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

Amide proton transfer-weighted MRI for renal tumors: Comparison with diffusion-weighted imaging

OBJECTIVE

To investigate the potential of amide proton transfer-weighted (APTw) MRI in identifying benign and malignant renal tumors and to evaluate whether APTw MRI can add diagnostic value to diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Participants with renal tumor underwent preoperative multiparametric MRI, including APTw MRI and DWI. The APTw and apparent diffusion coefficient (ADC) of malignant tumors and benign tumors were calculated independently by two radiologists and compared. The value of the mean APTw and the mean ADC for differentiating malignant and benign tumors was evaluated by receiver operating characteristic analysis.

RESULTS

In total, 65 participants (mean age, 59 years ±14; 41 men) were evaluated: 54 with malignant and 11 with benign renal tumors. Malignant renal tumors showed higher mean APTw values [2.03% (1.63) vs 1.00% (1.60); P < 0.01] and lower mean ADC values (1.22 × 10-3 mm2/s ± 0.37 vs 1.51 × 10-3 mm2/s ± 0.37; P < 0.05) than benign renal tumors. The area under the receiver operating characteristic curve (AUC) of APTw, ADC and the combination of them for the identification of benign and malignant renal tumors was 0.78(95% CI: 0.66, 0.87; P < 0.001),0.70(95% CI: 0.54, 0.86; P < 0.05) and 0.79 (95% CI: 0.67, 0.88; P < 0.001). The optimal cutoff value for mean APTw was 2.14% (sensitivity, 74%; specificity, 73%). There was no difference between these three parameters for differentiating malignant from benign renal tumors (P > 0.05).

CONCLUSION

The APTw MRI has the potential use as an imaging biomarker for renal malignant and benign tumors.

Differentiating renal cell carcinoma and oncocytoma with volumetric MRI histogram analysis

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 5th, 10th, 25th, 50th, 75th, 90th, and 95th 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 5th, 10th, 25th, 50th, 75th, 90th, and 95th 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 5th ranking of ADC measurements (p<0.001), in addition to mean of ADC (p=0.050), and the 10th (p=0.002) and 25th (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.

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

Objectives

Standard 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.

Methods

A 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.

Results

Clinical 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.

Conclusion

The 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.