23.08.2024 | Endocrine Tumors
Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics–Clinical Model
verfasst von:
Taha M. Ahmed, MD, Zhuotun Zhu, PhD, Mohammad Yasrab, MD, Alejandra Blanco, MD, Satomi Kawamoto, MD, Jin He, MD, PhD, Elliot K. Fishman, MD, Linda Chu, MD, Ammar A. Javed, MD
Erschienen in:
Annals of Surgical Oncology
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Ausgabe 12/2024
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Abstract
Background
PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs).
Patients and Methods
An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally.
Results
Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm).
Conclusions
Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.
