Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat

Yang, Ruimeng; Wu, Jialiang; Sun, Lei; Lai, Shih‐Wei; Xu, Yu; Liu, Xiaoyang; Ma, Ying; Zhen, Xin

doi:10.1007/s00330-019-06384-5

Cited by 68 publications

(39 citation statements)

References 16 publications

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“…This study also employed a novel method of using classifier fusion for MVI prediction modeling, such as a process analogous to disease diagnosis by a MDT, produce more reliable and accurate predictions compared with a single classifier. This was based on two reasons: 1) First, there is large performance differences between different classifiers, which has been confirmed in our previous study (31). It is not practical to select a suitable classifier for a given task from a large pool of classifiers, since different classifiers are built on different mathematical grounds; 2) Second, fusion of classifiers has been proved to generate more stable and reproducible classification performance than an individual classifier, and is effective in improving classification/prediction accuracy in decision-making (32,32,(42)(43)(44).…”

Section: Discussionsupporting

confidence: 62%

Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography

et al. 2021

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ObjectiveTo investigate microvascular invasion (MVI) of HCC through a noninvasive multi-disciplinary team (MDT)-like radiomics fusion model on dynamic contrast enhanced (DCE) computed tomography (CT).MethodsThis retrospective study included 111 patients with pathologically proven hepatocellular carcinoma, which comprised 57 MVI-positive and 54 MVI-negative patients. Target volume of interest (VOI) was delineated on four DCE CT phases. The volume of tumor core (Vtc) and seven peripheral tumor regions (Vpt, with varying distances of 2, 4, 6, 8, 10, 12, and 14 mm to tumor margin) were obtained. Radiomics features extracted from different combinations of phase(s) and VOI(s) were cross-validated by 150 classification models. The best phase and VOI (or combinations) were determined. The top predictive models were ranked and screened by cross-validation on the training/validation set. The model fusion, a procedure analogous to multidisciplinary consultation, was performed on the top-3 models to generate a final model, which was validated on an independent testing set.ResultsImage features extracted from Vtc+Vpt(12mm) in the portal venous phase (PVP) showed dominant predictive performances. The top ranked features from Vtc+Vpt(12mm) in PVP included one gray level size zone matrix (GLSZM)-based feature and four first-order based features. Model fusion outperformed a single model in MVI prediction. The weighted fusion method achieved the best predictive performance with an AUC of 0.81, accuracy of 78.3%, sensitivity of 81.8%, and specificity of 75% on the independent testing set.ConclusionImage features extracted from the PVP with Vtc+Vpt(12mm) are the most reliable features indicative of MVI. The MDT-like radiomics fusion model is a promising tool to generate accurate and reproducible results in MVI status prediction in HCC.

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Section: Discussionsupporting

confidence: 62%

Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography

et al. 2021

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“…Likewise, other studies focusing on the value of radiomics in predicting Fuhrman grade of ccRCC also revealed that models based on nephrographic phase had the highest discrimination power and contained more radiomics features than models based on other phases (10)(11)(12)). Yet, several researchers reported that the unenhanced phase had better performance in differentiating RCC and angiomyolipoma than other phases (27,28), suggesting that radiomics features from distinct CT phases might have different advantages in distinct areas.…”

Section: Discussionmentioning

confidence: 99%

The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

et al. 2021

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Background and purposeRadiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown.MethodsComputed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan–Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model’s validation.ResultsHierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn’t distinguish patients’ prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models.ConclusionWe firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.

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“…By applying artificial neural network (ANN) classifiers, Yan et al showed that texture analysis (TA) may be a reliable quantitative strategy to differentiate between AMLwvf, ccRCC, and pRCC with an accuracy in the range of 90.7-100% based on three-phasic CECT scan images [20]. Other investigations employed similar strategies, although with ML-based TA from CECT images, and they reported higher accuracy (93.9%) and AUC (0.955) [21,24]. Moreover, Cui et al proposed an automatic computer identification system to differentiate AMLwvf from all RCC subtypes from whole-tumor CECT images using an over-sampling technique to increase the sample volume of AMLwvf [23].…”

Section: Angiomyolipoma (Amlwvf) Vs Rcc Subtypesmentioning

confidence: 99%

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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Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat

Cited by 68 publications

References 16 publications

Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography

Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography

The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

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

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