Jiang, C., Yuan, Y., Gu, B., Ahn, E., Kim, J., Feng, D., Huang, Q., and Song, S. (2023) Preoperative prediction of microvascular invasion and perineural invasion in pancreatic ductal adenocarcinoma with 18F-FDG PET/CT radiomics analysis. Clinical Radiology. (In Press)

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Abstract

AIM: To develop and validate a predictive model based on 2-[18F]-fluoro-2-deoxy-d-glucose (18F-FDG) positron-emission tomography (PET)/computed tomography (CT) radiomics features and clinicopathological parameters to preoperatively identify microvascular invasion (MVI) and perineural invasion (PNI), which are important predictors of poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS: Preoperative 18F-FDG PET/CT images and clinicopathological parameters of 170 patients in PDAC were collected retrospectively. The whole tumour and its peritumoural variants (tumour dilated with 3, 5, and 10 mm pixels) were applied to add tumour periphery information. A feature-selection algorithm was employed to mine mono-modality and fused feature subsets, then conducted binary classification using gradient boosted decision trees. RESULTS

For MVI prediction, the model performed best on a fused subset of 18F-FDG PET/CT radiomics features and two clinicopathological parameters, with an area under the receiver operating characteristic curve (AUC) of 83.08%, accuracy of 78.82%, recall of 75.08%, precision of 75.5%, and F1-score of 74.59%. For PNI prediction, the model achieved best prediction results only on the subset of PET/CT radiomics features, with AUC of 94%, accuracy of 89.33%, recall of 90%, precision of 87.81%, and F1 score of 88.35%. In both models, 3 mm dilation on the tumour volume produced the best results.

CONCLUSIONS: The radiomics predictors from preoperative 18F-FDG PET/CT imaging exhibited instructive predictive efficacy in the identification of MVI and PNI status preoperatively in PDAC. Peritumoural information was shown to assist in MVI and PNI predictions

Item ID:	79325
Item Type:	Article (Research - C1)
ISSN:	1365-229X
Copyright Information:	© 2023 Published by Elsevier Ltd on behalf of The Royal College of Radiologists.
Date Deposited:	19 Jul 2023 01:53
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 60% 32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320222 Radiology and organ imaging @ 20% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 20%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 70% 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 30%
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