Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature - 05/01/24
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Highlights |
• | A radiomic signature derived from CT data was developed to preoperatively predict tumor grade of pancreatic neuroendocrine tumors. |
• | This study demonstrates that non-invasive assessment of tumor grade using the proposed radiomic-signature is feasible and highly accurate. |
• | Accurate prediction of pancreatic neuroendocrine tumor grade via radiomic signature could allow timely tailoring of patient management and allow non-invasive serial assessment in patients on surveillance. |
Abstract |
Purpose |
The purpose of this study was to develop a radiomics-signature using computed tomography (CT) data for the preoperative prediction of grade of nonfunctional pancreatic neuroendocrine tumors (NF-PNETs).
Materials and methods |
A retrospective study was performed on patients undergoing resection for NF-PNETs between 2010 and 2019. A total of 2436 radiomic features were extracted from arterial and venous phases of pancreas-protocol CT examinations. Radiomic features that were associated with final pathologic grade observed in the surgical specimens were subjected to joint mutual information maximization for hierarchical feature selection and the development of the radiomic-signature. Youden-index was used to identify optimal cutoff for determining tumor grade. A random forest prediction model was trained and validated internally. The performance of this tool in predicting tumor grade was compared to that of EUS-FNA sampling that was used as the standard of reference.
Results |
A total of 270 patients were included and a fusion radiomic-signature based on 10 selected features was developed using the development cohort (n = 201). There were 149 men and 121 women with a mean age of 59.4 ± 12.3 (standard deviation) years (range: 23.3–85.0 years). Upon internal validation in a new set of 69 patients, a strong discrimination was observed with an area under the curve (AUC) of 0.80 (95% confidence interval [CI]: 0.71–0.90) with corresponding sensitivity and specificity of 87.5% (95% CI: 79.7–95.3) and 73.3% (95% CI: 62.9–83.8) respectively. Of the study population, 143 patients (52.9%) underwent EUS-FNA. Biopsies were non-diagnostic in 26 patients (18.2%) and could not be graded due to insufficient sample in 42 patients (29.4%). In the cohort of 75 patients (52.4%) in whom biopsies were graded the radiomic-signature demonstrated not different AUC as compared to EUS-FNA (AUC: 0.69 vs. 0.67; P = 0.723), however greater sensitivity (i.e., ability to accurately identify G2/3 lesion was observed (80.8% vs. 42.3%; P < 0.001).
Conclusion |
Non-invasive assessment of tumor grade in patients with PNETs using the proposed radiomic-signature demonstrated high accuracy. Prospective validation and optimization could overcome the commonly experienced diagnostic uncertainty in the assessment of tumor grade in patients with PNETs and could facilitate clinical decision-making.
Le texte complet de cet article est disponible en PDF.Keywords : Artificial intelligence, Pancreatic neoplasms, Pancreatic neuroendocrine tumors, Radiomics, WHO grading
Abbreviations : COVID-19, AUC, CI, CT, EUS-FNA, LoG, PanNEC, PET, PNET, PPCT, SD, WHO
Plan
Vol 105 - N° 1
P. 33-39 - janvier 2024 Retour au numéro
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