Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma

Yang, Chunmei; Huang, Mengping; Li, Shupan; Chen, Jianqiang; Yang, Yuhong; Qin, Na; Huang, Deqing; Shu, Jiwu

doi:10.1016/j.canlet.2019.11.036

Cited by 52 publications

(48 citation statements)

References 34 publications

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“…In contrast to the traditional FNAC approach, medical imaging is non-invasive and can be used to assess and monitor the entire tumour burden temporally and spatially, which reduces the need for investigational surgery and avoids the tedious care of post-surgical patients [5,6]. However, the details of feature changes within radiographic imaging are not always obvious to the naked eye, which limits the diagnostic accuracy of medical imaging [7,8].…”

Section: Introductionmentioning

confidence: 99%

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

Song

Chen

et al. 2021

BMC Med Imaging

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Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

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Section: Introductionmentioning

confidence: 99%

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

Song

Chen

et al. 2021

BMC Med Imaging

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“…Another study also con rmed the applicability of such method [36]. Radiomics model was also suggested to predict differentiation degree and lymph node metastases of extrahepatic cholangiocarcinoma [37]. To our knowledge, no study was performed to investigate individual prediction of survival of pCCA using MRI features.…”

Section: Discussionmentioning

confidence: 90%

Development and validation of magnetic resonance imaging-based nomograms for patients with perihilar cholangiocarcinoma after radical resection: Preoperative prediction of survival

Zhao¹,

Zhang²,

Zhang³

et al. 2020

Preprint

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BackgroundSurvival status prediction for perihilar cholangiocarcinoma (pCCA) patients is essential for postoperative clinical decision making. This study aimed to develop and validate prediction models for overall survival (OS) evaluation in pCCA patients preoperatively.Materials and MethodsA total of 184 patients who had curative resection for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development while other 74 patients for model testing. Imaging-derived radiomics signatures were developed. Independent preoperative clinical predictors were involved independently or in combination with radiomics signatures to construct different preoperative models through multivariable Cox proportional hazards method. The nomograms were constructed to predict OS, and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve and decision curve.ResultsThe clinical model (Modelclinic) was constructed based on three independent variables including preoperative CEA, cN stage and invasion of hepatic artery in images. The model with best performance (Modelclinic&AP&PVP) was build based on three independent variables, SignatureAP and SignaturePVP. In training and testing cohorts, the concordance indexes (C-indexes) of Modelclinic were 0.846 (95% CI, 0.735-0.957) and 0.755 (95% CI, 0.540-969), and Modelclinic&AP&PVP manifested favorable performance with C-indexes of 0.962 (95% CI, 0.905-1) and 0.814 (95% CI, 0.569-1), and both of them outperformed TNM staging system (C-indexes, 0.616, 95% CI, 0.522-0.711 and 0.599, 95% CI, 0.490-0.708). Good agreement was observed in the calibration curves, and favorable clinical utility was validated using the decision curve analysis both for Modelclinic and Modelclinic&AP&PVP.ConclusionsTwo preoperative nomograms were constructed to predict 1-, 3- and 5-years survival following surgery for individual pCCA patients. Such methods are easy to be performed which have clinical application potential for decision-making and patients stratification in randomized, controlled trials.

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“…To date, some studies have evaluated the discriminative ability of different MRI sequences on the basis of radiomics, among which some studies have mentioned to the favorable predictive value of ADC in radiomics analyses on discriminating benign and malignant tumors (25,26). The radiomics model based on ADC sequence has a positive application in the classification of meningioma, cholangiocarcinoma and glioma (27)(28)(29)(30).…”

Section: Discussionmentioning

confidence: 99%

MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors

et al. 2021

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BackgroundWe conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).MethodsForty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal–Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.ResultsThe high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).ConclusionsRadiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.

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Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma

Cited by 52 publications

References 34 publications

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

Development and validation of magnetic resonance imaging-based nomograms for patients with perihilar cholangiocarcinoma after radical resection: Preoperative prediction of survival

MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors

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