Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?

Ma, Yanqing; Cao, Fang; Xu, Xiren; Ma, Wei

doi:10.1007/s00261-020-02414-9

Cited by 31 publications

(27 citation statements)

References 19 publications

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“…meaningful phenomena previously (27), but showed no statistical significance in this study. Wedge-shaped signs and necrotic or cystic lesions showed a significant difference between RAML-wvf and ccRCC, according to Ma et al (28), but they did not show statistical significance in multivariate analysis in this study. Among the independent risk factors, a non-high CT attenuation of the tumor in pre-enhancement scanning is the predominant factor, which means the tumor attenuation is lower than the attenuation of renal parenchyma.…”

Section: Discussioncontrasting

confidence: 66%

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features

Wang

Zhou

et al. 2021

Front. Oncol.

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BackgroundRenal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging.MethodsA total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated.ResultsFour independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956–0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954–1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854–0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2–4 points; >4 to ≤11 points) significantly increased with increasing scores.ConclusionThis scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.

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Section: Discussioncontrasting

confidence: 66%

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features

Wang

Zhou

et al. 2021

Front. Oncol.

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“…However, up to 5% of AMLs lack macroscopic fat, a condition known as AML without visible fat (AMLwvf), making it challenging to differentiate them from RCC by conventional imaging [36]. Several studies described certain imaging features that are highly suggestive of AMLwvf and analyzed them with radiomics models [20][21][22][23]25,26].…”

Section: Angiomyolipoma (Amlwvf) Vs Rcc Subtypesmentioning

confidence: 99%

“…While some studies investigated deep feature classification, by using ML and ANN-based classifiers, to improve texture features to further enhance the differentiation between AMLwvf and RCC [22], others built radiomics nomograms from these texture features, showing that they outperform other models using solely clinical factors [25]. Interestingly, Ma et al not only showed that whole-tumor radiomics-based CECT analysis is superior to conventional CECT analysis, but also that the unenhanced phase showed higher AUC than the corticomedullar phase (CMP) and nephrographic phase (NP) [26]. These findings are encouraging for the future acquisition of imaging studies without the need of contrast agents to spare patients with renal masses the associated radiation burden of multi-phasic CECT.…”

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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“… 9 Previous publications reported that CT-based radiomics signature demonstrated better performance in distinguishing fp-AMLs from ccRCCs. 10 According to another result of CT texture analysis in previous literature, renal cell carcinomas (RCCs) showed lower homogeneity and higher entropy compared with fp-AMLs. 11 Ideally, radiomics features performed promising results in evaluating the Fuhrman grades of the ccRCC.…”

Section: Introductionmentioning

confidence: 98%

A CT-Based Tumoral and Mini-Peritumoral Radiomics Approach: Differentiate Fat-Poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma

Ma¹,

Xu²,

Pang³

et al. 2021

CMAR

Self Cite

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Objective This study aimed to evaluate the role of tumor and mini-peritumor in the context of CT-based radiomics analysis to differentiate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC). Methods A total of 58 fp-AMLs and 172 ccRCCs were enrolled. The volume of interest (VOI) was manually delineated in the standardized CT images and radiomics features were automatically calculated with software. After methods of feature selection, the CT-based logistic models including tumoral model (Ra-tumor), mini-peritumoral model (Ra-peritumor), perirenal model (Ra-Pr), perifat model (Ra-Pf), and tumoral+perirenal model (Ra-tumor+Pr) were constructed. The area under curves (AUCs) were calculated by DeLong test to evaluate the efficiency of logistic models. Results The AUCs of Ra-peritumor of nephrographic phase (NP) were slightly higher than those of corticomedullary phase (CMP). Furthermore, the Ra-Pr showed significant higher efficiency than the Ra-Pf, and relative more optimal radiomics features were selected in the Ra-Pr than Ra-Pf. The Ra-tumor+Pr combined tumoral and perirenal radiomics analysis was of most significant in distinction compared with Ra-tumor and Ra-peritumor. Conclusion The validity of NP to differentiate fp-AML from ccRCC was slightly higher than that of CMP. To the NP analysis, the Ra-Pr was superior to the Ra-Pf in distinction, and the lesions invaded to the perirenal tissue more severely than to the perifat tissue. It is important to the individual therapeutic surgeries according to the different lesion location. The pooled tumoral and perirenal radiomics analysis was the most promising approach in distinguishing fp-AML and ccRCC.

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Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?

Cited by 31 publications

References 19 publications

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features

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

A CT-Based Tumoral and Mini-Peritumoral Radiomics Approach: Differentiate Fat-Poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma

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