoma (HCC). In addition, no significant difference was observed between the metastases and HCC.

CONCLUSIONS

D and ADC values were useful for the differentiation between benignity and malignancy; higher values of ADC, D, and f were observed in hemangioma compared to HCC. Nevertheless, IVIM did not result as the optimal approach for differentiation between the metastases and HCC.

Quantitative parameters of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI): potential application in predicting pathological grades of pancreatic ductal adenocarcinoma

Background

The aim of this study was to compare intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) parameters such as standard apparent diffusion coefficient (ADC_standard), pure diffusion coefficient (D_slow), pseudodiffusion coefficient (D_fast) and perfusion fraction (ƒ) for differentiating pancreatic ductal adenocarcinoma (PDAC) with different pathological grades.

Methods

Institutional Review Board of our hospital approved this study protocol. Subjects comprised 38 PDACs confirmed by pathology. Pancreatic multiple b values DWI with 15 b values of 0, 10, 20, 40, 60, 80, 100, 150, 200, 400, 800, 1,000, 1200, 1,500, and 2,000 s/mm² was performed using GE Discovery MR750 3.0T scanner. ADC_standard, D_slow, D_fast and ƒ values of all PDACs were calculated using mono- and bi-exponential models. Parameters of well/moderately differentiated and poorly differentiated PDAC were compared using Independent Sample t-test. P values <0.05 were considered significant.

Results

Mean D_slow value of well/moderately differentiated PDAC was significantly lower than that of poorly differentiated PDAC (0.540×10^-3vs. 0.676×10^-3 mm²/s, P<0.001). Mean ƒ value of well/moderately differentiated PDAC was significantly higher than that of poorly differentiated PDAC (60.3% vs. 38.4%, P<0.001). The area under curve value of ƒ in differentiating well/moderately differentiated PDAC from poorly differentiated PDAC was slightly higher than that of D_slow (0.894>0.865). When the D_slow value was less than or equal to 0.599×10^-3 mm²/s, the sensitivity and specificity were 100% and 84.6% respectively. When ƒ value was greater than 49.6%, the sensitivity and specificity were 92.0% and 84.6% respectively.

Conclusions

D_slow and ƒ derived from IVIM-DWI model can be used to distinguish well/moderately differentiated PDAC from poorly differentiated PDAC. And to serve this purpose, D_slow and ƒ have high diagnostic performance. IVIM-DWI is a promising and non-invasive tool for predicting pathological grade of PDAC.

Intravoxel incoherent motion: application in differentiation of hepatocellular carcinoma and focal nodular hyperplasia

PURPOSE

We aimed to explore whether intravoxel incoherent motion (IVIM)-related parameters of hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) demonstrate differences that could be used to differentiate and improve diagnostic efficiency.

METHODS

A total of 27 patients, including 22 with HCC and 5 with FNH, underwent liver 3.0 T magnetic resonance imaging for routine sequences. They were concurrently examined by IVIM diffusion-weighted imaging (DWI) scanning with 11 different b values (0-800 s/mm2). IVIM-derived parameters, such as pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient (ADCtotal), were quantified automatically by post-processing software and compared between HCC and FNH groups. A receiver operating characteristic (ROC) curve was then created to predict their diagnostic value.

RESULTS

D* was weak in terms of reproducibility among the other parameters. ADCtotal, D, and D* were significantly lower in the HCC group than in the FNH group, while f did not show a significant difference. ADCtotal and D had the largest area under the curve values (AUC; 0.915 and 0.897, respectively) and similarly high efficacy to differentiate the two conditions.

CONCLUSION

IVIM provides a new modality to differentiate the HCC and FNH. ADCtotal and D demonstrated outstanding and comparable diagnosing utility.

Intravoxel Incoherent Motion MRI of Rectal Cancer: Correlation of Diffusion and Perfusion Characteristics With Prognostic Tumor Markers

OBJECTIVE

The objective of our study was to evaluate the intravoxel incoherent motion (IVIM)-DWI derived parameters and their relationships with tumor prognostic markers using 3-T MRI in patients with rectal cancer.

SUBJECTS AND METHODS

Fifty-two patients with histopathologically proven rectal cancer who underwent preoperative pelvic MRI were prospectively enrolled in this study. Diffusion and perfusion parameters including the apparent diffusion coefficient (ADC), pure diffusion coefficient, perfusion fraction, and pseudodiffusion coefficient derived from IVIMDWI were independently measured by two radiologists. Comparisons of IVIM-DWI-derived parameters in patients with different tumor prognostic markers were made using the independent-samples t test, ANOVA, and Mann-Whitney U test. The correlations between IVIM-DWI-derived parameters and tumor grade and tumor stage were further evaluated using Spearman correlation analysis. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC).

RESULTS

Excellent interobserver reproducibility was obtained for the IVIM-DWI-derived parameters (range of ICCs with 95% limits of agreement = 0.9309-0.9948, which is narrow). ADC, pseudodiffusion coefficient, and perfusion fraction tended to rise with greater tumor differentiation (r = 0.520, p < 0.001; r = 0.447, p = 0.001; r = 0.354, p = 0.010, respectively). The pure diffusion coefficient and pseudodiffusion coefficient showed a trend of decreasing with increasing tumor stages (r = 0.479, p < 0.001; r = 0.517, p < 0.001). The group of patients with extramural vascular invasion (EMVI) showed lower pseudodiffusion coefficient values than the group of patients with no EMVI (p < 0.05).

CONCLUSION

IVIM-DWI-derived parameters in patients with rectal cancer, especially the pseudodiffusion coefficient, are associated with tumor grade and tumor stage and show statistically significant differences between subjects with EMVI and those without EMVI. IVIM-DWI-derived parameters would be helpful in predicting tumor aggressiveness and prognosis.

Role of MR in the differentiation of IgG4-related from non-IgG4-related hepatic inflammatory pseudotumor

BACKGROUND

Hepatic inflammatory pseudotumor (IPT) is classified into 2 types based on IgG4 stain: IgG4-related and non-IgG4-related; the two types differ not only in their pathological characteristics, but also in the clinical features. This study aimed to investigate the MR character of hepatic IPT, and differentiate the IgG4-related IPT from the non-IgG4-related IPT.

METHODS

Twenty-five patients with 27 histologically proven hepatic IPTs were retrospectively analyzed. Ten lesions were diagnosed as IgG4-related IPT, and the other 17 as non-IgG4-related IPT. The MR signal features on T1, T2-weighted, dynamic-enhanced, and diffusion-weighted imaging were evaluated and compared.

RESULTS

The dominant lesions were subcapsularly distributed (n=17, 63.0%) with clear boundary (n=20, 74.1%), and showed progressive enhancement pattern (n=21, 77.8%) with diffuse homogeneous (n=12, 44.4%) or heterogeneous (n=8, 29.6%) hyperintensity, accompanied by delayed capsule-like enhancement (n=17, 63.0%) and central nonenhanced areas (n=18, 66.7%). Morphological features (P>0.05) were not sufficient to differentiate IgG4-related IPT from non-IgG4-related IPT; the wash-out pattern was only found in 2 IgG4-related IPT, while the progressive enhancement pattern was more common in the non-IgG4-related lesions (n=16) (P=0.022). During portal and delayed phases, iso-/hypoenhanced lesions were only seen in 3 IgG4-related IPT, and circular-enhanced lesions (n=5) existed exceptionally in the non-IgG4-related group with significant differences (P=0.029 and 0.027). Most IgG4-related IPTs had lower apparent diffusion coefficient compared with the liver parenchyma (n=6), while most non-IgG4-related IPTs had higher apparent diffusion coefficient value (n=13) (P=0.046).

CONCLUSIONS

Although MR images of hepatic IPT have certain characteristics, they are not enough to differentiate IgG4-related IPT from non-IgG4-related IPT. The enhancement pattern, signal features on portal and delayed phases, and the apparent diffusion coefficient value of the lesion may be helpful for the diagnosis.

Monitoring Tumor Response to Antivascular Therapy Using Non-Contrast Intravoxel Incoherent Motion Diffusion-Weighted MRI

Antivascular therapy is a promising approach to the treatment of non-small cell lung cancer (NSCLC), where an imaging modality capable of longitudinally monitoring treatment response could provide early prediction of the outcome. In this study, we sought to investigate the feasibility of using intravoxel incoherent motion (IVIM) diffusion MRI to quantitatively assess the efficacy of the treatments of a vascular-disrupting agent CA4P or its combination with bevacizumab on experimental NSCLC tumors. CA4P caused a strong but reversible effect on tumor vasculature; all perfusion-related parameters-D*, f, fD*, and K^trans-initially showed a decrease of 30% to 60% at 2 hours and then fully recovered to baseline on day 2 for CA4P treatment or on days 4 to 8 for CA4P + bevacizumab treatment; the diffusion coefficient in tumors decreased initially at 2 hours and then increased from day 2 to day 8. We observed a good correlation between IVIM parameters and dynamic contrast-enhanced MRI (DCE-MRI; K^trans). We also found that the relative change in f and fD* at 2 hours correlated well with changes in tumor volume on day 8. In conclusion, our results suggest that IVIM is a promising alternative to DCE-MRI for the assessment of the change in tumor perfusion as a result of antivascular agents and can be used to predict the efficacy of antivascular therapies without the need for contrast media injection. Cancer Res; 77(13); 3491-501. ©2017 AACR.

Is the combination of MR and CT findings useful in determining the tumor grade of pancreatic neuroendocrine tumors?

PURPOSE

To retrospectively elucidate the findings useful in determining the tumor grade of pancreatic neuroendocrine tumors (PNETs) by combined assessment of magnetic resonance (MR) and dynamic computed tomography (CT) images.

MATERIALS AND METHODS

Eighty-nine patients with PNETs (96 lesions) were included, and classified as G1, 59; G2, 29; and G3, 8 lesions. Image analysis included lesion diameter, shape, enhancement pattern on arterial phase (AP) and delayed phase CT images, calcification, cystic portion, main pancreatic duct dilatation, signal-intensity on T1-, T2-weighted MR images, and appearance of apparent diffusion coefficient (ADC).

RESULTS

Significant differences among G1, G2, and G3 groups were noted in tumor maximal diameter (p < 0.0001), shape (p < 0.0001), enhancement pattern on AP image (p < 0.0001), cystic portion (p = 0.012), and ADC finding. In multivariate analysis, ADC finding was the independent factor (p = 0.002). The combination findings of low ADC ratio (ADC value of the lesion/ADC value of the parenchyma <0.94), not homogeneous hyper-attenuation, lobulated shape, and hyper-intensity on T2-weighted image were suggestive of G2 or G3 with a probability of 100%. Conversely, all lesions with high ADC ratio and small size (≤25 mm) belonged to the G1 group.

CONCLUSION

Combined assessment of MR and CT findings could improve the prediction of tumor grading in PNETs.

Pancreatic neuroendocrine neoplasms: Magnetic resonance imaging features according to grade and stage

AIM

To describe magnetic resonance (MR) imaging features of pancreatic neuroendocrine neoplasms (PanNENs) according to their grade and tumor-nodes-metastases stage by comparing them to histopathology and to determine the accuracy of MR imaging features in predicting their biological behavior.

METHODS

This study was approved by our institutional review board; requirement for informed patient consent was waived due to the retrospective nature of the study. Preoperative MR examinations of 55 PanNEN patients (29 men, 26 women; mean age of 57.6 years, range 21-83 years) performed between June 2013 and December 2015 were reviewed. Qualitative and quantitative features were compared between tumor grades and stages determined by histopathological analysis.

RESULTS

Ill defined margins were more common in G2-3 and stage III-IV PanNENs than in G1 and low-stage tumors (P < 0.001); this feature had high specificity in the identification of G2-3 and stage III-IV tumors (90.3% and 96%, 95%CI: 73.1-97.5 and 77.7-99.8). The mean apparent diffusion coefficient value was significantly lower in G2-3 and stage III-IV lesions compared to well differentiated and low-stage tumors (1.09 × 10^-3 mm²/s vs 1.45 × 10^-3 mm²/s and 1.10 × 10^-3 mm²/s vs 1.53 × 10^-3 mm²/s, P = 0.003 and 0.001). Receiving operator characteristic analysis determined optimal cut-offs of 1.21 and 1.28 × 10^-3 mm²/s for the identification of G2-3 and stage III-IV tumors, with sensitivity and specificity values of 70.8/80.7% and 64.5/64% (95%CI: 48.7-86.6/60-92.7 and 45.4-80.2/42.6-81.3).

CONCLUSION

MR features of PanNENs vary according to their grade of differentiation and their stage at diagnosis and could predict the biological behavior of these tumors.

Intravoxel incoherent motion diffusion-weighted MRI of the abdomen: The effect of fitting algorithms on the accuracy and reliability of the parameters

PURPOSE

To evaluate the influence of fitting methods on the accuracy and reliability of intravoxel incoherent motion (IVIM) parameters, with a particular emphasis on the constraint function.

MATERIALS AND METHODS

Diffusion-weighted (DW) imaging data were analyzed using IVIM-based full-fitting (simultaneous fit of all parameters) and segmented-fitting (step-by-step fit of each parameter), each with and without the constraint function, to estimate the molecular diffusion coefficient (D_slow ), perfusion fraction (f), and flow-related diffusion coefficient (D_fast ). Computational simulations were performed at variable signal-to-noise ratios to evaluate the relative error (RE) and coefficient of variation (CV) of the estimated IVIM parameters. DW imaging of the abdomen was performed twice at 1.5 Tesla using nine b-values (0-900 s/mm² ) in 12 health volunteers (6 men and 6 women; mean age: 30 years). The measurement repeatability of IVIM parameters in the liver and the pancreas was evaluated using the within-subject coefficient of variation (_w CV).

RESULTS

In simulations, full-fitting without the constraint function yielded the largest RE (P < 0.001 for D_slow and f; P ≤ 0.044 for D_fast ) and CV (P ≤ 0.033 for D_slow and f; P ≤ 0.473 for D_fast ) for IVIM parameters among all four algorithms. In volunteer imaging, full-fitting without the constraint function also resulted in the poorest repeatability for D_slow (_w CV, 17.12%-65.45%) and f (_w CV, 19.35%-42.84%) in the liver and pancreas, while the other algorithms had similar repeatability values (_w CV, 4.05%-11.99% for D_slow and 9.65%-18.66% for f). Measurement repeatability of D_fast (_w CV, 29.52%-85.01%) was the poorest among the IVIM parameters.

CONCLUSION

For accurate and reliable measurement of IVIM parameters, segmented fitting or full-fitting with the constraint function should be used for IVIM-based analysis of DW imaging.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1637-1647.

Intravoxel Incoherent Motion Protocol Evaluation and Data Quality in Normal and Malignant Liver Tissue and Comparison to the Literature

OBJECTIVES

Although intravoxel incoherent motion (IVIM) becomes more and more popular, there is currently no clear consensus on the number and distribution of b-values to use. In this work, we (1) tested and evaluated the data quality of a 25-b-value IVIM protocol in patients with malignant liver lesions and normal liver tissue as a standard of reference, (2) calculated an optimal b-value distribution and compared with the standard of reference, and (3) compared the 25-b-value protocol with other proposed protocols in the literature.

MATERIALS AND METHODS

Intravoxel incoherent motion imaging with 25 b-values was performed at 3 T in a total of 15 patients with malignant liver lesions. Reference IVIM parameter maps were calculated in tumor and normal liver tissue. With these parameters, optimal IVIM protocols with reduced numbers of b-values were calculated. These optimal IVIM protocols were again applied to calculate new IVIM parameter maps that were compared with the reference parameter maps by calculating mean relative errors. In addition, 35 other IVIM protocols, as found in literature, were compared in a similar way with the 25-b-value protocol serving as a standard of reference.

RESULTS

The mean relative error depends on the number of b-values and their distribution. In tumor tissue, the error is higher and more variable than in normal-appearing liver tissue. The largest errors occur in tumor tissue and in the protocols having low numbers of b-values in the IVIM protocols. In the calculated optimal IVIM protocols, the mean relative errors decreased by 40% or more when the number of b-values included increased from 4 to 16. The mean relative errors in the protocols adapted from the literature vary substantially between the various b-value distributions. One optimized 16-b-value protocol, which was found in literature, reduced the average relative error by 80% when compared with 4- and 5-b-value protocols listed in literature.

CONCLUSIONS

Including more b-values and applying an optimized b-value distribution significantly reduces errors in the IVIM parameter estimates, thereby increasing its accuracy.This effect is even more pronounced in inhomogeneous tumor compared with that in normal liver tissue. However, when restrictions in acquisition time or patient-related factors apply, a minimum of 16 b-values should be considered for reliable results.

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.

Diffusion Tensor MR Imaging Evaluation of Callosal Abnormalities in Schizophrenia: A Meta-Analysis

Widespread white matter (WM) abnormalities have been found in patients with schizophrenia. Corpus callosum (CC) is the key area that connects the left and right brain hemispheres. However, the results of studies considering different subregions of the CC as regions of interest in patients with schizophrenia have been inconsistent. To obtain a more consistent evaluation of the diffusion characteristics change of the corpus callosum (CC) related to schizophrenia. A meta-analysis involving fractional anisotropy (FA) values in the CC of 729 schizophrenic subjects and 682 healthy controls from 22 studies was conducted. Overall FA values in the CC of the schizophrenic group were less than that of the healthy control group [weighted mean difference (WMD) = -0.021,P< 0.001]. So were the FA values in the genus region (WMD = -0.019, P< 0.001) and the splenium region (WMD = -0.020, P< 0.001) of the CC respectively. The FA reduction was also significant in subjects with chronic schizophrenia (WMD = -0.032, P< 0.001) and first-episode schizophrenia (WMD = -0.014, P = 0.001). In present study, we demonstrated an overall FA decrease in the CC of schizophrenic patients. In the two subgroup analyses of the genu vs splenium region and chronic vs first-episode schizophrenia, the decrease of all groups was significant. Further studies with more homogenous populations and standardized DTI protocols are needed to confirm and extend these findings.

Digestive neuroendocrine neoplasms: A 2016 overview

Digestive neuroendocrine neoplasms (DNENs) have an incidence of 2.39 per 100,000 inhabitants per year, and a prevalence of 35 cases per 100,000; the gap between these rates is to be referred to the relatively long survival that characterizes the majority of these tumors, which can be thus considered as chronic oncological diseases. Up to 80% of patients are stage IV since the first diagnosis, presenting a 5-yr overall survival rate of 35%-55% and a twice higher mortality than limited disease. DNENs express somatostatin receptors in more than 80% of cases, detected through immunohistochemistry or functional imaging tests (FITs). This feature identifies patients who may benefit from "cold" somatostatin analogs (SSAs) or peptide receptors radionuclide therapy, although SSAs are sometimes used also with a negative uptake at FITs. The therapeutic options have been recently increased after the identification of molecular pathways involved in DNENs pathogenesis, and the subsequent use of targeted therapies (i.e., Everolimus and Sunitinib) for these neoplasms. This review offers an overview about pancreatic and small bowel NENs, critically underlining the issues that still need to be clarified and the future perspectives to be investigated.

Neuroendocrine liver metastases: Value of apparent diffusion coefficient and enhancement ratios for characterization of histopathologic grade

PURPOSE

To assess the value of apparent diffusion coefficient (ADC) measured with diffusion-weighted imaging (DWI) and enhancement ratios (ER) measured with contrast-enhanced T1-weighted imaging (CE-T1WI) for the characterization of histopathologic tumor grade of neuroendocrine tumor liver metastases (NETLM).

MATERIALS AND METHODS

Twenty-two patients with pathology-proven NETLM and pretreatment 1.5 Tesla (T) and 3T MRI including DWI were included in this Institutional Review Board-approved retrospective study. ADC histogram parameters, including mean, minimum (min), skewness, and kurtosis as well as ER, were computed for all lesions. Tumor grading was based on the World Health Organization 2010 classification. Kruskal-Wallis and Mann-Whitney test were used to assess for differences in ADC and ER between different tumor grades. MRI parameters were correlated with pathologic findings using Spearman correlation test. Receiver operating characteristic analysis was performed to determine optimum thresholds for predicting tumor grade.

RESULTS

Forty-eight NETLM (mean size 3.5 cm) were analyzed with the following grade distribution: G1 (n = 25), G2 (n = 16), and G3 (n = 7). ADC-mean (×10^-3 mm² /s) of G3 tumors (0.87 ± 0.43) was significantly lower than that of G1 (1.47 ± 0.63) and G2 (1.27 ± 0.63; P = 0.042). A weak significant negative correlation was observed between ADC and tumor grade (ADC-mean: r = -0.33, P = 0.02; ADC-min: r = -0.37, P = 0.01) and Ki-67 (ADC-mean: r = -0.31, P = 0.03; ADC-min: r = -0.39, P = 0.007). AUROC, sensitivity and specificity of ADC-mean/ADC-min/ER (measured at the early arterial phase) for differentiation of G3 versus G1-G2 were 0.80/0.76/0.67, 100%/50%/70%, and 68.4%/84.2%/66.6%, respectively.

CONCLUSION

ADC is a promising marker for characterization of histopathologic grade of NETLM. These results should be confirmed in a prospective study. J. Magn. Reson. Imaging 2016;44:1432-1441.

Use of intravoxel incoherent motion diffusion-weighted MR imaging for assessment of treatment response to invasive fungal infection in the lung

OBJECTIVES

The purpose of this study was to determine whether intravoxel incoherent motion (IVIM) -derived parameters and apparent diffusion coefficient (ADC) could act as imaging biomarkers for predicting antifungal treatment response.

METHODS

Forty-six consecutive patients (mean age, 33.9 ± 13.0 y) with newly diagnosed invasive fungal infection (IFI) in the lung according to EORTC/MSG criteria were prospectively enrolled. All patients underwent diffusion-weighted magnetic resonance (MR) imaging at 3.0 T using 11 b values (0-1000 sec/mm²). ADC, pseudodiffusion coffiecient D*, perfusion fraction f, and the diffusion coefficient D were compared between patients with favourable (n=32) and unfavourable response (n=14).

RESULTS

f values were significantly lower in the unfavourable response group (12.6%±4.4%) than in the favourable response group (30.2%±8.6%) (Z=4.989, P<0.001). However, the ADC, D, and D* were not significantly different between the two groups (P>0.05). Receiver operating characteristic curve analyses showed f to be a significant predictor for differentiation, with a sensitivity of 93.8% and a specificity of 92.9%.

CONCLUSIONS

IVIM-MRI is potentially useful in the prediction of antifungal treatment response to patients with IFI in the lung. Our results indicate that a low perfusion fraction f may be a noninvasive imaging biomarker for unfavourable response.

KEY POINTS

• Recognition of IFI indicating clinical outcome is important for treatment decision-making. • IVIM can reflect diffusion and perfusion information of IFI lesions separately. • Perfusion characteristics of IFI lesions could help differentiate treatment response. • An initial low f may predict unfavourable response in IFI.

State-of-the-art Imaging of Pancreatic Neuroendocrine Tumors

Pancreatic neuroendocrine tumors are rare tumors that present many imaging challenges, from detecting small functional tumors to fully staging large nonfunctioning tumors, including identifying all sites of metastatic disease, particularly nodal and hepatic, and depicting vascular involvement. The correct choice of imaging modality requires knowledge of the tumor type (eg, gastrinoma versus insulinoma), and also the histology (well vs poorly differentiated). Evolving techniques in computed tomography (CT), MRI, endoscopic ultrasonography, and nuclear medicine, such as dual-energy CT, diffusion-weighted MRI, liver-specific magnetic resonance contrast agents, and new nuclear medicine agents, offer new ways to visualize, and ultimately manage, these tumors.

Various diffusion magnetic resonance imaging techniques for pancreatic cancer

Pancreatic cancer is one of the most common malignant tumors and remains a treatment-refractory cancer with a poor prognosis. Currently, the diagnosis of pancreatic neoplasm depends mainly on imaging and which methods are conducive to detecting small lesions. Compared to the other techniques, magnetic resonance imaging (MRI) has irreplaceable advantages and can provide valuable information unattainable with other noninvasive or minimally invasive imaging techniques. Advances in MR hardware and pulse sequence design have particularly improved the quality and robustness of MRI of the pancreas. Diffusion MR imaging serves as one of the common functional MRI techniques and is the only technique that can be used to reflect the diffusion movement of water molecules in vivo. It is generally known that diffusion properties depend on the characterization of intrinsic features of tissue microdynamics and microstructure. With the improvement of the diffusion models, diffusion MR imaging techniques are increasingly varied, from the simplest and most commonly used technique to the more complex. In this review, the various diffusion MRI techniques for pancreatic cancer are discussed, including conventional diffusion weighted imaging (DWI), multi-b DWI based on intra-voxel incoherent motion theory, diffusion tensor imaging and diffusion kurtosis imaging. The principles, main parameters, advantages and limitations of these techniques, as well as future directions for pancreatic diffusion imaging are also discussed.

Pancreatic Neuroendocrine Neoplasms: Clinical Value of Diffusion-Weighted Imaging

BACKGROUND/AIMS

Diffusion-weighted imaging (DWI) can depict random motions of water molecules in biological tissues during magnetic resonance (MR) examinations. Few papers have tested its application to pancreatic neuroendocrine neoplasms (PanNENs). The aim of this paper is to assess the clinical value of DWI regarding the identification and characterization of PanNENs and diagnosis of liver metastases.

METHODS

Preoperative MR examinations of 30 PanNEN patients were retrospectively reviewed; 30 patients with pathologically proven pancreatic ductal adenocarcinoma (PDAC) were included to compare the imaging features. Qualitative and quantitative MR features were compared between histotypes. A blinded-reader comparison of diagnostic confidence for PanNENs and liver metastases was conducted on randomized image sets. All results were compared with pathological data.

RESULTS

PanNEN conspicuity was higher on DW images compared to conventional MR sequences. DWI had higher detection rates for PanNENs than had conventional sequences (93.3 vs. 71.1%). Sharp margins and absence of main pancreatic duct/common bile duct dilation and chronic pancreatitis were more common among PanNENs as compared to PDACs. Arterial iso- or hyperenhancement and portal hyperenhancement were more frequent within PanNENs as compared to PDACs. No differences between histotypes were found for quantitative features. Arterial-phase images had the highest interobserver agreement for the diagnosis of PanNEN (Cohen's κ = 0.667). DWI provided the highest detection rate for liver metastases as well as excellent interobserver agreement for the diagnosis of liver metastases (κ = 0.932), with good accuracy (AUC = 0.879-0.869).

CONCLUSION

DWI has clinical value regarding the identification of PanNENs and the diagnosis of liver metastases, while conventional MR sequences are fundamental for their characterization.

Prognostication and response assessment in liver and pancreatic tumors: The new imaging

Diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and perfusion computed tomography (CT) are technical improvements of morphologic imaging that can evaluate functional properties of hepato-bilio-pancreatic tumors during conventional MRI or CT examinations. Nevertheless, the term “functional imaging” is commonly used to describe molecular imaging techniques, as positron emission tomography (PET) CT/MRI, which still represent the most widely used methods for the evaluation of functional properties of solid neoplasms; unlike PET or single photon emission computed tomography, functional imaging techniques applied to conventional MRI/CT examinations do not require the administration of radiolabeled drugs or specific equipments. Moreover, DWI and DCE-MRI can be performed during the same session, thus providing a comprehensive “one-step” morphological and functional evaluation of hepato-bilio-pancreatic tumors. Literature data reveal that functional imaging techniques could be proposed for the evaluation of these tumors before treatment, given that they may improve staging and predict prognosis or clinical outcome. Microscopic changes within neoplastic tissues induced by treatments can be detected and quantified with functional imaging, therefore these techniques could be used also for post-treatment assessment, even at an early stage. The aim of this editorial is to describe possible applications of new functional imaging techniques apart from molecular imaging to hepatic and pancreatic tumors through a review of up-to-date literature data, with a particular emphasis on pathological correlations, prognostic stratification and post-treatment monitoring.

MR imaging of primary hepatic neuroendocrine neoplasm and metastatic hepatic neuroendocrine neoplasm: a comparative study

PURPOSE

To investigate MR characteristics in differentiating primary hepatic neuroendocrine neoplasm (PHNEN) from metastatic hepatic neuroendocrine neoplasm (MHNEN).

MATERIALS AND METHODS

Thirty-nine patients with histopathologically proven liver neuroendocrine neoplasm were retrospectively analyzed. The morphological and MR signal features on T1, T2-weighted, dynamic-enhanced, and diffusion-weighted imaging were evaluated and compared between the PHNEN group (n = 12) and the MHNEN group (n = 27).

RESULTS

The tumor size (P = 0.0084), number (P = 0.017), distribution (P = 0.000), contour (P = 0.041), the presence of capsule-like enhancement (P = 0.034), tumor homogeneity (P = 0.018) and the apparent diffusion coefficient (ADC) values (P = 0.024) were different between PHNENs and MHNENs. Large, solitary or massive-growing nodules with lobulated or irregular contour, capsule-like enhancement, heterogeneous signals or lower ADC values supported the diagnosis of PHNEN compared with MHNEN. ROC analysis demonstrated an area under the curve of 0.746, when the optimal cutoff value of 1.049 × 10(-3) mm(2)/s was used, a sensitivity of 63.0 % (95 % CI, 44.2-79.4 %), a specitivity of 80.0 % (95 % CI, 50.1-96.4 %), a positive predictive value of 89.5 % (95 % CI, 70.9-98.2 %), and a negative predictive value of 44.4 % (95 % CI, 23.4-67.0 %) can be achieved.

CONCLUSIONS

MRI may provide valuable information for the diagnosis and differential diagnosis of PHNENs and MHNENs.

The status of neuroendocrine tumor imaging: from darkness to light?

Diagnostic imaging plays a pivotal role in the diagnosis, staging, treatment selection and follow-up for neuroendocrine tumors. The available diagnostic strategies are morphologic imaging, including computed tomography, magnetic resonance imaging (MRI) and ultrasound techniques, and molecular imaging, including scintigraphy with (111)In-pentetreotide and positron emission tomography with (68)Ga-DOTA-peptides, (18)F-DOPA and (11)C-5-HTP. A combination of anatomic and functional techniques is routinely performed to optimize sensitivity and specificity. The introduction of diffusion-weighted MRI and dynamic contrast-enhanced techniques represents a promising advance in radiologic imaging, whereas new receptor-binding peptides, including somatostatin agonists and antagonists, represent the recent most favorable innovation in molecular imaging. Future development includes the short-term validation of these techniques, but in extension also a more comprehensive multilevel integration of biologic information pertaining to a specific tumor and patient, possibly encompassing genomic considerations, currently evolving as a new entity denoted 'precision medicine'. The ideal is a diagnostic sequence that captures the global status of an individual's tumor and encompasses a multidimensional characterization of tumor location, metabolic performance and target identification. To date, advances in imagery have focused on increasing resolution, discrimination and functional characterization. In the future, the fusion of imagery with the parallel analysis of biological and genomic information has the potential to considerably amplify diagnosis.