Hepatic neuroendocrine tumour: Apparent diffusion coefficient as a potential marker of prognosis associated with tumour grade and overall survival

Utility of clinical and MR imaging parameters for prediction and monitoring of response to capecitabine and temozolomide (CAPTEM) therapy in patients with liver metastases of neuroendocrine tumors

BACKGROUND

This study explores the predictive and monitoring capabilities of clinical and multiparametric MR parameters in assessing capecitabine and temozolomide (CAPTEM) therapy response in patients with neuroendocrine tumors (NET).

PATIENTS AND METHODS

This retrospective study (n = 44) assessed CAPTEM therapy response in neuroendocrine liver metastases (NELM) patients. Among 33 monitored patients, as a subgroup of the overall study cohort, pretherapeutic and follow-up MRI data (size, apparent diffusion coefficient [ADC] values, and signal intensities), along with clinical parameters (chromogranin A [CgA] and Ki-67%), were analyzed. Progression-free survival (PFS) served as the reference. Responders were defined as those with PFS ≥ 6 months.

RESULTS

Most patients were male (75%) and had G2 tumors (76%) with a pancreatic origin (84%). Median PFS was 5.7 months; Overall Survival (OS) was 25 months. Non-responders (NR) had higher Ki-67 in primary tumors (16.5 vs. 10%, p = 0.01) and increased hepatic burden (20% vs. 5%, p = 0.007). NR showed elevated CgA post-treatment, while responders (R) exhibited a mild decrease. ADC changes differed significantly between groups, with NR having decreased ADCmin (-23%) and liver-adjusted ADCmean/ADCmean liver (-16%), compared to R's increases of ADCmin (50%) and ADCmean/ADCmean liver (30%). Receiver operating characteristic (ROC) analysis identified the highest area under the curve (AUC) (0.76) for a single parameter for ∆ ADC mean/liver ADCmean, with a cut-off of < 6.9 (76% sensitivity, 75% specificity). Combining ∆ Size NELM and ∆ ADCmin achieved the best balance (88% sensitivity, 60% specificity) outperforming ∆ Size NELM alone (69% sensitivity, 65% specificity). Kaplan-Meier analysis indicated significantly longer PFS for ∆ ADCmean/ADCmean liver < 6.9 (p = 0.024) and ∆ Size NELM > 0% + ∆ ADCmin < -2.9% (p = 0.021).

CONCLUSIONS

Survival analysis emphasizes the need for adapted response criteria, involving combined evaluation of CgA, ADC values, and tumor size for monitoring CAPTEM response in hepatic metastasized NETs.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The combination of hepatobiliary phase with Gd-EOB-DTPA and DWI is highly accurate for the detection and characterization of liver metastases from neuroendocrine tumor

OBJECTIVES

To compare the diagnostic accuracy of dynamic contrast-enhanced phases, hepatobiliary phase (HBP), and diffusion-weighted imaging (DWI) for the detection of liver metastases from neuroendocrine tumor (NET).

METHODS

Sixty-seven patients with suspected NET liver metastases underwent gadoxetic acid-enhanced MRI. Three radiologists read four imaging sets separately and independently: DWI, T2W+dynamic, T2WI+HBP, and DWI+HBP. Reference standard included all imaging, histological findings, and clinical data. Sensitivity and specificity were calculated and compared for each imaging set. Interreader agreement was evaluated by intraclass correlation coefficient (ICC). Univariate logistic regression was performed to evaluate lesion characteristics (size, ADC, and enhancing pattern) associated to false positive and negative lesions.

RESULTS

Six hundred twenty-five lesions (545 metastases, 80 benign lesions) were identified. Detection rate was significantly higher combining DWI+HBP than the other imaging sets (sensitivity 86% (95% confidence interval (CI) 0.845-0.878), specificity 94% (95% CI 0.901-0.961)). The sensitivity and specificity of the other sets were 82% and 65% for DWI, 88% and 69% for T2WI, and 90% and 82% for HBP+T2WI, respectively. The interreader agreement was statistically higher for both HBP sets (ICC = 0.96 (95% CI 0.94-0.97) for T2WI+HBP and ICC = 0.91 (95% CI 0.87-0.94) for DWI+HBP, respectively) compared with that for DWI (ICC = 0.76 (95% CI 0.66-0.83)) and T2+dynamic (ICC = 0.85 (95% CI 0.79-0.9)). High ADC values, large lesion size, and hypervascular pattern lowered the risk of false negative.

CONCLUSION

Given the high diagnostic accuracy of combining DWI+HBP, gadoxetic acid-enhanced MRI is to be considered in NET patients with suspected liver metastases. Fast MRI protocol using T2WI, DWI, and HBP is of interest in this population.

KEY POINTS

• The combined set of diffusion-weighted (DW) and hepatobiliary phase (HBP) images yields the highest sensitivity and specificity for neuroendocrine liver metastasis (NELM) detection. • Gadoxetic acid should be the contrast agent of choice for liver MRI in NET patients. • The combined set of HBP and DWI sequences could also be used as a tool of abbreviated MRI in follow-up or assessment of treatment such as somatostatin analogs.

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.

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.