FNAC Diagnosis of eosinophilic variant of chromophobe renal cell carcinoma – A challenge for cytopathologists

Chromophobe Renal Cell Carcinoma (ChRCC) is a rare distinct subtype of Renal cell carcinoma. It arises from intercalated cells of the renal cortex. The cytomorphological features of ChRCC show significant overlap with Clear cell Renal Cell Carcinoma (CCRCC) and Oncocytoma. The prognosis of ChRCC is intermediate between benign Renal Oncocytoma and the relatively aggressive CCRCC. Hence, a correct pre or intra-operative cytodiagnosis helps in deciding the extent of surgery. We report a case of eosinophilic variant of ChRCC in a 70 years female, diagnosed on Fine Needle Aspiration Cytology (FNAC).The Immunocytochemistry (ICC), histology and Immunohistochemistry confirmed the diagnosis of ChRCC. Diagnosing ChRCC; especially its eosinophilic variant on FNAC is very challenging. Prominent cellular heterogeneity, pleomorphism, perinuclear halos and binucleation are important diagnostic clues for cytodiagnosis of ChRCC. In difficult cases, ICC helps in confirming the diagnosis.

Automated Classification of Papillary Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma Based on a Small Computed Tomography Imaging Dataset Using Deep Learning

ObjectivesThis study was conducted in order to design and develop a framework utilizing deep learning (DL) to differentiate papillary renal cell carcinoma (PRCC) from chromophobe renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on a small set of computed tomography (CT) images and provide a feasible method that can be applied to light devices.MethodsTraining and validation datasets were established based on radiological, clinical, and pathological data exported from the radiology, urology, and pathology departments. As the gold standard, reports were reviewed to determine the pathological subtype. Six CNN-based models were trained and validated to differentiate the two subtypes. A special test dataset generated with six new cases and four cases from The Cancer Imaging Archive (TCIA) was applied to validate the efficiency of the best model and of the manual processing by abdominal radiologists. Objective evaluation indexes [accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC)] were calculated to assess model performance.ResultsThe CT image sequences of 70 patients were segmented and validated by two experienced abdominal radiologists. The best model achieved 96.8640% accuracy (99.3794% sensitivity and 94.0271% specificity) in the validation set and 100% (case accuracy) and 93.3333% (image accuracy) in the test set. The manual classification achieved 85% accuracy (100% sensitivity and 70% specificity) in the test set.ConclusionsThis framework demonstrates that DL models could help reliably predict the subtypes of PRCC and ChRCC.

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

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.