Analysis of dual energy spectral CT and pathological grading of clear cell renal cell carcinoma (ccRCC)

Wei, Jinyan; Zhao, Jun; Zhang, Xueling; Wang, Dan; Zhang, Wenjuan; Wang, Zhiping; Zhang, Junlin

doi:10.1371/journal.pone.0195699

Cited by 38 publications

(26 citation statements)

References 31 publications

(32 reference statements)

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“…Wei et al showed that the tumor shape differed significantly between low‐grade and high‐grade ccRCC 28 . Two MRI characteristics (shape and margin, necrosis) extracted in this study were consistent with previous studies, 8,27,28 reflecting the features of invasive growth and uneven distribution of blood supply in high‐grade ccRCC.…”

Section: Discussionsupporting

confidence: 88%

Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

Liu

Dong

et al. 2020

Magnetic Resonance Imaging

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Background Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). Purpose To develop and validate an MRI‐based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC. Study Type Retrospective. Population In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned. Field Strength/Sequence Pretreatment 3.0T renal MRI. Imaging sequences were fat‐suppressed T2WI, contrast‐enhanced T1WI, and diffusion weighted imaging. Assessment Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high‐grade ccRCC. Statistical Tests The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics. Results The radiomic signature showed good performance in discriminating high‐grade (grades 3 and 4) from low‐grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high‐grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05). Data Conclusion Multiparametric MRI‐based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions. Level of Evidence 3 Technical Efficacy Stage 2

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Section: Discussionsupporting

confidence: 88%

Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

Liu

Dong

et al. 2020

Magnetic Resonance Imaging

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“…In tumor tissue, when the differentiation degree of tumor is low, RCC grows fast, and the neovascularization structure is incomplete and unevenly distributed. These lesions show abnormal hemodynamics, resulting in uneven distribution of blood supply [22]. Therefore, in cortical and medullary phase, especially in cortical phase, NIC and NWC correlated with microvascular grading.…”

Section: Discussionmentioning

confidence: 96%

Effect of Spectral CT on Tumor Microvascular Angiogenesis in Renal Cell Carcinoma

Zhang

Qiu

et al. 2021

Preprint

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Background: To examine the value of spectral CT quantitative parameters in renal cell carcinoma (RCC) microvascular angiogenesis.Methods: The authors evaluated 32 patients with pathologically confirmed RCC who underwent triple-phase contrast-enhanced CT with spectral CT imaging mode from January 2017 to December 2019. Quantitative parameters including the parameters derived from iodine concentration (IC) and water concentration (WC) of 120 keV monochromatic images. The microvascular density (MVD), microvascular area (MVA) and etc. al. of specimens were evaluated. The correlation between IC and WC (including average values and random values) with microvascular parameters were analyzed using Pearson or Spearman rank correlation coefficients.Results: The MVD of all tumors was 26.00 (15.00-43.75) vessels per field at ×400 magnification. The MVD of RCC correlated positively with the mean IC, mean WC, mean NWC, mean NIC, random IC, random NIC in renal cortical phase, WCD1, WCD2, NWCD2 and ICD1 (Spearman rank correlation coefficients, r range, 0.362–0.533; all p < 0.05). The MVA of all tumors was (16.16±8.98) % per field at ×400 magnification. The MVA of RCC correlated positively with the mean IC, mean WC, mean NWC, mean NIC, random IC, random NIC in renal cortical, mean WC and mean NWC in renal parenchymal phase, WCD1, WCD2, WCD3, NWCD2, and NWCD3 (Pearson or Spearman rank correlation coefficients, r range, 0.357–0.576; all p < 0.05). Microvascular grading correlated positively with the mean NWC, mean NIC and random NIC in renal cortical phase, mean NWC in renal parenchymal phase, NWCD2, WCD3, NWCD3, NICD2 and NICD3 (Spearman rank correlation coefficients, r range, 0.367–0.520; all p < 0.05). As for tumor diameter (55.19±19.15), μm, only NWCD3 was associated with it (Spearman rank correlation coefficients, r = 0.388; p < 0.05). Conclusions: IC and WC indexes derived from spectral CT, especially the renal cortical phase, difference between renal cortical phase and parenchymal phase were useful indicators for evaluating tumor angiogenesis. Moreover, the difference of the derived parameters of WC in different phases has stronger correlation with tumor angiogenesis than that of IC.

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“…Because the energy spectrum curves of different substances show differences, differences of the energy spectrum curves in different tissue sources, characteristics and components can be estimated, which enables the differential diagnosis. It can be inferred that the energy spectrum curve can be used to differentiate the origin of tumors, benign and malignant tumors, and tumors with different differentiation degrees, moreover, the energy spectrum curve can be quantitatively analyzed to provide new diagnostic ideas [14][15].…”

Section: Discussionmentioning

confidence: 99%

Clinical Value of Energy Spectrum Curves of Dual-energy CT in Evaluating Pathological Grading of Gastric Adenocarcinoma

Chen

et al. 2019

Preprint

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Backgroud: Accurate diagnosis of cancer staging and pathological differentiation are critical for the formulation of individualized treatment and prognosis of gastric cancer. It is vital to explore noninvasive preoperative imaging techniques to evaluate the pathological differentiation degree of gastric cancer tissues, and provide better diagnostic basis and decision-making reference for treatment. The purpose of this study was to explore the clinical value of energy spectrum curves of dual-source dual-energy CT in the quantitative evaluation of different pathological grades of gastric adenocarcinoma. Methods: A total of 62 patients with 1 well, 25 moderately and 36 poorly differentiated gastric adenocarcinomas pathologically confirmed by surgery were collected, and they underwent dual-source dual-energy CT plain scanning and enhanced scanning before operation. Dual-Energy software was used to measure the slope of the energy spectrum curves (λ) in arterial and venous phases after image reconstruction. Patients were divided into two groups according to the pathological results, including well and moderately differentiated gastric adenocarcinoma group and poorly differentiated gastric adenocarcinoma group. Data of each group were analyzed by independent sample t-test. The receiver operating characteristic curve was plotted to evaluate the diagnostic efficiency of the corresponding parameters. Results: There were significant differences in λ values of 40-50keV, 40-60keV, 40-80keV, 40-90keV, 40-100keV, 40-120keV, 40-130keV, 40-140keV and 40-150keV energy ranges in venous phase between the well and moderately differentiated group and poorly differentiated group (P<0.05), but no significant differences in λ values of different energy ranges in arterial phase between the two groups (P>0.05). And the area under curve in 40-120keV energy range was the largest in venous phase. K40-120keV =2.69 was selected as the diagnostic threshold with the maximum Youden index, the sensitivity and specificity were 61.1% and 76%, respectively. Conclusion: The energy spectrum curve of dual-energy CT had certain diagnostic value in the quantitative evaluation of pathological grading of gastric adenocarcinoma.

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Analysis of dual energy spectral CT and pathological grading of clear cell renal cell carcinoma (ccRCC)

Cited by 38 publications

References 31 publications

Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

Effect of Spectral CT on Tumor Microvascular Angiogenesis in Renal Cell Carcinoma

Clinical Value of Energy Spectrum Curves of Dual-energy CT in Evaluating Pathological Grading of Gastric Adenocarcinoma

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