Radiomics analysis of contrast-enhanced T1W MRI: predicting the recurrence of acute pancreatitis

Tang, Lingling; Ma, Lin; Chen, Yuying; Hu, Yuntao; Chen, Xinyue; Huang, Xiaohua; Liu, Nian

doi:10.1038/s41598-022-13650-y

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…The fact that texture analysis of the pancreas is an important predictor of AP prognosis confirms its significance in the initial diagnosis of patients with CT indication. Previous studies pointed out the relevance of radiomics analyses in the prediction of AP recurrence [19,34,35]. Furthermore, the prediction of AP severity is of high clinical relevance.…”

Section: Discussionmentioning

confidence: 99%

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography

Bette,

Canalini,

Feitelson

et al. 2024

Diagnostics

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In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

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Section: Discussionmentioning

confidence: 99%

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography

Bette,

Canalini,

Feitelson

et al. 2024

Diagnostics

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“…Quantitative imaging features are extracted from digital images in a high-throughput manner and changes in the human body at the tissue, cellular, and gene level can therefore be reflected by radiomics ( 18 , 19 ). Studies have found that radiomics models can be used to evaluate the severity of AP and indicate the prognosis and evolution of pancreatitis ( 20 - 22 ). One previous study developed a computed tomography (CT)-based radiomics nomogram to predict the incidence of PPDM-A, which was a single-center study with a small sample size, and only radiomic features were extracted from unenhanced plain CT images, ignoring potentially valuable information ( 23 ).…”

Section: Introductionmentioning

confidence: 99%

A radiomics model of contrast-enhanced computed tomography for predicting post-acute pancreatitis diabetes mellitus

Hu,

Yang,

Zeng

et al. 2024

Quant Imaging Med Surg

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Background Diabetes mellitus can occur after acute pancreatitis (AP), but the accurate quantitative methods to predict post-acute pancreatitis diabetes mellitus (PPDM-A) are lacking. This retrospective study aimed to establish a radiomics model based on contrast-enhanced computed tomography (CECT) for predicting PPDM-A. Methods A total of 374 patients with first-episode AP were retrospectively enrolled from two tertiary referral centers. There were 224 patients in the training cohort, 56 in the internal validation cohort, and 94 in the external validation cohort, and there were 86, 22, and 27 patients with PPDM-A in these cohorts, respectively. The clinical characteristics were collected from the hospital information system. A total of 2,398 radiomics features, including shape-based features, first-order histogram features, high order textural features, and transformed features, were extracted from the arterial- and venous-phase CECT images. Intraclass correlation coefficients were used to assess the intraobserver reliability and interobserver agreement. Random forest-based recursive feature elimination, collinearity analysis, and least absolute shrinkage and selection operator (LASSO) were used for selecting the final features. Three classification methods [eXtreme Gradient Boosting (XGBoost), Adaptive Boosting, and Decision Tree] were used to build three models and performances of the three models were compared. Each of the three classification methods were used to establish the clinical model, radiomics model, and combined model for predicting PPDM-A, resulting in a total of nine classifiers. The predictive performances of the models were evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Eleven radiomics features were selected after a reproducibility test and dimensionality reduction. Among the three classification methods, the XGBoost classifier showed better and more consistent performances. The AUC of the XGBoost’s radiomics model to predict PPDM-A in the training, internal, and external cohorts was good (0.964, 0.901, and 0.857, respectively). The AUC of the XGBoost’s combined model to predict PPDM-A in the training, internal, and external cohorts was good (0.980, 0.901, and 0.882, respectively). The AUC of the XGBoost’s clinical model to predict PPDM-A in the training, internal, and external cohorts did not perform well (0.685, 0.733, and 0.619, respectively). In the external validation cohort, the AUC of the XGBoost’s radiomics model was significantly higher than that of the clinical model (0.857 vs. 0.619, P<0.001), but there was no significant difference between the combined and radiomics models (0.882 vs. 0.857, P=0.317). Conclusions The radiomics model based on CECT performs well and can be used as an earl...

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AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis

Maletz,

Balagurunathan,

Murphy

et al. 2024

Abdom Radiol

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Radiomics analysis of contrast-enhanced T1W MRI: predicting the recurrence of acute pancreatitis

Cited by 6 publications

References 38 publications

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography

A radiomics model of contrast-enhanced computed tomography for predicting post-acute pancreatitis diabetes mellitus

AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis

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