OBJECTIVES: The aim of this study is to evaluate the computed tomography texture parameters in predicting grading. METHODS: This study analyzed 68 nonfunctioning pancreatic neuroendocrine neoplasms (Pan-NENs). Clinical and radiological parameters were studied. Four model models were built, including clinical and standard radiologic parameters (model 1), first- and second-order computed tomography features (models 2 and 3), all parameters (model 4). The diagnostic accuracy was reported as area under the curve. A score was computed using the best model and validated to predict progression-free survival. RESULTS: The size of tumors and heterogeneous enhancement were related to the risk of "non-G1" Pan-NENs (coefficients 0.471, P = 0.012, and 1.508, P = 0.027). Four second-order parameters were significantly related to the presence of "non-G1" Pan-NENs: the gray level co-occurrence matrix correlation (6.771; P = 0.011), gray level co-occurrence matrix contrast variance (0.349; P = 0.009), the neighborhood gray-level different matrix contrast (-63.129; P = 0.001), and the gray-level zone length matrix with the low gray-level zone emphasis (-0.151; P = 0.049). Model 4 was the best, with a higher area under the curve (0.912; P = 0.005). The score obtained predicted the progression-free survival. CONCLUSIONS: Computed tomography radiomics signature can be useful in preoperative workup.

The 3-Dimensional-Computed Tomography Texture Is Useful to Predict Pancreatic Neuroendocrine Tumor Grading

Ricci C.
Primo
;
Mosconi C.;Ingaldi C.;Vara G.;Verna M.;Pettinari I.;Alberici L.;Campana D.;Ambrosini V.;Minni F.;Golfieri R.;Casadei R.
Ultimo
2021

Abstract

OBJECTIVES: The aim of this study is to evaluate the computed tomography texture parameters in predicting grading. METHODS: This study analyzed 68 nonfunctioning pancreatic neuroendocrine neoplasms (Pan-NENs). Clinical and radiological parameters were studied. Four model models were built, including clinical and standard radiologic parameters (model 1), first- and second-order computed tomography features (models 2 and 3), all parameters (model 4). The diagnostic accuracy was reported as area under the curve. A score was computed using the best model and validated to predict progression-free survival. RESULTS: The size of tumors and heterogeneous enhancement were related to the risk of "non-G1" Pan-NENs (coefficients 0.471, P = 0.012, and 1.508, P = 0.027). Four second-order parameters were significantly related to the presence of "non-G1" Pan-NENs: the gray level co-occurrence matrix correlation (6.771; P = 0.011), gray level co-occurrence matrix contrast variance (0.349; P = 0.009), the neighborhood gray-level different matrix contrast (-63.129; P = 0.001), and the gray-level zone length matrix with the low gray-level zone emphasis (-0.151; P = 0.049). Model 4 was the best, with a higher area under the curve (0.912; P = 0.005). The score obtained predicted the progression-free survival. CONCLUSIONS: Computed tomography radiomics signature can be useful in preoperative workup.
Ricci C.; Mosconi C.; Ingaldi C.; Vara G.; Verna M.; Pettinari I.; Alberici L.; Campana D.; Ambrosini V.; Minni F.; Golfieri R.; Casadei R.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/853698
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