Improving Renal Tumor Diagnosis with Computed Tomography and Artificial Neural Networks

Abstract

Background:Renal cell carcinoma is one of the most common cancers in Europe, with a total incidence rate of 18.4 cases per 100 000 population. There is currently significant overdiagnosis (11% to 30.9%) at times of planned surgery based on radiological studies. The purpose of this study was to create an artificial neural network (ANN) solution based on computed tomography (CT) images as an additional tool to improve the differentiation of malignant and benign renal tumors and to aid active surveillance. Material/Methods:A retrospective study based on CT images was conducted. Axial CT images of 357 renal tumor cases were collected. There were 265 (74.2%) cases histologically proven to be malignant, while 34 (9.5%) cases were benign. Radiologists diagnosed 58 (16.3%) cases as angiomyolipoma (AML), based on characteristic appearance, not confirmed histopathologically. For ANN training, the arterial CT phase images were used. A total of 7207 arterial-phase images were collected, then cropped and added to the database with the associated diagnosis. For the test dataset (ANN validation), 38 cases (10 benign, 28 malignant) were chosen by subgroup randomization to correspond to statistical tumor type distribution. The VGG-16 ANN architecture was used in this study. Results:Trained ANN correctly classified 23 out of 28 malignant tumors and 8 out of 10 benign tumors. Accuracy was 81.6% (95% confidence interval, 65.7-92.3%), sensitivity was 82.1% (63.1-93.9%), specificity was 80.0% (44.4-97.5%), and F1 score was 86.8% (74.7-94.5%). Conclusions:The created ANN achieved promising accuracy in differentiating benign vs malignant renal tumors.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

“…In line with AI in radiology, efforts to use AI in RCC histopathology have been undertaken in recent years. This relatively new field, which is called pathomics or computational pathology, can be used to improve efficiency, accessibility, cost-effectiveness, and time consumption, as well as enhance accuracy and reproducibility with lower subjectivity [11,[14][15][16][17]. In addition, Whole Slide Imaging (WSI) technology allows machine learning in pathology by providing an enormous amount of high-quality information for training and testing AI models to identify specific features and patterns that can be complex for even the human eye to discern [12,18,19].…”