Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis

Chen, Yong; Chen, Tianwu; Wu, Changqiang; Lin, Qiao; Hu, Ran; Xie, Chao-Lian; Zuo, Hou-Dong; Wu, Jiali; Mu, Qiwen; Fu, Quanshui; Yang, Guoqing; Zhang, Xiao Ming

doi:10.1007/s00330-018-5824-1

Cited by 66 publications

(76 citation statements)

References 29 publications

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“…Several studies have shown that radiomics has strong predictive ability because radiomics reflects the information of the quantitative image analysis rather than the information observed with the naked eye. Our previous studies have also demonstrated the applicability of radiomics in the study of AP . In this study our provided radiomics model has better predictive performance than three scoring systems by integrating high‐dimensional radiomics features.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the characteristics of texture analysis, radiomics is also suitable for nontumorigenic research, although it is currently mainly used in diagnosing cancer . Our previous study has shown that a radiomics model based on CT can accurately predict the recurrence of AP …”

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confidence: 99%

“…9 Our previous study has shown that a radiomics model based on CT can accurately predict the recurrence of AP. 10 Currently, radiologists mainly assess the severity of pancreatitis by observing the morphological changes of the pancreas. However, in the early stage of AP the morphological changes of the pancreas in some patients are not obvious on CT or MR images, especially necrosis of the pancreas, which may cause the severity of the disease to be underestimated.…”

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confidence: 99%

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Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity

Lin

Chen

et al. 2019

Magnetic Resonance Imaging

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Background Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity. Purpose To develop a contrast‐enhanced (CE) MRI‐based radiomics model for the early prediction of AP severity. Study Type Retrospective. Subjects A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe). Field Strength/Sequence 3.0T, T1‐weighted CE‐MRI. Assessment Radiomics features were extracted from the portal venous‐phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP. Statistical Tests Independent t‐test, Mann–Whitney U‐test, chi‐square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test. Results Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI). Data Conclusion The radiomics model had good performance in the early prediction of AP severity. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:397–406.

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Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity

Lin

Chen

et al. 2019

Magnetic Resonance Imaging

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“…All patients were randomly allocated to the training and validation cohorts at the ratio of 7:3 based on the published report by Chen et al [15]. Therefore, 413 oesophageal SCC patients were allocated to the training cohort, of whom 189 and 224 patients were in the resectable and unresectable subgroups, respectively.…”

Section: Methodsmentioning

confidence: 99%

CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study

Zeng

et al. 2019

Cancer Imaging

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Background Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. Methods Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. Results Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values < 0.01 for both cohorts). Multivariable logistic regression model showed the best performance (AUC = 0.92 ± 0.04 and 0.87 ± 0.02, accuracy = 0.87 and 0.86, and F-1score = 0.93 and 0.86 in training and validation cohorts, respectively) in comparison with any other model (P-value < 0.001). Good calibration was observed for multivariable logistic regression model. Conclusion CT radiomic models could help predict resectability of oesophageal SCC, and multivariable logistic regression model is the most predictive model.

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“…In addition to increasing the social burden and affecting the quality of life of the patient with RAP, current epidemiological and imaging data suggest that RAP has a higher risk of developing chronic pancreatitis(CP), and the more times of recurrence, the higher the risk of progression to CP [7,8]. However, most of the literatures around the world focus on the analysis of high risk factors and imaging changes of RAP [9,10] it has not been reported in the literature that there are high risk factors and predictive models related to short-term recurrence of RAP. Therefore, in this study, the clinical data of 261 patients with RAP were retrospectively analyzed to explore the clinical characteristics and risk factors of short-term recurrence of RAP, and to use of non-invasive and quantitative biomarkers to establish a predictive model, so as to provide a basis for effectively predicting and preventing of RAP recurrence.…”

Section: Introductionmentioning

confidence: 99%

Establishment of a Model for Predicting the Recurrence of Recurrent Acute Pancreatitis in A Short Period of Time

Yang

Liu

et al. 2020

Preprint

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Background Little is known about what factors affect recurrence of recurrent acute pancreatitis (RAP) in a short-term. We performed a retrospective study of patients with RAP, and used non-invasive and quantitative biomarkers to establish a predictive model, so as to provide a basis for effectively predicting and preventing recurrence of RAP. MethodsWe performed a retrospective study of 261 RAP patients admitted to Dazhou Central Hospital from January 2008 through December 2018. The short-term group (n=138) and the long-term group (n=123) were compared. Eight clinical patient characteristics and past medical history were collected from admission records. The prediction model was developed by independent risk factors，and the model to predict the effects were confirmed by the area under the ROC cure．ResultsThe study found that β2-microglobulin (β2-MG) (OR, 0.534; 95% CI, 0.329 to 0.866) and C-reactive protein (CRP) (OR, 1.01; 95% CI, 1.002 to 1.017) were the independent predictors of short-term recurrence. Based on these Independent risk factors，a predicted model for short-term recurrence in RAP patients was constructed．The model was L=β2-MG -0.016BCRP. The AUC for model was 0.651 and the optimal cut-off value was 0.7414, with a sensitivity of 65.9%and a specificity of 63.4%.ConclusionsA new biomarker-based model for predicting risk of short-term recurrence in patients with RAP was satisfactorily developed. The model utilizing β2-MG and CRP may assist physicians in patient education and decision-making when preventing from recurrent attacks with RAP.

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Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis

Cited by 66 publications

References 29 publications

Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity

Radiomics model of contrast‐enhanced MRI for early prediction of acute pancreatitis severity

CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study

Establishment of a Model for Predicting the Recurrence of Recurrent Acute Pancreatitis in A Short Period of Time

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