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.; Mosconi C.; Ingaldi C.; Vara G.; Verna M.; Pettinari I.; Alberici L.; Campana D.; Ambrosini V.; Minni F.; Golfieri R.; Casadei R.. - In: PANCREAS. - ISSN 1536-4828. - ELETTRONICO. - 50:10(2021), pp. 1392-1399. [10.1097/MPA.0000000000001927]
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.