Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer

Shu, Zhenyu; Mao, Dewang; Song, Qiaowei; Xu, Yuqian; Pang, Peipei; Zhang, Yang

doi:10.1007/s00330-021-08242-9

Cited by 27 publications

(18 citation statements)

References 30 publications

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“…The MRI-based radiomics and machine learning showed that the Bayes-based radiomics signature performed better compared with other LR-based, SVM-based, KNN-based, and RF-based radiomics signature to predict the extramural venous invasion in RC patients. 19 The deep learning based on high-resolution T2-weighted magnetic resonance images showed good predictive performance for MSI status in RC patients. 20 The multivariate analysis of a previous study to predict the treatment response of RC patients found that RF and KNN achieved the highest AUC among pre-treatment and post-treatment features.…”

Section: Discussionmentioning

confidence: 88%

A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

Yuan¹,

Yu²,

Xu³

et al. 2022

CMAR

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Objective To predict the status of microsatellite instability (MSI) of rectal carcinoma (RC) using different machine learning algorithms based on tumoral and peritumoral radiomics combined with clinicopathological characteristics. Methods There were 497 RC patients enrolled in this retrospective study. The tumoral and peritumoral CT-based radiomic features were calculated after tumor segmentation. The radiomic features from two radiologists were compared by way of inter-observer correlation coefficient (ICC). After methods of variance, correlation, and dimension reduction, six machine learning algorithms of logistic regression (LR), Bayes, support vector machine, random forest, k-nearest neighbor, and decision tree were conducted to develop models for predicting MSI status of RC. The relative standard deviation (RSD) was quantified. The radiomics and significant clinicopathological variables constituted the radiomics-clinicopathological nomogram. The receiver operator curve (ROC) was made by DeLong test, and the area under curve (AUC) with 95% confidence interval (95% CI) was calculated to evaluate the performance of the model. Results The venous phase of CT examination was selected for further analysis because the proportion of radiomic features with ICC greater than 0.75 was higher. The tumoral and peritumoral model by LR algorithm (M-LR) with minimal RSD showed good performance in predicting MSI status of RC with the AUCs of 0.817 and 0.726 in the training and validation set. The radiomic-clinicopathological nomogram performed better in both the training and validation set with AUCs of 0.843 and 0.737. Conclusion The radiomics-clinicopathological nomogram demonstrated better predictive performance in evaluating the MSI status of RC.

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Section: Discussionmentioning

confidence: 88%

A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

Yuan¹,

Yu²,

Xu³

et al. 2022

CMAR

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“…The present study was performed to explore the preoperative predictive value of biparametric MRI based radiomics features for LVI of rectal cancer, and the results showed that the radiomics models based on T2WI and DWI had good performance. A recent study of rectal cancer showed that a multiparameter radiomics model had good performance in predicting extramural venous invasion of rectal cancer [20].…”

Section: Discussionmentioning

confidence: 99%

Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer

et al. 2023

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Background Preoperative assessment of lymphovascular invasion(LVI) of rectal cancer has very important clinical significance. However, accurate preoperative imaging evaluation of LVI is highly challenging because the resolution of MRI is still limited. Relatively few studies have focused on prediction of LVI of rectal cancer with the tool of radiomics, especially in patients with negative statue of MRI-based extramural vascular invasion (mrEMVI).The purpose of this study was to explore the preoperative predictive value of biparametric MRI-based radiomics features for LVI of rectal cancer in patients with the negative statue of mrEMVI. Methods The data of 146 cases of rectal adenocarcinoma confirmed by postoperative pathology were retrospectively collected. In the cases, 38 had positive status of LVI. All patients were examined by MRI before the operation. The biparametric MRI protocols included T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). We used whole-volume three-dimensional method and two feature selection methods, minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO), to extract and select the features. Logistics regression was used to construct models. The area under the receiver operating characteristic curve (AUC) and DeLong’s test were used to evaluate the diagnostic performance of the radiomics based on T2WI and DWI and the combined models. Results Radiomics models based on T2WI and DWI had good predictive performance for LVI of rectal cancer in both the training cohort and the validation cohort. The AUCs of the T2WI model were 0.87 and 0.87, and the AUCs of the DWI model were 0.94 and 0.92. The combined model was better than the T2WI model, with AUCs of 0.97 and 0.95. The predictive performance of the DWI model was comparable to that of the combined model. Conclusions The radiomics model based on biparametric MRI, especially DWI, had good predictive value for LVI of rectal cancer. This model has the potential to facilitate the clinical recognition of LVI in rectal cancer preoperatively.

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“…Although the diagnostic value of RF was slightly higher than that of the MLP, the latter was found to be more stable than the former in the training cohort. The stability of the machine learning classifier is also very important for its model construction and clinical application ( 22 ). Therefore, the MLP was chosen to develop the radiomics and mixed model.…”

Section: Discussionmentioning

confidence: 99%

“…To select the best machine learning classifier, relative standard deviation (RSD) was employed to quantify the stability of the six classifiers. RSD ( 22 ) was defined as the ratio between the standard deviation and mean of the fivefold cross-validation AUC values in the training cohort:…”

Section: Methodsmentioning

confidence: 99%

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

Zhang

et al. 2022

Front. Oncol.

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ObjectivesThis study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.MethodsA total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.ResultsThe MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001).ConclusionsMachine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

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Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer

Cited by 27 publications

References 30 publications

A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma

Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

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