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

Abstract

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.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

“…This approach overcame the problem related to the accurate delineation of tumoral volume of interest (VOI). 52 The same authors provided, in addition, a radiomics CT nomogram for discriminating AML from RCC, 53 built using selected features reaching an AUC, for this nomogram, of 0.968. 54 More recently, Han et al performed a retrospective research in 58 patients with AML and 140 with RCC, pathologically confirmed, to evaluate the prognostic value of CT radiomics in distinguishing AML from RCC.…”

“…Razik et al ., 57 performed an MRI analysis to distinguish AML, RCC, and ONC, reporting in an MRI-based radiomics, an AUC > 0.8, with best performing parameter based on the mean of positive pixels (MPP) on DWI (AUC of 0.891). Jian et al 53 instead, evaluated the combined use of MRI radiomics plus urinary creatinine for this purpose in a preliminary study, reporting the best AUC for the T2WI model (0.874), which increased up to 0.919 when combined with urinary creatinine, proposing the addition of other variables to radiomics approach to improve the diagnostic capabilities. Matsumoto et al 58 demonstrated instead that the ADC map was enough in differentiating AML from RCC via a radiomics MRI-based approach, reporting good AUC (0.87) in the validation group.…”

“… 128 , 129 In the renal lesions, radiomics studies were used to reduce overfitting with good results. 49 – 53 , 55 , 70 , 111 …”

Research on texture images and radiomics in urology: a review of urological MR imaging applications

“…In this context, radiomics models have been developed to best discriminate these lesions. Indeed, in 2022, Jian et al reported for the 1st time a MRI-based radiomics study for differentiation of mf-AML ( n = 19) form RCC ( n = 50) [6 ▪▪ ]. The authors analyzed 396 radiomic features for each MRI sequence, in manually selected region of interest (ROI), in T2WI and intravoxel incoherent motion (IVIM) DWI as well as clinical features.…”

Editorial: New Frontiers for Artificial Intelligence in Surgical Decision Making and its Organizational Impacts

“…The study by Jian and colleagues ( 7 ) strived to evaluate the diagnostic performance of magnetic resonance imaging-based radiomics in the differentiation of fat-poor angiomyolipomas from other renal neoplasms. A study conducted on 69 patients allowed the research team to demonstrate how the clinical-radiomics model could represent a valuable noninvasive diagnostic tool to distinguish fat-poor angiomyolipomas from other renal neoplasms.…”