Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade

Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis

Background

Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.

Method

A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.

Results

A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.

Conclusion

AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.

A primer on artificial intelligence in pancreatic imaging

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.

A novel tool to predict nodal metastasis in small pancreatic neuroendocrine tumors: A multicenter study

BACKGROUND

Nonfunctional pancreatic neuroendocrine tumors display a wide range of biological behavior, and nodal disease is associated with metastatic disease and poorer survival. The aim of this study was to develop a tool to predict nodal disease in patients with small (≤2 cm) nonfunctional pancreatic neuroendocrine tumors.

METHODS

A multicenter retrospective study was performed on patients undergoing resection for small nonfunctional pancreatic neuroendocrine tumors. Patients with genetic syndromes, metastatic disease at diagnosis, neoadjuvant therapy, or positive resection margin were excluded. Factors associated with nodal disease were identified to develop a predictive model. Internal validation was performed using bootstrap with 1,000 resamples.

RESULTS

Nodal disease was observed in 39 (11.1%) of the 353 patients included. Presence of nodal disease was significantly associated with lower 5-year disease-free survival (71.6% vs 96.2%, P < .001). Two predictors were strongly associated with nodal disease: G2 grade (odds ratio: 3.51, 95% confidence interval: 1.71-7.22, P = .001) and tumor size (per mm increase, odds ratio: 1.14, 95% confidence interval: 1.03-1.25, P = .009). Adequate discrimination was observed with an area under the curve of 0.71 (95% confidence interval: 0.63-0.80). Based on risk distribution, 3 risk groups of nodal disease were identified; low (<5%), intermediate (≥5% to <20%), and high (≥20%) risk. The observed mean risk of nodal disease was 3.7% in the low-risk patients, 9.6% in the intermediate-risk patients, and 30.4% in the high-risk patients (P < .001). The 10-year disease-free survival in the low, intermediate, and high-risk groups was 100%, 88.8%, and 50.1%, respectively.

CONCLUSION

Our model using tumor grade and size can predict nodal disease in small nonfunctional pancreatic neuroendocrine tumors. Integration of this tool into clinical practice could help guide management of these patients.

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.