Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Yan, Gaowu; Gaowen, Yan; Li, Hongwei; Liang, Hongwei; Peng, Chen; Bhetuwal, Anup; McClure, Morgan A.; Li, Yongmei; Yang, Guoqing; Li, Yong; Zhao, Linwei; Fan, Xiaoping

doi:10.3389/fmed.2022.922299

Cited by 4 publications

(1 citation statement)

References 90 publications

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“…A systematic review by Zhou Y et al [ 51 ] indicated that radiomics exhibits desirable accuracy in the differential diagnosis of pancreatitis and pancreatic cancer, although this conclusion is drawn from a limited number of original studies. Another systematic review by Yan et al [ 52 ] suggested that contrast-enhanced MRI may have more favorable accuracy. Tarján D et al [ 53 ] developed an AI-based early prediction tool for the severity of AP; however, this tool has not been validated with a large number of real cases.…”

Section: Discussionmentioning

confidence: 99%

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Qian,

Zhuang,

Xie

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Qian,

Zhuang,

Xie

et al. 2024

Heliyon

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Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT

Rocca,

Brunese,

Santone

et al. 2024

Radiol med

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Introduction Acute pancreatitis (AP) is a common disease, and several scores aim to assess its prognosis. Our study aims to automatically recognize mild AP from computed tomography (CT) images in patients with acute abdominal pain but uncertain diagnosis from clinical and serological data through Radiomic model based on formal methods (FMs). Methods We retrospectively reviewed the CT scans acquired with Dual Source 256-slice CT scanner (Somatom Definition Flash; Siemens Healthineers, Erlangen, Germany) of 80 patients admitted to the radiology unit of Antonio Cardarelli hospital (Naples) with acute abdominal pain. Patients were divided into 2 groups: 40 underwent showed a healthy pancreatic gland, and 40 affected by four different grades (CTSI 0, 1, 2, 3) of mild pancreatitis at CT without clear clinical presentation or biochemical findings. Segmentation was manually performed. Radiologists identified 6 patients with a high expression of diseases (CTSI 3) to formulate a formal property (Rule) to detect AP in the testing set automatically. Once the rule was formulated, and Model Checker classified 70 patients into “healthy” or “unhealthy”. Results The model achieved: accuracy 81%, precision 78% and recall 81%. Combining FMs results with radiologists agreement, and applying the mode in clinical practice, the global accuracy would have been 100%. Conclusions Our model was reliable to automatically detect mild AP at primary diagnosis even in uncertain presentation and it will be tested prospectively in clinical practice.

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Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis

Pan,

Jiao,

et al. 2024

British Journal of Radiology

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Purpose To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological information on CT images to assist improved and faster diagnosis in acute pancreatitis. Methods This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 to September 2022. Patients were divided into training (N = 688), validation (N = 145), testing dataset (N = 291, N = 104 for normal pancreas, N = 98 for AP, N = 89 for AP complicated with PDAC (AP&PDAC)). A model based on convolutional neural network (MSAnet) was developed. The pancreas segmentation and measurement were performed via eight open-source models and MSAnet based tools, and the efficacy was evaluated using Dice similarity coefficient (DSC) and Intersection over union (IoU). The DSC and IoU for patients with different ages were also compared. The outline of tumor and edema in the AP and were segmented by clustering. The diagnostic efficacy for radiologists with or without the assistance of MSAnet tool in AP and AP&PDAC was evaluated using receiver operation curve and confusion matrix. Results Among all models, MSAnet based tool showed best performance on the training and validation dataset, and had high efficacy on testing dataset. The performance was age-affected. With assistance of the AI tool, the diagnosis time was significantly shortened by 26.8% and 32.7% for junior and senior radiologists, respectively. The area under curve in diagnosis of AP was improved from 0.91 to 0.96 for junior radiologist and 0.98 to 0.99 for senior radiologist. In AP&PDAC diagnosis, AUC was increased from 0.85 to 0.92 for junior and 0.97 to 0.99 for senior. Conclusion MSAnet based tools showed good pancreas segmentation and measurement performance, which help radiologists improve diagnosis efficacy and workflow in both AP and AP with PDAC conditions. Advances in knowledge This study developed an AI tool with automated pancreas segmentation and measurement and provided evidence for AI tool assistance in improving the workflow and accuracy of AP diagnosis.

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Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Cited by 4 publications

References 90 publications

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT

Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis

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