The additional utility of apparent diffusion coefficient values of clear-cell renal cell carcinoma for predicting metastasis during clinical staging

Yoshida, Rika; Yoshizako, Takeshi; Araki, Hisatoshi; Mori, Hiroshi; Tamaki, Yukihisa; Ishikawa, Noriyoshi; Kitagaki, Hajime

doi:10.1177/2058460116687174

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…Our result agreed with the previous study, which found a statistically significant difference in the ADC [25].…”

Section: Discussionsupporting

confidence: 94%

“…Thus, the minimum ADC was observed to be an independent factor for recurrence of RCC after nephrectomy [8]. Yoshida et al exhibited a measurably noteworthy distinction in the ADC between T1a RCC (estimate ≤ 4 cm) with or without lymph nodes or metastasis [9]. Therefore, we hypothesize that the ADC value may be one of the prognostic factors for tumor aggressiveness in patients with RCC.…”

Section: Introductionmentioning

confidence: 69%

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Assessment of apparent diffusion coefficient value as prognostic factor for renal cell carcinoma aggressiveness

Ahmad

Sabr

Roshy

2019

Egypt J Radiol Nucl Med

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Background: Assurance of prognostic elements is important for the management of renal cell carcinoma (RCC). Our goal was to check the relation between apparent diffusion coefficient (ADC) values and parameters predicting prognosis of RCC. Fifty pathologically confirmed RCC underwent diffusion-weighted (DW) MRI. ADC values were calculated using b factor (800 s/mm 2). The correlation between ADC values and tumor size, cystic/necrotic feature, growth pattern, unenhanced T1, histological grade, clinical stage, and distant metastasis were analyzed. Results: The optimal ADC threshold for prognosis of RCC appeared to be 1.4 × 10 −3 mm 2 /s. There was a significant inverse correlation between ADC values and growth pattern (R = − 0, P = 0.05), unenhanced T1(R = − 0.41, P = 0.01), cystic/necrotic feature (R = − 0.4, P = 0.01), histological grade (R = − 0.37, P = 0.02), clinical stage (r = − 0.4, P = 0.01), and distant metastasis (R = − 0.33, P = 0.04), and significant linear correlation with tumor size (R = 0.39, P < 0.02). Conclusion: The performance of ADC value as a newly proposed prognostic parameter follows with the degree of tumor differentiation and that may recognize extremely aggressive RCC. RCC with low ADC values should be inspected extensively for the risk of high pathological grade, high clinical stage, and distant metastasis.

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“…Our result agreed with the previous study, which found a statistically significant difference in the ADC [25].…”

Section: Discussionsupporting

confidence: 94%

Section: Introductionmentioning

confidence: 69%

Assessment of apparent diffusion coefficient value as prognostic factor for renal cell carcinoma aggressiveness

Ahmad

Sabr

Roshy

2019

Egypt J Radiol Nucl Med

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“…MRI-derived ADC values are useful in characterizing tumor activity [ 54 ]. Some studies [ 55 , 56 ] that evaluated the utility of ADC to differentiate low- from high-grade ccRCC reported that MRI has a favorable predictive accuracy in detecting high-grade ccRCC (AUC = 0.80).…”

Section: Discussionmentioning

confidence: 99%

Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

Moldovanu

Boca

Lebovici

et al. 2020

JPM

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Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92–1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

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“…For this purpose, two approaches are attractive for researchers at present. One is the Apparent diffusion coefficient (ADC) value in MRI imaging (14), and the other is CT-based semiquantitative and quantitative techniques (15,16).…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

et al. 2020

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To identify optimal classification methods for computed tomography (CT) radiomicsbased preoperative prediction of clear cells renal cell carcinoma (ccRCC) grade.Methods and material: Seventy-one ccRCC patients (31 low-grade and 40 high-grade) were included in the study. All tumors were segmented manually on CT images, and three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) were applied on tumor volumes. In total, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association of each feature with the histological condition. In the case of multivariate analysis, the following was implemented: three feature selection including the least absolute shrinkage and selection operator (LASSO), student's t-test and minimum Redundancy Maximum Relevance (mRMR) algorithms. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate the high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under receiver operating characteristic curve (AUC).Results: Univariate analysis demonstrated that among different image sets, 128 bin discretized images have statistically significant different (q-value < 0.05) texture parameters with a mean of AUC 0.74±3 (q-value < 0.05). The three ML-based classifier shows proficient discrimination of the high from low-grade ccRCC. The AUC was 0.78 in logistic regression, 0.62 in random forest, and 0.83 in SVM model, respectively. Conclusion:Radiomics features can be a useful and promising non-invasive method for preoperative evaluation of ccRCC Fuhrman grades.

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The additional utility of apparent diffusion coefficient values of clear-cell renal cell carcinoma for predicting metastasis during clinical staging

Cited by 6 publications

References 18 publications

Assessment of apparent diffusion coefficient value as prognostic factor for renal cell carcinoma aggressiveness

Assessment of apparent diffusion coefficient value as prognostic factor for renal cell carcinoma aggressiveness

Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

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