A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data

Wang, Jincheng; Fu, Rao; Tao, Xuewen; Mao, Yingfan; Wang, Fei; Zhang, Zechuan; Yu, Weiwei; Chen, Jun; He, Jian; Sun, Beicheng

doi:10.1186/s40364-020-00219-y

Cited by 35 publications

(25 citation statements)

References 40 publications

(40 reference statements)

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“…Nomogram modelling has been used effectively in a number of studies. 29,30 We recommend that patients who are judged to be at high risk using our diagnostic model should have supplemental CT scans before surgery to confirm the diagnosis and rule-out abdominal metastasis of GBC. Additionally, compared with artificially assigning risk factors, assigning corresponding weights to variables through statistical methods may result in more objective extraction of information from clinical data.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram-based Model to Predict Neoplastic Risk for Patients with Gallbladder Polyps

Zhang

Wang

Qin

et al. 2021

J Clin Transl Hepatol

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Background and Aims: Gallbladder polyp (GBP) assessment aims to identify the early stages of gallbladder carcinoma. Many studies have analyzed the risk factors for malignant GBPs. In this retrospective study, we aimed to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs. Methods: We developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood test findings were analyzed. Mann-Whitney U test and multivariate logistic regression analyses were used to identify independent predictors and establish the nomogram model. An internal validation was conducted in 225 consecutive patients. Performance and clinical benefit of the model were evaluated using receiver operating characteristic curves and decision curve analysis (DCA), respectively. Results: Age, cholelithiasis, carcinoembryonic antigen, polyp size, and sessile shape were confirmed as independent predictors of GBP neoplastic potential in the training group. Compared with five other proposed prediction methods, the established nomogram model presented better discrimination of neoplastic GBPs in the training cohort (area under the curve [AUC]: 0.846) and the validation cohort (AUC: 0.835). DCA demonstrated that the greatest clinical benefit was provided by the nomogram compared with the other five methods. Conclusions: Our developed preoperative nomogram model can successfully be used to evaluate the neoplastic potential of GBPs based on simple clinical variables that maybe useful for clinical decisionmaking.

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Section: Discussionmentioning

confidence: 99%

A Nomogram-based Model to Predict Neoplastic Risk for Patients with Gallbladder Polyps

Zhang

Wang

Qin

et al. 2021

J Clin Transl Hepatol

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“…All the indicators included in our model can be obtained through physical examination of outpatient. Modelling by using nomograms has been used in a number of studies and has been shown to be effective [29] [30]. We recommend that patients who are judged to be at high risk through our diagnostic model could be supplemented with CT scans before surgery to further con rm the diagnosis and rule out gallbladder cancer abdominal metastasis.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

Zhang

Wang

et al. 2021

Preprint

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Background: Gallbladder polyps (GBPs) assessment seeks to identify early-stage gallbladder carcinoma (GBC). Many studies have analyzed the risk factors for malignant GBPs, and we try to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.Methods: This retrospective study developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. Spearman correlation and logistic regression analysis were used to identify independent predictors and establish a nomogram model. An internal validation was conducted in 225 consecutive patients. Performance of models was evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results: Age, cholelithiasis, CEA, polyp size and sessile were confirmed as independent predictors for neoplastic potential of GBPs in the training group. Compared with other proposed prediction methods, the established nomogram model presented good discrimination ability in the training cohort (area under the curve [AUC]: 0.845) and the validation cohort (AUC: 0.836). DCA demonstrated the most clinical benefits can be provided by the nomogram. Conclusions: Our developed preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs based on simple clinical variables, that maybe useful for clinical decision-making.

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“…Regions of interest (ROIs) were selected in the liver of all patients by two radiologists using 3D slicer (version 4.8.0; http://www.slicer.org ) [ 17 ]. The ROIs were manually segmented along the tumor contour on each transverse section.…”

Section: Methodsmentioning

confidence: 99%

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis

Mao

Tang

et al. 2022

Computational and Mathematical Methods in Medicine

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Introduction. Considering the narrow window of surgery, early diagnosis of liver cancer is still a fundamental issue to explore. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA) are considered as two different types of liver cancer because of their distinct pathogenesis, pathological features, prognosis, and responses to adjuvant therapies. Qualitative analysis of image is not enough to make a discrimination of liver cancer, especially early-stage HCC or ICCA. Methods. This retrospective study developed a radiomic-based model in a training cohort of 122 patients. Radiomic features were extracted from computed tomography (CT) scans. Feature selection was operated with the least absolute shrinkage and operator (LASSO) logistic method. The support vector machine (SVM) was selected to build a model. An internal validation was conducted in 89 patients. Results. In the training set, the AUC of the evaluation of the radiomics was 0.855 higher than for radiologists at 0.689. In the valuation cohorts, the AUC of the evaluation was 0.847 and the validation was 0.659, which indicated that the established model has a significantly better performance in distinguishing the HCC from ICCA. Conclusion. We developed a radiomic diagnosis model based on CT image that can quickly distinguish HCC from ICCA, which may facilitate the differential diagnosis of HCC and ICCA in the future.

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A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data

Cited by 35 publications

References 40 publications

A Nomogram-based Model to Predict Neoplastic Risk for Patients with Gallbladder Polyps

A Nomogram-based Model to Predict Neoplastic Risk for Patients with Gallbladder Polyps

A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis

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