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Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis.

ABSTRACT: OBJECTIVES:(1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies. METHODS:In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed. RESULTS:Fifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93-0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27-9.12; p?

SUBMITTER: Ursprung S

PROVIDER: S-EPMC7248043 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis.

Ursprung Stephan S Beer Lucian L Bruining Annemarie A Woitek Ramona R Stewart Grant D GD Gallagher Ferdia A FA Sala Evis E

European radiology 20200214 6

<h4>Objectives</h4>(1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies.<h4>Methods</h4>In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment ...[more]

PMID: 32060715

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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.MethodsPatients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.ResultsA total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765-0.9585) and 0.8088 (95% CI: 0.7064-0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353-0.8987) and 0.8017 (95% CI: 0.6878-0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646-0.9824) and an AUC of 0.9099 (95% CI: 0.8324-0.9873) for the training cohort and validation cohort, respectively.ConclusionWe developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Project description:The aim of this study was to analyze the computed tomography (CT) and magnetic resonance imaging (MRI) findings of dermatofibrosarcoma protuberans (DFSP), with a view to improving the diagnosis of this kind of tumor. A total of 27 cases of histopathologically confirmed DFSP were analyzed retrospectively. Of these, 18 patients underwent a CT scan and 9 patients underwent an MRI. All patients underwent unenhanced and contrast-enhanced examinations; 1 patient underwent multiphrase CT enhancement examination. Imaging characteristics, including location, shape, size, number, edge, and attenuation or intensity of each lesion, both unenhanced and contrast enhanced, were analyzed. Of the 27 cases, 24 were solitary, 2 had 2 nodules, and 1 had multiple confluent tumors. The lesion with multiple confluent tumors was ill defined and irregular; the other lesions were oval or round, well-defined nodules or masses. The unenhanced CT images showed 19 homogenous isodense lesions. There was no calcification in any of the patients. The contrast-enhanced CT images showed intermediate and marked nonhomogeneous enhancement in 13 lesions, intermediate homogeneous enhancement in 4 lesions, and a mild heterogeneous enhancement in 2 lesions. MR T1-weighted images revealed 1 ill-defined and 9 well-defined homogeneous isointense lesions. T2-weighted images showed homogeneous hyperintensity to the muscles in 6 lesions, 3 mild hyperintense lesions with hypointense lesions, and 1 mixed, mild hyperintense and isointense lesion. Contrast-enhanced T1-weighted images demonstrated intermediate and marked nonhomogeneous enhancement in 9 lesions and intermediate homogeneous enhancement in 1 lesion. DFSP is characterized by a subcutaneous well-defined soft tissue nodule or mass on plain CT/MR scans, and shows intermediate-to-marked enhancement on contrast-enhanced CT/MR scans. The imaging findings for DFSP are nonspecific, but may help to define the diagnosis in an appropriate clinical setting.

Project description:Background and objectiveIschemic stroke is a foremost cause for disability and death worldwide. This study is conducted in order to compare the diagnostic values between transcranial Doppler ultrasound (ultrasonography), computed tomography (CT), and magnetic resonance imaging (MRI) in patients suffering from ischemic stroke by performing a network meta-analysis.MethodsWe made use of Cochrane Library, PubMed, and Embase in order to obtain literature and papers. The combination analysis of both direct and indirect evidence in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy was conducted so as to assess the odds ratios (ORs) and surface under the cumulative ranking curve (SUCRA) values of the seven different imaging methods. These imaging techniques include ultrasonography, computed tomography (traditional CT, computed tomography angiography [CTA], computed tomography perfusion [CTP]), and MRI (traditional MRI, diffusion-weighted imaging [DWI], magnetic resonance angiography), in order to properly diagnose ischemic stroke patients.ResultsThirteen eligible diagnostic trials were enrolled into this network meta-analysis. The results of the traditional meta-analysis showed that among CT methods, CTP showed higher sensitivity, NPV, and accuracy; among MRI methods, DWI had relatively higher sensitivity, NPV, and accuracy. The results of network meta-analysis showed that DWI had relatively higher sensitivity, NPV, and accuracy when compared with traditional CT, CTA, magnetic resonance angiography and traditional MRI. CTP showed higher SUCRA among CT methods while DWI showed higher SUCRA among MRI methods. A cluster analysis revealed that DWI had the highest diagnostic value in terms of sensitivity, PPV, NPV, and accuracy amongst the aforementioned seven imaging techniques.ConclusionThis network meta-analysis provides supporting evidence to the idea that DWI has a higher diagnostic value regarding ischemic stroke among MRI methods, and CTP has a poor diagnostic value among CT methods, which provide therapeutic considerations for Ischemic stroke intervention.

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Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis.

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Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis.

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