A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

Xv, Yingjie; Lv, Fajin; Guo, Haoming; Liu, Zhaojun; Luo, Di; Liu, Jing; Gou, Xin; He, Wen; Xiao, Mingzhao; Zheng, Yineng

doi:10.3389/fonc.2021.712554

Cited by 16 publications

(12 citation statements)

References 39 publications

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“…All four models have similar and excellent performance and detailed AUC values are respectively displayed in the graph, indicating the robust performance of quantitative features after LASSO selection. (20,21). In this retrospective study, using stacking ensemble machine learning methods, we achieved excellent diagnostic performance in discriminating benign CRLs from malignant CRLs, which outperformed the Bosniak classification system.…”

Section: Discussionmentioning

confidence: 82%

“…Previous findings suggested that the progression of Bosniak IIF cystic renal masses was 4 years ( 19 ). The high-risk Bosniak CRL had a quick progression that required radical nephrectomy rather than inappropriate surgical procedures like renal cyst decortication ( 20 , 21 ). In this retrospective study, using stacking ensemble machine learning methods, we achieved excellent diagnostic performance in discriminating benign CRLs from malignant CRLs, which outperformed the Bosniak classification system.…”

Section: Discussionmentioning

confidence: 99%

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Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Tan

Liao

et al. 2022

Front. Oncol.

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Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature’s reproducibility. Pearson correlation coefficients for normal distribution features and Spearman’s rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

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Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Tan

Liao

et al. 2022

Front. Oncol.

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“…According to prior research, the progression of Bosniak IIF cystic renal masses is four years, which indicates a four years follow-up is inevitable [ 25 ]. Rapid progression of the high-risk Bosniak CRL necessitates radical nephrectomy rather than ineffective surgical procedures like renal cyst decortication [ 26 , 27 ]. In this retrospective study, we employed a blending ensemble machine learning model to stratify malignant and benign CRLs in cystic renal masses, which outperformed the Bosniak classification system.…”

Section: Discussionmentioning

confidence: 99%

Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions

Feng

et al. 2023

Insights Imaging

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Background The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the most discriminative candidate features in the development cohort. Feature’s reproducibility was evaluated by intra-class correlation coefficients and inter-class correlation coefficients. Pearson correlation coefficients for normal distribution and Spearman's rank correlation coefficients for non-normal distribution were utilized to remove redundant features. After that, a blending ensemble machine learning model were developed in training cohort. Area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA) were employed to evaluate the performance of the final model in testing cohort. Results The fusion feature-based machine learning algorithm demonstrated excellent diagnostic performance in external validation dataset (AUC = 0.934, ACC = 0.905). Net benefits presented by DCA are higher than Bosniak-2019 version classification for stratifying patients with CRL to the appropriate surgery procedure. Conclusions Fusion feature-based classifier accurately distinguished malignant and benign CRLs which outperformed the Bosniak-2019 version classification and illustrated improved clinical decision-making utility.

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“…Prior to radiomics feature extraction, to reduce feature variability, we performed the following image preprocessing steps, containing gray discretization, intensity normalization, and voxel resampling ( 29 ). Then, radiomics features were extracted from the second phase of DCE-MRI images through the open source PyRadiomics library.…”

Section: Methodsmentioning

confidence: 99%

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer

et al. 2023

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BackgroundThe goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer.MethodsOne hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness.ResultsMultivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78–0.93) and the testing set (AUC, 0.80; 95% CI, 0.65–0.95).ConclusionThe DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

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A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

Cited by 16 publications

References 39 publications

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer

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