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

Baseline radiologic features as predictors of efficacy in patients with pancreatic neuroendocrine tumors with liver metastases receiving surufatinib

Objective

Currently, pre-treatment prediction of patients with pancreatic neuroendocrine tumors with liver metastases (PNELM) receiving surufatinib treatment was unsatisfying. Our objective was to examine the association between radiological characteristics and efficacy/prognosis.

Methods

We enrolled patients with liver metastases in the phase III, SANET-p trial (NCT02589821) and obtained contrast-enhanced computed tomography (CECT) images. Qualitative and quantitative parameters including hepatic tumor margins, lesion volumes, enhancement pattern, localization types, and enhancement ratios were evaluated. The progression-free survival (PFS) and hazard ratio (HR) were calculated using Cox's proportional hazard model. Efficacy was analyzed by logistic-regression models.

Results

Among 152 patients who had baseline CECT assessments and were included in this analysis, the surufatinib group showed statistically superior efficacy in terms of median PFS compared to placebo across various qualitative and quantitative parameters. In the multivariable analysis of patients receiving surufatinib (N=100), those with higher arterial phase standardized enhancement ratio-peri-lesion (ASER-peri) exhibited longer PFS [HR=0.039; 95% confidence interval (95% CI): 0.003-0.483; P=0.012]. Furthermore, patients with a high enhancement pattern experienced an improvement in the objective response ratio [31.3% vs. 14.7%, odds ratio (OR)=3.488; 95% CI: 1.024-11.875; P=0.046], and well-defined tumor margins were associated with a higher disease control rate (DCR) (89.3% vs. 68.2%, OR=4.535; 95% CI: 1.285-16.011; P=0.019) compared to poorly-defined margins.

Conclusions

These pre-treatment radiological features, namely high ASER-peri, high enhancement pattern, and well-defined tumor margins, have the potential to serve as predictive markers of efficacy in patients with PNELM receiving surufatinib.

A Machine Learning Approach Using [18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor

PURPOSE

We sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET)-based radiomics and clinical features in patients with pancreatic neuroendocrine tumors (PNETs).

PROCEDURES

A total of 58 patients with PNETs who underwent pretherapeutic [18F]FDG PET/computed tomography (CT) were retrospectively enrolled. PET-based radiomics extracted from segmented tumor and clinical features were selected to develop prediction models by the least absolute shrinkage and selection operator feature selection method. The predictive performances of ML models using neural network (NN) and random forest algorithms were compared by the areas under the receiver operating characteristic curves (AUROCs) and validated by stratified five-fold cross validation.

RESULTS

We developed two separate ML models for predicting high-grade tumors (Grade 3) and tumors with poor prognosis (disease progression within two years). The integrated models consisting of clinical and radiomic features with NN algorithm showed the best performances than the other models (stand-alone clinical or radiomics models). The performance metrics of the integrated model by NN algorithm were AUROC of 0.864 in the tumor grade prediction model and AUROC of 0.830 in the prognosis prediction model. In addition, AUROC of the integrated clinico-radiomics model with NN was significantly higher than that of tumor maximum standardized uptake model in predicting prognosis (P < 0.001).

CONCLUSIONS

Integration of clinical features and [18F]FDG PET-based radiomics using ML algorithms improved the prediction of high-grade PNET and poor prognosis in a non-invasive manner.

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.

Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI-A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making

BACKGROUND

Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC).

METHODS

Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure).

RESULTS

Internal validation of the model's accuracy showed a high overlap for NELM and livers (Matthew's correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001).

CONCLUSION

The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model's measurements correlated well with MCC's evaluation of therapeutic response.

Quantitative CT assessment by histogram and volume ratio in pyrrolizidines alkaloids-induced hepatic sinusoidal obstruction syndrome

OBJECTIVE

To quantitatively assess hypoattenuation volume ratio and hepatic parenchymal hypoattenuation on contrast enhanced computed tomography (CECT) in patients with pyrrolizidines alkaloids (PAs)-induced hepatic sinusoidal obstruction syndrome (HSOS), and evaluate the correlations of the CT-based quantitative values with clinical factors.

METHODS

Thirty-five patients with PAs-induced HSOS who underwent CECT were retrospectively enrolled. The ratio of hypoattenuation volume to total liver volume, and changes in damaged area-to-normal liver density ratio (ΔDR) derived from histogram on portal venous phase were quantitatively measured. Heterogeneous hypoattenuation (CT score) scored by hypoattenuation volume ratio and ΔDR were calculated. The correlation between imaging findings and clinical factors was analyzed using Pearson correlation test.

RESULTS

Liver function tests were abnormal in most patients, the mean Hounsfield unit (HU) of damaged area (58.68 ± 17.3) was significantly lower (P < 0.001) than the corresponding normal liver (82.27 ± 23.97). Heterogeneous hypoattenuation were mild in 13 patients (37 %), moderate in 16 patients (46 %), and severe in 6 patients (17 %). ΔDR derived from histogram was positively correlated (weakly to moderately) with total bilirubin (r = 0.341, P = 0.045), direct bilirubin (r = 0.385, P = 0.022), and alkaline phosphatase (r = 0.491, P = 0.003), while such correlation was not observed in hypoattenuation volume ratio. The severity of heterogeneous hypoattenuation scored by hypoattenuation volume ratio and ΔDR was positively correlated (weakly) with prothrombin time (r = 0.357, P = 0.035), international normalized ratio (r = 0.363, P = 0.032), alkaline phosphatase (r = 0.359, P = 0.034), and model for end-stage liver disease (MELD) score (r = 0.347, P = 0.041).

CONCLUSION

Heterogeneous hypoattenuation scored by volume ratio and ΔDR on CECT provides a non-invasive approach in evaluating the severity of PAs-induced HSOS.