Radiomics model–based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade

• Published in: European radiology Vol. 33; no. 2; pp. 1152 - 1161
• Main Authors: Tikhonova, Valeriya S., Karmazanovsky, Grigory G., Kondratyev, Evvgeny V., Gruzdev, Ivan S., Mikhaylyuk, Kseniya A., Sinelnikov, Mikhail Y., Revishvili, Amiran Sh
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023; Springer Nature B.V
• Subjects: Accuracy; Adenocarcinoma; Algorithms; Carcinoma, Pancreatic Ductal - diagnostic imaging; Carcinoma, Pancreatic Ductal - pathology; Carcinoma, Pancreatic Ductal - surgery; Computed tomography; Diagnostic Radiology; Diagnostic systems; Discretization; Histology; Humans; Imaging; Internal Medicine; Interventional Radiology; Medicine; Medicine & Public Health; Neuroradiology; Oncology; Pancreas; Pancreatic cancer; Pancreatic Neoplasms; Pancreatic Neoplasms - diagnostic imaging; Pancreatic Neoplasms - pathology; Pancreatic Neoplasms - surgery; Phases; Predictions; Principal components analysis; Radiology; Radiomics; Regularization; Retrospective Studies; Standardization; Subgroups; Texture; Tomography, X-Ray Computed - methods; Tumors; Ultrasound; CT; Texture analysis; Pancreas; Pancreatic ductal adenocarcinoma; Radiomics
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-09046-1

Objective To develop diagnostic radiomic model–based algorithm for pancreatic ductal adenocarcinoma (PDAC) grade prediction. Methods Ninety-one patients with histologically confirmed PDAC and preoperative CT were divided into subgroups based on tumor grade. Two histology-blinded radiologists independently segmented lesions for quantitative texture analysis in all contrast enhancement phases. The ratio of densities of PDAC and unchanged pancreatic tissue, and relative tumor enhancement (RTE) in arterial, portal venous, and delayed phases of the examination were calculated. Principal component analysis was used for multivariate predictor analysis. The selection of predictors in the binary logistic model was carried out in 2 stages: (1) using one-factor logistic models (selection criterion was p < 0.1); (2) using regularization (LASSO regression after standardization of variables). Predictors were included in proportional odds models without interactions. Results There were significant differences in 4, 16, and 8 texture features out of 62 for the arterial, portal venous, and delayed phases of the study, respectively ( p < 0.1). After selection, the final diagnostic model included such radiomics features as DISCRETIZED HU standard, DISCRETIZED HUQ3, GLCM Correlation, GLZLM LZLGE for the portal venous phase of the contrast enhancement, and CONVENTIONAL_HUQ3 for the delayed phase of CT study. On its basis, a diagnostic model was built, showing AUC for grade ≥ 2 of 0.75 and AUC for grade 3 of 0.66. Conclusion Radiomics features vary in PDAC of different grades and increase the accuracy of CT in preoperative diagnosis. We have developed a diagnostic model, including texture features, which can be used to predict the grade of PDAC. Key Points • A diagnostic algorithm based on CT texture features for preoperative PDAC grade prediction was developed. • The assumption that the scanning protocol can influence the results of texture analysis was confirmed and assessed. • Our results show that tumor differentiation grade can be assessed with sufficient diagnostic accuracy using CT texture analysis presented in this study.