Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

Jun Cheng,Rohit Mehra,Michael Cheng,Liang Cheng,Dong Ni,Qianjin Feng,Kun Huang,Jie Zhang,Zhi Han,Wei Shao

doi:10.1038/s41467-020-15671-5

Abstract

TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin- stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis.

Highlights

TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-Renal cell carcinoma (RCC)) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images
Dataset 1 was obtained from Indiana University, consisting of 50 TFE3-RCC patients and 50 clear cell RCC (ccRCC) patients with matched gender and tumor grade
Our results showed that using the 30 features selected by the minimum redundancy maximum relevance algorithm, our best classifier, SVM with Gaussian kernel, attained an average area under the curve (AUC) of 0.886

Summary

Introduction

TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. Choueiri et al.[14] showed that VEGF-targeted agents demonstrated some efficacy in patients with metastatic TFE3-RCC in a small retrospective review Improving underdiagnosis of this rare subtype of RCC will facilitate sample curation, improve clinical trial access, and more importantly, contribute to the development of effective therapies for this group of patients. TFE3-RCC cases often feature epithelioid clear cells arranged in branching, papillary structures with fibrovascular cores and/or a nested architecture. These features are suggestive of TFE3-RCC, the spectrum of morphology is quite variable and can overlap with other RCC subtypes such as ccRCC or papillary RCC1,2. We want to apply machine learning to digitized H&E-stained pathological images and study whether it can help identify TFE3RCC unique image features and distinguish TFE3-RCC from the most common RCC subtype, ccRCC

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