Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning–Based Study for Predicting Histopathologic Nuclear Grade

Koçak, Burak; Durmaz, Emine Şebnem; Ateş, Ece; Kaya, Ozlem Korkmaz; Kılıçkesmez, Özgür

doi:10.2214/ajr.18.20742

Cited by 55 publications

(39 citation statements)

References 45 publications

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“…Two studies assessed the accuracy of non-enhanced-CT scan images to differentiate between low-and high-grade tumors. An 85.1% accuracy was reported using ML-based TA, similar to the 81.5% accuracy reported when using ANN algorithms [42,43]. Moreover, most studies built models with texture features retrieved from different phases of CECT scans.…”

Section: Nuclear Grade Predictionsupporting

confidence: 68%

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Suarez-Ibarrola

Basulto-Martínez

Heinze

et al. 2020

Cancers

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Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.

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Section: Nuclear Grade Predictionsupporting

confidence: 68%

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Suarez-Ibarrola

Basulto-Martínez

Heinze

et al. 2020

Cancers

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“…Microscopic heterogeneity could also be reflected by tumors grossly. Interestingly, texture analysis provides a plausible method to non-invasively evaluate macroscopic heterogeneity (15,24). Li et al (25) have explored the potential application of CT-based texture analysis in discriminating atypical pancreatic neuroendocrine tumors (PNETs) from PDAC.…”

Section: Discussionmentioning

confidence: 99%

Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma

et al. 2020

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The purpose was to assess the predictive ability of computed tomography (CT)-based radiomics signature in differential diagnosis between pancreatic adenosquamous carcinoma (PASC) and pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: Eighty-one patients (63.6 ± 8.8 years old) with PDAC and 31 patients (64.7 ± 11.1 years old) with PASC who underwent preoperative CE-CT were included. A total of 792 radiomics features were extracted from the late arterial phase (n = 396) and portal venous phase (n = 396) for each case. Significantly different features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy and maximum relevance method. A radiomics signature was constructed using random forest method, the robustness and the reliability of which was validated using 10-times leave group out cross-validation (LGOCV) method. Results: Seven radiomics features from late arterial phase images and three from portal venous phase images were finally selected. The radiomics signature performed well in differential diagnosis between PASC and PDAC, with 94.5% accuracy, 98.3% sensitivity, 90.1% specificity, 91.9% positive predictive value (PPV), and 97.8% negative predictive value (NPV). Moreover, the radiomics signature was proved to be robust and reliable using the LGOCV method, with 76.4% accuracy, 91.1% sensitivity, 70.8% specificity, 56.7% PPV, and 96.2% NPV. Conclusion: CT-based radiomics signature may serve as a promising non-invasive method in differential diagnosis between PASC and PDAC.

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“…In order to predict the PM status of patients with GC, we used the least absolute shrinkage and selection operator (LASSO) logistic regression model to select the optimal radiomics features from the primary texture features, and then, the development of the radiomics score (Rad-score) was constructed in the training cohort (21). For further detecting and addressing the collinearity among features, scatterplot correlation matrix with Person correlation coefficient was applied to investigate the interrelationship among the primary selected features and PM status, and if features had a correlation coefficient that was higher than 0.80 between each other, then the one with the highest collinearity was excluded from the analysis (22)(23)(24). In this study, we used the R software (version 3.5.3) with the "glmnet" package to perform the LASSO regression (25,26).…”

Section: Radiomics Feature Selection and Signature Developmentmentioning

confidence: 99%

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

et al. 2020

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The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703-0.799), 0.802 (95%CI, 0.691-0.912), and 0.745 (95%CI, 0.683-0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748-0.836), 0.870 (95%CI, 0.795-0.946), and 0.815 (95%CI, 0.763-0.867), respectively. Conclusions: CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.

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Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning–Based Study for Predicting Histopathologic Nuclear Grade

Cited by 55 publications

References 45 publications

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

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