Magnetic resonance imaging features of minimal-fat angiomyolipoma and causes of preoperative misdiagnosis

Li, Xiaolong; Shi, Li-Xin; Du, Qi-Cong; Wang, Wei; Shao, Lei; Wang, Yingwei

doi:10.12998/wjcc.v8.i12.2502

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…The mf-AML is often misdiagnosed as RCC prior to surgery (7). Differentiating these two entities is especially crucial for treatment planning and prognosis evaluation but challenging (28).…”

Section: Discussionmentioning

confidence: 99%

“…In total, 396 radiomics features were extracted from each MRI sequence, including four groups: 1) Histogram (n=42); 2) Gray-level co-occurrence matrix (GLCM) (n=108); 3) Gray-level run length matrix (GLRLM) (n=226); and 4) Shape (n=20). GLCM and GLRLM in four directions (0°, 45°, 90°, 135°) and three displacements (1,4,7) were calculated to describe patterns or the spatial distribution of voxel intensities. The description of the extracted radiomics features is shown in Supplementary Material.…”

Section: Ivim-dwi Image Postprocessingmentioning

confidence: 99%

“…Research on the differential diagnosis of mf-AML is difficult to accumulate cases, because its frequency is significantly lower than that of other common renal tumors. Prior studies have shown several imaging findings (mainly from CT) may be helpful for diagnosis of mf-AML, such as an oval shape without capsule (7), homogeneous hypointensity on T2weighted MR images (8), higher attenuation than renal parenchyma (9), tumor-to-cortex enhancement ratio (10), and prolonged enhancement pattern (9). The diagnostic accuracy of standard MRI with opposed-phase and in-phase gradient-echo (GRE) sequences for the differentiation of mf-AML and RCC was poor (11).…”

Section: Introductionmentioning

confidence: 99%

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MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study

et al. 2022

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ObjectivesStandard magnetic resonance imaging (MRI) techniques are different to distinguish minimal fat angiomyolipoma (mf-AML) with minimal fat from renal cell carcinoma (RCC). Here we aimed to evaluate the diagnostic performance of MRI-based radiomics in the differentiation of fat-poor AMLs from other renal neoplasms.MethodsA total of 69 patients with solid renal tumors without macroscopic fat and with a pathologic diagnosis of RCC (n=50) or mf-AML (n=19) who underwent conventional MRI and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were included. Clinical data including age, sex, tumor location, urine creatinine, and urea nitrogen were collected from medical records. The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were measured from renal tumors. We used the ITK-SNAP software to manually delineate the regions of interest on T2-weighted imaging (T2WI) and IVIM-DWI from the largest cross-sectional area of the tumor. We extracted 396 radiomics features by the Analysis Kit software for each MR sequence. The hand-crafted features were selected by using the Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO). Diagnostic models were built by logistic regression analysis. Receiver operating characteristic curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for the differentiation of mf-AML and RCC. Decision curve analysis (DCA) was used to evaluate the clinical utility of the models.ResultsClinical model based on urine creatinine achieved an AUC of 0.802 (95%CI: 0.761-0.843). IVIM-based model based on f value achieved an AUC of 0.692 (95%CI: 0.627-0.757). T2WI-radiomics model achieved an AUC of 0.883 (95%CI: 0.852-0.914). IVIM-radiomics model achieved an AUC of 0.874 (95%CI: 0.841-0.907). Combined radiomics model achieved an AUC of 0.919 (95%CI: 0.894-0.944). Clinical-radiomics model yielded the best performance, with an AUC of 0.931 (95%CI: 0.907-0.955). The calibration curve and DCA confirmed that the clinical-radiomics model had a good consistency and clinical usefulness.ConclusionThe clinical-radiomics model may be served as a noninvasive diagnostic tool to differentiate mf-AML with RCC, which might facilitate the clinical decision-making process.

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Section: Discussionmentioning

confidence: 99%

Section: Ivim-dwi Image Postprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study

et al. 2022

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“…Diffusion-weighted imaging (DWI) has been newly incorporated into the magnetic resonance imaging (MRI) procedures for the differential diagnosis of renal masses. This addition allows for the non-invasive characterization of tumors [ 7 – 9 ].…”

Section: Highlight Key Pointsmentioning

confidence: 99%

Differentiating renal cell carcinoma and oncocytoma with volumetric MRI histogram analysis

Akinci

2023

North Clin Istanbul

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OBJECTIVE In this study, the utility of histogram parameters derived from diffusion-weighted imaging for differentiate renal cell carcinoma (RCC) from oncocytoma was investigated. METHODS This research tracked 126 individuals who were diagnosed with RCC and oncocytoma through histopathological analysis, using magnetic resonance imaging (MRI) assessments from 2015 to 2023. We observed various attributes of these patients, including demographic details, surgical records, pre-surgery MRI results, MRI apparent diffusion coefficient (ADC) histogram analysis, and post-surgery histopathological outcomes. Calculations of ADC measurements such as mean, minimum, and maximum in conjunction with the 5 th , 10 th , 25 th , 50 th , 75 th , 90 th , and 95 th quantile points were made. In addition, we also noted the skewness, kurtosis, and variance of these data points. RESULTS The focus group for this investigation consisted of 75 male and 51 female patients. Out of these, 82 were diagnosed with RCC and 44 with oncocytoma. All ADC parameters including ADCmin, ADCmedian, ADCmean, and ADCmax, including the 5 th , 10 th , 25 th , 50 th , 75 th , 90 th , and 95 th quantile divisions among the oncocytoma cohort were observed to be higher than the corresponding ones in the RCC group. A statistically meaningful difference was discovered between the minimum ADC value along with the 5 th ranking of ADC measurements (p<0.001), in addition to mean of ADC (p=0.050), and the 10 th (p=0.002) and 25 th (p=0.015) quantiles of ADC data. When considering the region below the curve (AUC) in ROC analysis, the value of ADCmin was recorded as 0.739, with a sensitivity of 75.0%, and specificity of 68.2%. CONCLUSION To distinguish oncocytoma from RCC, it may be useful to conduct a whole-tumor histogram and textural analysis of ADC values.

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“…To differentiate fp-AML from ccRCC, radiological features including high attenuation at unenhanced CT, absence of pseudocapsule, uniform enhancement pattern, low signal on T2-weighted imaging, and uniform high signal on diffusion-weighted imaging (DWI) images have been suggested [Li et al 2020;Park et al 2019]. However, the radiological features are subjective visual assessments, have poor inter-observer agreement, and limited reproducibility.…”

Section: Introductionmentioning

confidence: 99%

A MRI-based radiomics nomogram for differentiation of fat-poor renal angiomyolipoma from nodular clear cell renal cell carcinoma

Chai,

Yang,

et al. 2024

Preprint

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Objectives This study aims to assess the effectiveness of a radiomics nomogram based on multiparametric MRI in distinguishing between fat-poor renal angiomyolipoma (fp-AML) and renal clear cell carcinoma (ccRCC) in small renal tumors (≤ 4 cm). Methods A retrospective analysis was conducted from July 2017 to July 2023, including 65 ccRCC and 23 fp-AML patients with confirmed pathology. T2WI, DWI and ADC sequences were used to extract radiomics features. A t-test and LASSO were used to select features. A radiomic signature was created after calculating a radiomic score (Rad-score). To create a clinical model, clinical characteristics and subject MRI features were evaluated. Then, the Rad scores and clinical characteristics were integrated to develop a radiomics nomogram. The nomogram's performance was assessed regarding calibration, discrimination, and clinical utility. Results In the study, 88 renal masses from 86 patients were included. Each patient had their 3948 radiomics features retrieved, and 13 of those features were used to create the radiomics signature. The radiomics nomogram (AUC, 0.833; 95% CI, 0.675–0.992) showed superior discriminating capacity in the validation set compared to the clinical model (AUC, 0.774; 95% CI, 0.554–0.995) and radiomics signature (AUC, 0.778; 95% CI, 0.571–0.985) alone. Decision curve analysis indicated that the nomogram outperformed the clinical model in terms of clinical utility. Conclusions The multiparametric MRI-based radiomics nomograms effectively use Rad scores and clinical factors as non-invasive preoperative predictors, successfully differentiating fp-AML from ccRCC.

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Magnetic resonance imaging features of minimal-fat angiomyolipoma and causes of preoperative misdiagnosis

Cited by 6 publications

References 24 publications

MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study

MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study

Differentiating renal cell carcinoma and oncocytoma with volumetric MRI histogram analysis

A MRI-based radiomics nomogram for differentiation of fat-poor renal angiomyolipoma from nodular clear cell renal cell carcinoma

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