Differential diagnosis of renal oncocytoma and chromophobe renal cell carcinoma using CT features: a central scar-matched retrospective study

2021 ◽  
pp. 028418512098810
Author(s):  
Xiaoli Li ◽  
Pei Nie ◽  
Jing Zhang ◽  
Feng Hou ◽  
Qianli Ma ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Logistic Regression ◽  
Tumor Size ◽  
Logistic Regression Model ◽  
Density Ratio ◽  
Ct Imaging ◽  
Renal Oncocytoma ◽  
Imaging Features ◽  
Central Scar ◽  
Enhanced Ct

Background Renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) have a common cellular origin and different clinical management and prognosis. Purpose To explore the utility of computed tomography (CT) in the differentiation of RO and chRCC. Material and Methods Twenty-five patients with RO and 73 patients with chRCC presenting with the central scar were included retrospectively. Two experienced radiologists independently reviewed the CT imaging features, including location, tumor size, relative density ratio, segmental enhancement inversion (SEI), necrosis, and perirenal fascia thickening, among others. Interclass correlation coefficient (ICC, for continuous variables) or Kappa coefficient test (for categorical variables) was used to determine intra-observer and inter-observer bias between the two radiologists. Results The inter- and intra-reader reproducibility of the other CT imaging parameters were nearly perfect (>0.81) except for the measurements of fat (0.662). RO differed from chRCC in the cortical or medullary side ( P = 0.005), relative density ratio ( P = 0.020), SEI ( P < 0.001), and necrosis ( P = 0.045). The logistic regression model showed that location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were highly predictive of RO. The combined indicators from logistic regression model were used for ROC analysis. The area under the ROC curve was 0.923 ( P < 0.001). The sensitivity and specificity of the four factors combined for diagnosing RO were 88% and 86.3%, respectively. The correlation coefficient between necrosis and tumor size in all tumors including both of RO and chRCC was 0.584, indicating a positive correlation ( P < 0.001). Conclusion The CT imaging features of location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were valuable indicators in distinguishing RO from chRCC.

Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma

2016 ◽  
Vol 58 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Saelin Oh ◽  
Deuk Jae Sung ◽  
Kyung Sook Yang ◽  
Ki Choon Sim ◽  
Na Yeon Han ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Roc Curve ◽  
Tumor Size ◽  
Renal Cell ◽  
Clear Cell ◽  
Ct Imaging ◽  
Imaging Features ◽  
Cell Renal Cell Carcinoma ◽  
Fuhrman Grade

Background Identification of clinical features to determine the aggressive potential of tumors is highly warranted to stratify patients for adequate treatment. Computed tomography (CT) imaging features of clear cell renal cell carcinoma (ccRCC) may contribute to personalized risk assessment. Purpose To assess the correlation between CT imaging features and Fuhrman grade of ccRCC, and to identify the predictors of high Fuhrman grade in conjunction with tumor size. Material and Methods CT scans of 169 patients with 173 pathologically proven ccRCCs were retrospectively reviewed in consensus by two radiologists for the presence of intratumoral necrosis and intratumoral cyst and tumor size. Histologic grade was classified as either low (Fuhrman grade I or II) or high (Fuhrman grade III or IV). Statistical significance was evaluated by using univariate, multivariate regression, receiver operating characteristic (ROC) curve, and Spearman correlation analyses. Results On CT, 20 of the 173 tumors had intratumoral cysts, 60 had intratumoral necrosis, and 93 showed entirely solid tumors. The odds of high grade were higher with intratumoral necrosis and entirely solid tumor than with intratumoral cyst ( P < 0.03). Intratumoral necrosis showed a significantly high odds ratio of 25.73 for high Fuhrman grade. The ROC curve showed a threshold tumor size of 36 mm to predict high Fuhrman grade for overall tumors (area under the ROC curve, 0.70). In ccRCCs with intratumoral necrosis or cyst, tumor size did not significantly correlate with Fuhrman grade. Conclusion Intratumoral necrosis on CT was a strong and independent predictor of biologically aggressive ccRCCs, irrespective of tumor size.

scholarly journals Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT Imaging

2021 ◽  
Vol 10 ◽  
Author(s):  
Mengshi Dong ◽  
Gang Hou ◽  
Shu Li ◽  
Nan Li ◽  
Lina Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Logistic Regression Model ◽  
Ct Imaging ◽  
Mediastinal Lymph Nodes ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Test Sets ◽  
Stratified Analysis

PurposeTo establish and validate a radiomics model to estimate the malignancy of mediastinal lymph nodes (LNs) based on contrast-enhanced CT imaging.MethodIn total, 201 pathologically confirmed mediastinal LNs from 129 patients were enrolled and assigned to training and test sets. Radiomics features were extracted from the region of interest (ROI) delineated on venous-phase CT imaging of LN. Feature selection was performed with least absolute shrinkage and selection operator (LASSO) binary logistic regression. Multivariate logistic regression was performed with the backward stepwise elimination. A model was fitted to associate mediastinal LN malignancy with selected features. The performance of the model was assessed and compared to that of five other machine learning algorithms (support vector machine, naive Bayes, random forest, decision tree, K-nearest neighbor) using receiver operating characteristic (ROC) curves. Calibration curves and Hosmer-Lemeshow tests were used to assess the calibration degree. Decision curve analysis (DCA) was used to assess the clinical usefulness of the logistic regression model in both the training and test sets. Stratified analysis was performed for different scanners and slice thicknesses.ResultAmong the six machine learning methods, the logistic regression model with the eight strongest features showed a significant association with mediastinal LN status and the satisfactory diagnostic performance for distinguishing malignant LNs from benign LNs. The accuracy, sensitivity, specificity and area under the ROC curve (AUC) were 0.850/0.803, 0.821/0.806, 0.893/0.800, and 0.922/0.850 in the training/test sets, respectively. The Hosmer-Lemeshow test showed that the P value was &gt; 0.05, indicating good calibration, and the calibration curves showed good agreement between the classifications and actual observations. DCA showed that the model would obtain more benefit when the threshold probability was between 30% and 90% in the test set. Stratified analysis showed that the performance was not affected by different scanners or slice thicknesses. There was no significant difference (DeLong test, P &gt; 0.05) between any two subgroups, which showed the generalization of the radiomics score across different factors.ConclusionThe model we built could help assist the preoperative estimation of mediastinal LN malignancy based on contrast-enhanced CT imaging, with stability for different scanners and slice thicknesses.

scholarly journals Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Curve ◽  
Multinomial Logistic Regression ◽  
Univariate Analysis ◽  
Ct Imaging ◽  
Squamous Cell Carcinomas ◽  
Curve Analysis ◽  
Imaging Features ◽  
Roc Curve Analysis ◽  
Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

Utilization of renal mass biopsy in patients with localized renal cell carcinoma: A population-based study utilizing the National Cancer Database.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 662-662
Author(s):  
Devin Patel ◽  
Fady Ghali ◽  
Sunil Patel ◽  
Nathan Miller ◽  
Aaron Bradshaw ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Logistic Regression ◽  
Cell Carcinoma ◽  
Tumor Size ◽  
Renal Cell ◽  
Renal Mass ◽  
Temporal Trends ◽  
National Cancer Database ◽  
Multivariable Logistic Regression ◽  
Renal Mass Biopsy

662 Background: Recent guidelines suggest an increasing role for renal mass biopsy (RMB) in the management of renal cell carcinoma (RCC) prior to ablative therapy and in patients in whom active surveillance (AS) is being considered. Methods: We queried the National Cancer Database for cases of localized (cT1-cT3N0M0) RCC between 2004-2015. Unadjusted temporal trends in receipt of RMB were characterized over the study period based on type of treatment [partial nephrectomy (PN), radical nephrectomy (RN), ablation, and surveillance], tumor size, age and Charlson Comorbidity Index and compared using analysis of variance. Multivariable logistic regression was used to test for the association between patient, tumor and treatment variables and use of RMB. Results: 338,252 patients were analyzed, with 11.9% (40,276) undergoing RMB. Use of RMB increased from 1,586 (7.6%) in 2004 to 5,629 (16.2%) in 2015 (p<0.001). On treatment strategy, use of RMB increased the greatest in association with ablation from 27% to 63% across the study period (p<0.001). On tumor size, use of RMB increased the greatest for tumors 2-4 cm from 9% to 20% (p<0.001). Conversely, utilization of RMB increased proportionally across different age (p=0.17) and comorbidity (p=0.18) groups over time. Multivariable logistic regression revealed that year of diagnosis (OR 1.06; p<0.001), black race (OR 1.04; p=0.02), higher education (OR 1.09; p<0.001) and insured status (OR 1.23; p<0.001) were associated with increased RMB. However, increasing age (p=0.23) and comorbidity (p=0.35) were not. Compared to tumors <2 cm in size, tumors 2-4 cm (OR 1.36; p<0.001), 4-7 cm (OR 1.18; p<0.001) and >7 cm (OR 1.05; p=0.03) were associated with higher RMB. Compared to treatment with RN, treatment with PN was not associated with increased RMB (p=0.92); however, treatment with ablation (OR 10.90; p<0.001) and with surveillance (OR 4.83; p<0.001) were. Conclusions: The largest increase in RMB was associated with ablation treatment. Rates of RMB for tumors <2 cm and for older, sicker patients not undergoing treatment have correspondingly increased less, indicating RMB may not be as highly used for surveillance.

Clear Cell Renal Cell Carcinoma: Multiphasic Multidetector CT Imaging Features Help Predict Genetic Karyotypes

Radiology ◽  
2011 ◽  
Vol 261 (3) ◽  
pp. 854-862 ◽  
Author(s):  
Steven C. Sauk ◽  
Margaret S. Hsu ◽  
Daniel J. A. Margolis ◽  
David S. K. Lu ◽  
Nagesh P. Rao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Multidetector Ct ◽  
Ct Imaging ◽  
Imaging Features ◽  
Cell Renal Cell Carcinoma

scholarly journals Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hai-Yan Chen ◽  
Xue-Ying Deng ◽  
Yao Pan ◽  
Jie-Yu Chen ◽  
Yun-Ying Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Texture Analysis ◽  
Validation Cohort ◽  
Texture Features ◽  
Imaging Features ◽  
Cystic Neoplasms ◽  
Radiological Features ◽  
Model Combining ◽  
Enhanced Ct ◽  
Ct Texture Analysis

ObjectiveTo establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).Materials and MethodsFifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.ResultsFollowing multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.ConclusionThis study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study

2021 ◽  
Author(s):  
Xiaoli Li ◽  
Qianli Ma ◽  
Pei Nie ◽  
Yingmei Zheng ◽  
Cheng Dong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Chromophobe Renal Cell Carcinoma ◽  
Renal Oncocytoma ◽  
Clinical Factors ◽  
Data Set ◽  
Central Scar ◽  
Clinical Model ◽  
Radiomics Signature

Objective: Pre-operative differentiation between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is critical due to their different clinical behavior and different clinical treatment decisions. The aim of this study was to develop and validate a CT-based radiomics nomogram for the pre-operative differentiation of RO from chRCC. Methods: A total of 141 patients (84 in training data set and 57 in external validation data set) with ROs (n = 47) or chRCCs (n = 94) were included. Radiomics features were extracted from tri-phasic enhanced-CT images. A clinical model was developed based on significant patient characteristics and CT imaging features. A radiomics signature model was developed and a radiomics score (Rad-score) was calculated. A radiomics nomogram model incorporating the Rad-score and independent clinical factors was developed by multivariate logistic regression analysis. The diagnostic performance was evaluated and validated in three models using ROC curves. Results: Twelve features from CT images were selected to develop the radiomics signature. The radiomics nomogram combining a clinical factor (segmental enhancement inversion) and radiomics signature showed an AUC value of 0.988 in the validation set. Decision curve analysis revealed that the diagnostic performance of the radiomics nomogram was better than the clinical model and the radiomics signature. Conclusions: The radiomics nomogram combining clinical factors and radiomics signature performed well for distinguishing RO from chRCC. Advances in knowledge: Differential diagnosis between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is rather difficult by conventional imaging modalities when a central scar was present. A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of RO from chRCC with improved diagnostic efficacy. The CT-based radiomics nomogram might spare unnecessary surgery for RO.

Comparative study of CT and MRI appearances in mucinous tubular and spindle cell carcinoma and papillary renal cell carcinoma

2021 ◽  
pp. 20210548
Author(s):  
Dajun lu ◽  
Weibiao Yuan ◽  
Qingqiang Zhu ◽  
Jing Ye ◽  
Wenrong Zhu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Spindle Cell ◽  
Papillary Renal Cell Carcinoma ◽  
Spindle Cell Carcinoma ◽  
Ct Imaging ◽  
Imaging Features ◽  
Unenhanced Ct ◽  
Ct And Mri

Objective: To explore the feasibility of CT and MRI in differentiating mucinous tubular and spindle cell carcinoma (MTSCC) and papillary renal cell carcinoma (PRCC). Methods: 23 patients with MTSCC and 38 patients with PRCC were studied retrospectively. CT and MRI were undertaken to investigate differences in tumour characteristics. Results: 23 patients with MTSCC and 38 patients with PRCC (included 15 cases Type 1,and 23 cases Type 2), tumours (mean diameter 3.7 ± 1.6 cm vs 4.6 ± 1.7 cm, p < 0.05), cystic components (5 vs 32, p < 0.01), calcifications (3 vs 11, p > 0.05), haemorrhage (1 vs 22, p < 0.01), tumour boundaries (1 vs 37, p < 0.01), and homogeneous enhancement (20 vs 11, p < 0.01). The density of MTSCC was lower than that of PRCC, normal renal cortex (p < 0.05), except for the medulla(p > 0.05). MTSCC and PRCC tumour enhancement were lower than that for normal cortex and medulla during all enhanced phases (p < 0.05). Enhancement was higher with PRCC than with MTSCC tumours during all phases (p < 0.05). On MRI, nine cases of MTSCC and 19 cases of PRCC, tumour showed homogeneous (9 vs 3, p < 0.01), heterogeneous (0 vs 16, p < 0.01), hyperintense on T1WI (0 vs 15, p < 0.01), slightly hyperintense on T2WI (9 vs 1, p < 0.01), hypointense on T2WI (0 vs 15, p < 0.05) , relatively high signal intensity was seen on DWI (9 vs 15, p > 0.05), respectively. Conclusion: CT imaging features of MTSCC include isodense or hypodense mass on unenhanced CT, with unclear boundaries; however, PRCC showed mild hyperdensity, easily have cystic components. The degree enhancement of MTSCC is lower than that for PRCC. On MR, MTSCC was slightly hyperintense on T2WI, whereas PRCC was hypointense. Advances in knowledge: 1.CT imaging features of MTSCC include isodense or hypodense mass on unenhanced CT, with unclear boundaries. 2. CT imaging features of PRCC include mild hyperdensity on unenhanced CT, easily have cystic components. 3. On enhanced CT, the degree enhancement of MTSCC is lower than that for PRCC. On MR, MTSCC was slightly hyperintense on T2WI whereas PRCC was heterogeneously hypointense on T2WI.

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