|Title:||Radiomic features at ct can distinguish pancreatic cancer from noncancerous pancreas||Authors:||Chen, Po Ting
Huang, Su Yun
|Issue Date:||1-Jun-2021||Journal Volume:||3||Journal Issue:||4||Source:||Radiology: Imaging Cancer||Abstract:||
Purpose: To identify distinguishing CT radiomic features of pancreatic ductal adenocarcinoma (PDAC) and to investigate whether radiomic analysis with machine learning can distinguish between patients who have PDAC and those who do not. Materials and Methods: This retrospective study included contrast material–enhanced CT images in 436 patients with PDAC and 479 healthy controls from 2012 to 2018 from Taiwan that were randomly divided for training and testing. Another 100 patients with PDAC (enriched for small PDACs) and 100 controls from Taiwan were identified for testing (from 2004 to 2011). An additional 182 patients with PDAC and 82 healthy controls from the United States were randomly divided for training and testing. Images were processed into patches. An XGBoost (https://xgboost.ai/) model was trained to classify patches as cancerous or noncancerous. Patients were classified as either having or not having PDAC on the basis of the proportion of patches classified as cancerous. For both patch-based and patient-based classification, the models were characterized as either a local model (trained on Taiwanese data only) or a generalized model (trained on both Taiwanese and U.S. data). Sensitivity, specificity, and accuracy were calculated for patch-and patient-based analysis for the models. Results: The median tumor size was 2.8 cm (interquartile range, 2.0–4.0 cm) in the 536 Taiwanese patients with PDAC (mean age, 65 years 6 12 [standard deviation]; 289 men). Compared with normal pancreas, PDACs had lower values for radiomic features reflecting intensity and higher values for radiomic features reflecting heterogeneity. The performance metrics for the developed generalized model when tested on the Taiwanese and U.S. test data sets, respectively, were as follows: sensitivity, 94.7% (177 of 187) and 80.6% (29 of 36); specificity, 95.4% (187 of 196) and 100% (16 of 16); accuracy, 95.0% (364 of 383) and 86.5% (45 of 52); and area under the curve, 0.98 and 0.91. Conclusion: PDAC. Radiomic analysis with machine learning enabled accurate detection of PDAC at CT and could identify patients with
|Appears in Collections:||醫學系|
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