Compare Fuhrman Nuclear and Chromophobe Tumor Grade on Chromophobe RCC

Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Purpose

Since the prognosis of renal cell carcinoma (RCC) patients with bone metastasis (BM) is poor, this study is aimed at using big data to build a machine learning (ML) model to predict the risk of BM in RCC patients.

Methods

A retrospective study was conducted on 40,355 RCC patients in the SEER database from 2010 to 2017. LASSO regression and multivariate logistic regression analysis was performed to determine independent risk factors of RCC-BM. Six ML algorithm models, including LR, GBM, XGB, RF, DT, and NBC, were used to establish risk models for predicting RCC-BM. The prediction performance of ML models was weighed by 10-fold cross-validation.

Results

The study investigated 40,355 patients diagnosed with RCC in the SEER database, where 1,811 (4.5%) were BM patients. Independent risk factors for BM were tumor grade, T stage, N stage, liver metastasis, lung metastasis, and brain metastasis. Among the RCC-BM risk prediction models established by six ML algorithms, the XGB model showed the best prediction performance (AUC = 0.891). Therefore, a network calculator based on the XGB model was established to individually assess the risk of BM in patients with RCC.

Conclusion

The XGB risk prediction model based on the ML algorithm performed a good prediction effect on BM in RCC patients.

Application and comparison of Fuhrman nuclear grading system with the novel tumor grading system for chromophobe renal cell carcinoma and its correlation with disease-specific events

Introduction

The grading system of chromophobe renal cell carcinoma (ChRCC) is not well established. In this study, we aimed to compare the application of Fuhrman nuclear grade (FNG) with the novel chromophobe tumor grade (CTG). We also evaluated the correlation of these two grading systems with the clinical outcome.

Materials and Methods

Consecutive cases of ChRCC diagnosed on nephrectomy during 2005-2014 were identified. The clinical details of the patients were retrieved. Histopathology slides were reviewed and the nuclear grading was assigned using standard FNG and the CTG system. The CTG and FNG gradings were correlated with clinical outcome.

Results

A total of 80 cases were retrieved. Distribution of FNG was as follows: FNG-1, 1 (1.3%); FNG-2, 23 (28.3%); FNG-3, 44 (55.0%); and FNG-4, 12 (15%). CTG distribution was as follows: CTG-1, 48 (60.0%); CTG-2, 20 (25.0%); and CTG-3 12 (15.0%). Follow-up data was available in 46 cases; the median follow-up was 23.9 months (range 1-96.4 months). The median time to recurrence/metastasis was 17.2 months (range 3.2-31.2 months). Mean disease-free survival (DFS) was 68.5 months. Both CTG (P < 0.001) and FNG (P = 0.001) correlated with DFS; however, only CTG retained this significance when only the nonsarcomatous cases were analyzed. On receiver operating characteristics curve analysis, CTG had higher predictive accuracy for DFS for the entire group, while FNG lost the statistical significance when the nonsarcomatous cases were analyzed. CTG (P = 0.001) but not FNG (P = 0.106) correlated with the disease-specific adverse events in non-sarcomatous cases.

Conclusions

It is possible to apply CTG in ChRCC. It is a better predictor of DFS and disease-specific adverse events. CTG is more appropriate and applicable than the FNG in grading ChRCC.

Updates in Histologic Grading of Urologic Neoplasms

CONTEXT.—

Tumor histology offers a composite view of the genetic, epigenetic, proteomic, and microenvironmental determinants of tumor biology. As a marker of tumor histology, histologic grading has persisted as a highly relevant factor in risk stratification and management of urologic neoplasms (ie, renal cell carcinoma, prostatic adenocarcinoma, and urothelial carcinoma). Ongoing research and consensus meetings have attempted to improve the accuracy, consistency, and biologic relevance of histologic grading, as well as provide guidance for many challenging scenarios.

OBJECTIVE.—

To review the most recent updates to the grading system of urologic neoplasms, including those in the 2016 4th edition of the World Health Organization (WHO) Bluebook, with emphasis on issues encountered in routine practice.

DATA SOURCES.—

Peer-reviewed publications and the 4th edition of the WHO Bluebook on the pathology and genetics of the urinary system and male genital organs.

CONCLUSIONS.—

This article summarizes the recently updated grading schemes for renal cell carcinoma, prostate adenocarcinomas, and bladder neoplasms of the genitourinary tract.