The 2012 ISUP Vancouver and 2016 WHO classification of adult renal tumors: changes for common renal tumors

The efficacy of the new Bosniak classification v.2019 in benign lesions prediction within the higher Bosniak cysts classes

INTRODUCTION

Identification of benign lesions among higher classes of renal Bosniak cysts who are vulnerable to active surveillance instead of surgical approach is still questionable. We aimed in this study to delineate the efficacy of the new Bosniak v2019 classification in benign lesions identification among those cases with higher Bosniak classes in comparison with the final histopathology.

MATERIALS

In a retrospective review between 2010 and 2021 for patients diagnosed as higher classes Bosniak renal masses was done. Patients' demographics and radiological data i.e.,: age, gender, and final Bosniak v2019 categorization for class III: (1) Enhancing thick wall/septa >4 mm (III-WS) and (2) Enhancing irregular wall/septa or convex protrusion with obtuse margins <3 mm (III-OP) and for class IV as: (1) Enhancing nodule or convex protrusion with obtuse margins >4 mm (IV-OP) and (2) Enhancing nodule or convex protrusion with acute margins of any size (IV-AP).

RESULTS

A total of 137 patients were included. Bosniak III was identified in 56 patients. Malignancy was detected in 74.5% of resected masses. Among resected Bosniak III cyst, 46.4% were benign histopathologically. Male gender and Bosniak III-OP were independent risks for malignancy among the resected Bosniak III cysts. Conversely, in resected Bosniak IV renal cysts, only 9 of resected masses were benign. In univariate analysis, male gender, absence of multilocular cyst and endophytic masses were predictors for malignancy in resected Bosniak IV cyst. None of the previous predictors was significant in multivariate analysis.

CONCLUSION

The Bosniak subclassification v.2019 can define benign lesions. Bosniak III-OP was an independent risk for malignancy detection among the resected Bosniak III cysts.

The levels of IL-6 and soluble IL-33R are increased in the renal vein during surgery for clear cell renal cell carcinoma

PURPOSE

The main aim was to map serum levels of IL-1/IL-6 family cytokines and relevant receptors from serum samples taken across treatment in patients with Renal Cell Carcinoma (RCC). Additionally, we explored the possible interactions between these measurements, immunohistochemistry and intratumoral blood flow.

METHODS

We included 40 patients undergoing open surgery for renal tumors. Blood samples were collected before, during (taken simultaneously from a peripheral site and the renal vein (RV) before clamping) and after surgery. Samples were analyzed for IL-6, IL-27, IL-31, OSM, TNF-α, serum (s)-gp130, s-IL-6Rα, s-IL-33R, IL-1Rα and VEGF. All 35 RCC tumors were histologically subtyped as clear cell (CCRCC), papillary or chromophobe. Immunohistochemistry for the CCRCC group included expression of IL-6/IL-6R. Intratumoral blood flow was determined by calculating intratumoral contrast enhancement on preoperative computerized tomography (CT) imaging.

RESULTS

In the CCRCC patients, the intraoperative RV concentration of IL-6 was significantly higher than in both the preoperative and postoperative samples (p = 0.005 and p = 0.032, respectively). Furthermore, the intraoperative ratio showed significantly higher levels of IL-6 in the RV than in the simultaneously drawn peripheral sample. Immunohistochemistry showed general expression of IL-6 (23/24) in both tumor cells and the vasculature (20/23). Moreover, s-IL-6R was expressed in tumor cells in 23/24 studied patients. Increased blood flow in the CCRCC tumors predicted increased IL-6 levels in the RV (p < 0.001). The other cytokines and receptors showed an overall stability across the measurements. However, the intraoperative ratios of IL-33R and gp130 showed significantly higher levels in the RV.

CONCLUSION

Serum levels of IL-6 increased during surgery. Intraoperative IL-6 and s-IL-33R values were higher in the RV compared to the periphery, suggesting secretion from the tumor or tumor microenvironment itself. Supportive of this is an almost general expression of IL-6/s-IL-6R in tumor cells and IL-6 in vasculature in the tumor microenvironment. Other studied cytokines/receptors were remarkably stable across all measurements.

Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images

RATIONALE AND OBJECTIVES

To evaluate the ability of artificial neural networks (ANN) fed with radiomic signatures (RSs) extracted from multidetector computed tomography images in differentiating the histopathological grades of clear cell renal cell carcinomas (ccRCCs).

MATERIALS AND METHODS

The multidetector computed tomography images of 227 ccRCCs were retrospectively analyzed. For each ccRCC, 14 conventional image features (CIFs) were extracted manually by two radiologists, and 556 texture features (TFs) were extracted by a free software application, MaZda (version 4.6). The high-dimensional dataset of these RSs was reduced using the least absolute shrinkage and selection operator. Five minimum mean squared error models (minMSEMs) for predicting the ccRCC histopathological grades were constructed from the CIFs, the TFs of the corticomedullary phase images (CMP), and the TFs of the parenchyma phase (PP) images and their combinations, respectively abbreviated as CIF-minMSEM, CMP-minMSEM, PP-minMSEM, CIF+CMP-minMSEM, and CIF+PP-minMSEM. The RSs of each model were fed 30 times consecutively into an ANN for machine learning, and the predictive accuracy of each time ML was recorded for the statistical analysis.

RESULTS

The five predictive models were constructed from 12, 19, and 10 features selected from the CIFs, the TFs of the CMP images, and that of PP images, respectively. On the basis of their accuracy across the whole cohort, the five models were ranked as follows: CIF+CMP-minMSEM (accuracy: 94.06% ± 1.14%), CIF + PP-minMSEM (accuracy: 93.32% ± 1.23%), CIF-minMSEM (accuracy: 92.26% ± 1.65%), CMP-minMSEM (accuracy: 91.76% ± 1.74%), and PP-minMSEM (accuracy: 90.89% ± 1.47%).

CONCLUSION

Machine learning based on ANN helped establish an optimal predictive model, and TFs contributed to the development of high accuracy predictive models. The CIF+CMP-minMSEM showed the greatest accuracy for differentiating low- and high-grade ccRCCs.

Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment

Cystic renal cell carcinoma (RCC) is almost certainly overdiagnosed and overtreated. Efforts to diagnose and treat RCC at a curable stage result in many benign neoplasms and indolent cancers being resected without clear benefit. This is especially true for cystic masses, which compared with solid masses are more likely to be benign and, when malignant, less aggressive. For more than 30 years, the Bosniak classification has been used to stratify the risk of malignancy in cystic renal masses. Although it is widely used and still effective, the classification does not formally incorporate masses identified at MRI or US or masses that are incompletely characterized but are highly likely to be benign, and it is affected by interreader variability and variable reported malignancy rates. The Bosniak classification system cannot fully differentiate aggressive from indolent cancers and results in many benign masses being resected. This proposed update to the Bosniak classification addresses some of these shortcomings. The primary modifications incorporate MRI, establish definitions for previously vague imaging terms, and enable a greater proportion of masses to enter lower-risk classes. Although the update will require validation, it aims to expand the number of cystic masses to which the Bosniak classification can be applied while improving its precision and accuracy for the likelihood of cancer in each class.

Predictive models composed by radiomic features extracted from multi-detector computed tomography images for predicting low- and high- grade clear cell renal cell carcinoma: A STARD-compliant article

To evaluate the values of conventional image features (CIFs) and radiomic features (RFs) extracted from multi-detector computed tomography (MDCT) images for predicting low- and high-grade clear cell renal cell carcinoma (ccRCC).Two hundred twenty-seven patients with ccRCC were retrospectively recruited. Five hundred seventy features including 14 CIFs and 556 RFs were extracted from MDCT images of each ccRCC. The CIFs were extracted manually and RFs by the free software-MaZda. Least absolute shrinkage and selection operator (Lasso) was applied to shrink the high-dimensional data set and select the features. Five predictive models for predicting low- and high-grade ccRCC were constructed by the selected CIFs and RFs. The 5 models were as follows: model of minimum mean squared error (minMSE) of CIFs (CIF-minMSE), minMSE of cortico-medullary phase (CMP) of kidney (CMP-minMSE), minMSE of parenchyma phase (PP) of kidney (PP-minMSE), the combined model of CIF-minMSE and CMP-minMSE (CIF-CMP-minMSE), and the combined model of CIF-minMSE and PP-minMSE (CIF-PP-minMSE). The Lasso regression equation of each model was constructed, and the predictive values were calculated. The receiver operating characteristic (ROC) curves of predictive values of the 5 models were drawn by SPSS19.0, and the areas under the curves (AUCs) were calculated.According to Lasso regression, 12, 19 and 10 features were respectively selected from the CIFs, RFs of CMP image and that of PP images to construct the 5 predictive models. The models ordered by their AUCs from large to small were CIF-CMP-minMSE (AUC: 0.986), CIF-PP-minMSE (AUC: 0.981), CIF-minMSE (AUC: 0.980), CMP-minMSE (AUC: 0.975), and PP-minMSE (AUC: 0.963). The maximum diameter of the largest axial section of ccRCC had a maximum weight in predicting the grade of ccRCC among all the features, and its cutoff value was 6.15 cm with a sensitivity of 0.901, a specificity of 0.963, and an AUC of 0.975.When combined with CIFs, RFs extracted from MDCT images contributed to the larger AUC of the predictive model, but were less valuable than CIFs when used alone. The CIF-CMP-minMSE was the optimal predictive model. The maximum diameter of the largest axial section of ccRCC had the largest weight in all features.