Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Yu, Xiangling; Song, Wenlong; Guo, Dajing; Liu, Huan; Zhang, Haiping; He, Xiaojing; Song, Junjie; Zhou, Jun; Liu, Xin-Jie

doi:10.3389/fonc.2020.00459

Cited by 36 publications

(31 citation statements)

References 40 publications

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“…Compared to the same type of research by Yu et al. ( 20 ), the diagnostic efficiency of the nomogram in the training set was lower than that of the radiomics nomogram constructed in their study (AUC = 0.904), while the diagnostic efficiency of the nomogram in the test set was higher than that of theirs (AUC = 0.812), which indicated better stability of our nomogram. This may be caused by the different radiomics signature.…”

Section: Discussioncontrasting

confidence: 79%

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Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Liu

Yang

et al. 2021

Front. Oncol.

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ObjectiveTo establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.MethodsThe clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves.ResultsThe radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801.ConclusionThe radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.

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Section: Discussioncontrasting

confidence: 79%

“…However, to our knowledge, only Yu et al. have focused on predicting EMVI based on radiomics ( 20 ). But their results showed that their radiomics model had poor stability and low sensitivity, which may be resulted from their small amount of data and defects in the modeling method.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Liu

Yang

et al. 2021

Front. Oncol.

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“…There is a trend of increasing interest in radiomics features as non-invasive imaging tools for estimation of pathological or histological features, distinction of benign and malignant entities, prediction of prognosis or treatment response, and inference to the genetic expression ( 10 – 13 ). These imaging biomarkers possess a potential to be more cost effective and provide a more individualized medical care ( 11 , 14 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

et al. 2021

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Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p < 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.

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“…Radiomics shows multiple advantages in evaluating therapeutic response over traditional imaging analysis [7][8][9][10], thereby providing important details of tissue features [11][12][13][14][15][16][17][18][19]. Mounting evidence indicates potential benefits for radiomics in assessing therapeutic response in LARC [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models

et al. 2021

BMC Med Imaging

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Background To validate and compare various MRI-based radiomics models to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) of rectal cancer. Methods A total of 80 patients with locally advanced rectal cancer (LARC) who underwent surgical resection after nCRT were enrolled retrospectively. Rectal MR images were scanned pre- and post-nCRT. The radiomics features were extracted from T2-weighted images, then reduced separately by least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Four classifiers of Logistic Regression, Random Forest (RF), Decision Tree and K-nearest neighbor (KNN) models were constructed to assess the tumor regression grade (TRG) and pathologic complete response (pCR), respectively. The diagnostic performances of models were determined with leave-one-out cross-validation by generating receiver operating characteristic curves and decision curve analysis. Results Three features related to the TRG and 11 features related to the pCR were obtained by LASSO. Top five principal components representing a cumulative contribution of 80% to overall features were selected by PCA. For TRG, the area under the curve (AUC) of RF model was 0.943 for LASSO and 0.930 for PCA, higher than other models (P < 0.05 for both). As for pCR, the AUCs of KNN for LASSO and PCA were 0.945 and 0.712, higher than other models (P < 0.05 for both). The DCA showed that LASSO algorithm was clinically superior to PCA. Conclusion MRI-based radiomics models demonstrated good performance for evaluating the treatment response of LARC after nCRT and LASSO algorithm yielded more clinical benefit.

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Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Cited by 36 publications

References 40 publications

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture

Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models

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