Diagnostic value of texture analysis of apparent diffusion coefficient maps for differentiating fat-poor angiomyolipoma from non-clear-cell renal cell carcinoma

Arita, Yuki; Yoshida, Soichiro; Kwee, Thomas C.; Akita, Hirotaka; Okuda, S.; Iwaita, Yuki; Mukai, Kiyoko; Matsumoto, S; Ueda, Ryo; Ishii, Ryota; Mizuno, Ryuichi; Fujii, Yasuhisa; Oya, Mototsugu; Jinzaki, Masahiro

doi:10.1016/j.ejrad.2021.109895

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…A total of 3948 radiomic features were extracted from the T2WI, DWI and ADC sequences, from which 13 most signi cant features were ultimately identi ed to construct the radiomics model. In the test set, the model achieved an AUC value of 0.778, representing a substantial improvement compared to previous studies [Matsumoto et al 2022;Arita et al 2021] that had focused on extracting only a few dozen features. This expanded feature analysis permits a more thorough exploration of tumor information, thereby enhancing the predictive and classi catory capabilities of radiomics in the context of renal tumors.…”

Section: Discussionmentioning

confidence: 73%

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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Section: Discussionmentioning

confidence: 73%

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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“…The researchers placed 2D ROIs around the tumors and input them into an AlexNet CNN model, achieving 91% accuracy and an AUC of 0.9. Arita et al [93] analyzed the texture of 3D ADC maps derived from DW-MRI to distinguish between benign AML and malignant nccRCC. They encompassed a training dataset of 67 tumors (AML = 46 and nccRCC = 21) and a validation dataset of 39 tumors (AML = 24 and nccRCC = 15).…”

Section: Magnetic Resonance Imaging (Mri) Studiesmentioning

confidence: 99%

“…In terms of differentiating malignant from benign renal tumors, CT studies have demonstrated a slightly higher diagnostic accuracy [52,54,55,63,68,83] when compared with the results obtained by MRI studies [29,31,92,93]. This can be partially attributed to the superior resolution provided by CT in comparison with MRI.…”

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confidence: 99%

“…Suggested Diagnostic Radiomic Techniques: Generally, handcrafted radiomic techniques were more commonly investigated in both CT [14,27,28,34,35,37,54,62,68,74,82,86] and MRI [22,23,29,30,32,93,94] studies, as opposed to deep learning radiomic techniques, which were less frequently utilized in CT [66,81,83,88,95] and MRI [92] studies. Handcrafted techniques have proven efficient, as evidenced by high diagnostic accuracy, sensitivity, and specificity, as well as being well-understood (i.e., explainable AI), making them desirable and dependable.…”

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confidence: 99%

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Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Shehata

Abouelkheir

Gayhart

et al. 2023

Cancers

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Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients’ clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients’ clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

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“…The data extracted from radiomicroscopy, when combined with other clinical data and correlated with outcome, can create accurate, robust, and evidence-based clinical decision support systems (CDSS) [ 62 , 119 ]. The rationale for radiomics is that quantitative variables based on individual voxels are more sensitive to various clinical endpoints than the qualitative radiologic, histopathologic, and clinical data routinely used in clinical practice [ 120 , 121 , 122 , 123 , 124 ]. An extension of radiomics is radiogenomics, which aims to correlate imaging data with some known genetic predictors of response to therapy and metastatic spread, with potential prognostic utility [ 92 , 125 ].…”

Section: Radiomicsmentioning

confidence: 99%

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

Bruno

Granata

Bellisari

et al. 2022

Cancers

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In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice.

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Diagnostic value of texture analysis of apparent diffusion coefficient maps for differentiating fat-poor angiomyolipoma from non-clear-cell renal cell carcinoma

Cited by 9 publications

References 20 publications

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

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

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine

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