A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study

Nie, Pei; Yang, Guangjie; Wang, Yanmei; Xu, Yuchao; Yan, Lei; Zhang, Mingxin; Zhao, Lianzi; Wang, Ning; Zhao, Xia; Li, Xianjun; Cheng, Nan; Wang, Yicong; Chen, Chengcheng; Wang, Nan; Duan, Shaofeng; Wang, Ximing; Wang, Zhenguang

doi:10.1007/s00330-023-09869-6

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…In our study, we screened out crucial radiomics features from 1,316 candidate features and finally selected 14 radiomics features that can predict the condition of LVI, in which the wavelet filter provides more information ( n = 7). These results indicate that the wavelet filter provides the best radiomics information on tumor heterogeneity and is the best available option, in accordance with the results of other radiomics studies ( 21 ). Among the selected radiomics features in this study, GLSZM ( n = 7), NGTDM ( n = 1), GLRLM ( n = 3), and GLCM ( n = 1) are high-order texture features, which can accurately reflect tumor heterogeneity.…”

Section: Discussionsupporting

confidence: 90%

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Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma

Wang,

Bai,

Huang

et al. 2024

Front. Oncol.

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ObjectiveTo evaluate the value of a machine learning model using enhanced CT radiomics features in the prediction of lymphovascular invasion (LVI) of esophageal squamous cell carcinoma (ESCC) before treatment.MethodsWe reviewed and analyzed the enhanced CT images of 258 ESCC patients from June 2017 to December 2019. We randomly assigned the patients in a ratio of 7:3 to a training set (182 cases) and a validation (76 cases) set. Clinical risk factors and CT image characteristics were recorded, and multifactor logistic regression was used to screen independent risk factors of LVI of ESCC patients. We extracted the CT radiomics features using the FAE software and screened radiomics features using maximum relevance and minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, and finally, the radiomics labels of each patient were established. Five machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), Gauss naive Bayes (GNB), and multilayer perceptron (MLP), were used to construct the model of radiomics labels, and its clinical features were screened. The predictive efficacy of the machine learning model for LVI of ESCC was evaluated using the receiver operating characteristic (ROC) curve.ResultsTumor thickness [OR = 1.189, 95% confidence interval (CI) 1.060–1.351, P = 0.005], tumor-to-normal wall enhancement ratio (TNR) (OR = 2.966, 95% CI 1.174–7.894, P = 0.024), and clinical N stage (OR = 5.828, 95% CI 1.752–20.811, P = 0.005) were determined as independent risk factors of LVI. We extracted 1,316 features from preoperative enhanced CT images and selected 14 radiomics features using MRMR and LASSO to construct the radiomics labels. In the test set, SVM, KNN, LR, and GNB showed high predictive performance, while the MLP model had poor performance. In the training set, the area under the curve (AUC) values were 0.945 and 0.905 in the KNN and SVM models, but these decreased to 0.866 and 0.867 in the validation set, indicating significant overfitting. The GNB and LR models had AUC values of 0.905 and 0.911 in the training set and 0.900 and 0.893 in the validation set, with stable performance and good fitting and predictive ability. The MLP model had AUC values of 0.658 and 0.674 in the training and validation sets, indicating poor performance. A multiscale combined model constructed using multivariate logistic regression has an AUC of 0.911 (0.870–0.951) and 0.893 (0.840–0.962), accuracy of 84.4% and 79.7%, sensitivity of 90.8% and 87.1%, and specificity of 80.5% and 79.0% in the training and validation sets, respectively.ConclusionMachine learning models can preoperatively predict the condition of LVI effectively in patients with ESCC based on enhanced CT radiomics features. The GNB and LR models exhibit good stability and may bring a new way for the non-invasive prediction of LVI condition in ESCC patients before treatment.

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Section: Discussionsupporting

confidence: 90%

“…the results of other radiomics studies (21). Among the selected radiomics features in this study, GLSZM (n = 7), NGTDM (n = 1), GLRLM (n = 3), and GLCM (n = 1) are high-order texture features, which can accurately reflect tumor heterogeneity.…”

Section: Roc Curves Of Different Machine Learning Models Of the Train...mentioning

confidence: 82%

Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma

Wang,

Bai,

Huang

et al. 2024

Front. Oncol.

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“…Compared with our previous pathomics-based prognostic models for ccRCC patients, the novel multimodel study showed high accuracy and generalization performance in prognostic prediction. Moreover, the AUC values in our multimodal system were better than some recently published radiomics-based prognostic models, which also showed high AUC values in pure radiomics-based studies 33 , 34 . It was suggested that a predictive model with integration of multiple factors into a single signature was worth for further study.…”

Section: Discussionmentioning

confidence: 42%

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study

Chen,

Gao,

Guo

et al. 2024

International Journal of Surgery

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Background: Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery. Methods: A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively. A multimodel prediction signature (MMPS) for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. Results: Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The MMPS achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443–68.120, P<0.0001), 11.10 (95% CI: 5.467–22.520, P<0.0001), and 8.27 (95% CI: 1.482–46.130, P<0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC. Conclusion: Our novel multimodel prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients.

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“…For example, Pie Nie et al presented a multi-center study on ccRCC through this technique, and the predictive rate was 0.921 with a ROC. However, this study included only ccRCC, and it did not show a significant difference from the assessments made by radiologists [20]. Shengxing Feng et al also published a study similar to ours.…”

Section: Discussionmentioning

confidence: 49%

Development and Validation of a Prediction Model for Differentiation of Benign and Malignant Fat-Poor Renal Tumors Using CT Radiomics

Bang,

Wang,

Kim

et al. 2023

Applied Sciences

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Objectives: To develop and validate a machine learning-based CT radiomics classification model for distinguishing benign renal tumors from malignant renal tumors. Methods: We reviewed 499 patients who underwent nephrectomy for solid renal tumors at our institution between 2003 and 2021. In this retrospective study, patients who had undergone a computed tomography (CT) scan within 3 months before surgery were included. We randomly divided the dataset in a stratified manner as follows: 75% as the training set and 25% as the test set. By using various feature selection methods and a dimensionality reduction method exclusively for the training set, we selected 160 radiomic features out of 1,288 radiomic features to classify malignant renal tumors. Results: The training set included 396 patients, and the test set included 103 patients. The percentage of extracted radiomic features from patients was 32% (385/1218) after the reproducibility test. In terms of the average Area Under the Receiver Operating Characteristic Curve (AU-ROC) and the average Area Under the Precision-Recall Curve (AU-PRC), the Random Forest model achieved better performance (AU-ROC = 0.725; AU-PRC = 0.899). An average accuracy of 0.778 was obtained on evaluation with the hold-out test set. At the optimal threshold, the Random Forest model showed an F1 score of 0.746, precision of 0.862, sensitivity of 0.657, specificity of 0.651, and Negative Predictive Value (NPV) of 0.364. Conclusions: Our machine learning-based CT radiomics classification model performed well for the independent test set, indicating that it could be a useful tool for discriminating between malignant and benign solid renal tumors.

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A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study

Cited by 12 publications

References 32 publications

Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma

Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study

Development and Validation of a Prediction Model for Differentiation of Benign and Malignant Fat-Poor Renal Tumors Using CT Radiomics

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