Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT

Purpose: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening. Materials and methods: Patients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network. A total of 1223 radiomics features were independently extracted from both segmentation volumes, in the arterial and venous phases separately. Ten final features were selected to train classifiers to identify PanNETs and controls. The cohort was divided into training and testing sets, and performance of classifiers was assessed using area under the receiver operator characteristic curve (AUC), specificity and sensitivity, and compared against two radiologists blinded to the diagnoses. Results: A total of 135 patients with 142 PanNETs, and 135 healthy controls were included. There were 168 women and 102 men, with a mean age of 55.4 11.6 (standard deviation) years (range: 20-85 years). Median PanNET size was 1.3 cm (Q1, 1.0; Q3, 1.5; range: 0.5-1.9). The arterial phase LightGBM model achieved the best performance in the test set, with 90% sensitivity (95% confidence interval [CI]: 80-98), 76% specificity (95% CI: 62-88) and an AUC of 0.87 (95% CI: 0.79-0.94). Using features from the automated segmentations, this model achieved an AUC of 0.86 (95% CI: 0.79-0.93). In comparison, the two radiologists achieved a mean 50% sensitivity and 100% specificity using arterial phase CT images. Conclusion: Radiomics features identify small PanNETs, with stable performance when extracted using automated segmentations. These models demonstrate high sensitivity, complementing the high specificity of radiologists, and could serve as opportunistic screeners. (c) 2024 Soci & eacute;t & eacute; fran & ccedil;aise de radiologie. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.