Differentiating renal epithelioid angiomyolipoma from clear cell carcinoma: using a radiomics model combined with CT imaging characteristics

Kim, Taek Min; Ahn, Hyo Hyun; Lee, Hyo‐Jeong; Kim, Min Gwan; Cho, Jeong Yeon; Hwang, Sung Il; Kim, Sang-Youn

doi:10.1007/s00261-022-03571-9

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…The corticomedullary phase and nephrographic phase achieved an adequate performance (AUC = 0.767 and 0.783, respectively). 55 Similarly, Kim et al 56 assessed the predictive role of CT radiomics in 28 AML and 56 RCC, reporting an AUC of 0.89, close to those of experienced radiologists (AUC = 0.78; Table 2 ).…”

Section: Resultsmentioning

confidence: 79%

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

Russo

Crocetto

Barone

et al. 2023

Therapeutic Advances in Urology

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Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

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Section: Resultsmentioning

confidence: 79%

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

Russo

Crocetto

Barone

et al. 2023

Therapeutic Advances in Urology

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“…In recent years, the development and advancement of radiomics have signi cantly enhanced the ability to predict and classify renal tumors. CT and MRI imaging have been extensively utilized in radiomics and machine learning research to differentiate renal masses [Kim et al 2022;Sun et al 2022]. Given that T2, DWI, and ADC maps are widely adopted sequences in MRI examinations in various clinical settings, these three sequences were selected for feature extraction in the current study.…”

Section: Discussionmentioning

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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“…Non-invasive biomarkers are needed for a distinction between different malignant lesions [ 106 ]. Promising results have been provided by radiomics in the differentiation between clear cell renal cell carcinomas and non-clear cell renal cell carcinomas (papillary and chromophobe renal cell carcinomas), and between epithelioid angiomyolipomas [ 107 ] and renal oncocytoma; in this last case this approach avoids the surgical resection of the benign lesion due to a misdiagnosis [ 108 ]. In particular, CT radiomics has good performance in classifying pathological renal tumors [ 109 ].…”

Section: Artificial Intelligencementioning

confidence: 99%

Computed Tomography Urography: State of the Art and Beyond

et al. 2023

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Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients’ outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.

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Differentiating renal epithelioid angiomyolipoma from clear cell carcinoma: using a radiomics model combined with CT imaging characteristics

Cited by 8 publications

References 36 publications

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

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

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

Computed Tomography Urography: State of the Art and Beyond

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