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Using deep learning to differentiate among histology renal tumor types in computed tomography scans
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Journal Article
Using deep learning to differentiate among histology renal tumor types in computed tomography scans
2025
Overview
Background
This study employed a convolutional neural network (CNN) to analyze computed tomography (CT) scans with the aim of differentiating among renal tumors according to histologic sub-type.
Methods
Contrast-enhanced CT images were collected from patients with renal tumors. The patient cohort was randomly split to create a training dataset (90%) and a testing dataset (10%). Following image dataset augmentation, Inception V3 and Resnet50 models were used to differentiate between renal tumors subtypes, including angiomyolipoma (AML), oncocytoma, clear cell renal cell carcinoma (ccRCC), chromophobe renal cell carcinoma (chRCC), and papillary renal cell carcinoma (pRCC). 5-fold cross validation was then used to evaluate the models in terms of classification performance.
Results
The study cohort comprised 554 patients, including those with angiomyolipoma (
n
= 67), oncocytoma (
n
= 34), clear cell renal cell carcinoma (
n
= 246), chromophobe renal cell carcinoma (
n
= 124), and papillary renal cell carcinoma (
n
= 83). Dataset augmentation of the training dataset included this to 4238 CT images for analysis. The accuracy of the models was as follows: Inception V3 (0.830) and Resnet 50 (0.849).
Conclusion
This study demonstrated the efficacy of using deep learning models for the classification of renal tumor subtypes from contrast-enhanced CT images. While the models showed promising accuracy, further development is necessary to improve their clinical applicability.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Accuracy
/ Adenoma, Oxyphilic - diagnostic imaging
/ Adenoma, Oxyphilic - pathology
/ Adult
/ Aged
/ Angiomyolipoma - diagnostic imaging
/ Biopsy
/ Cancer
/ Carcinoma, Renal Cell - diagnostic imaging
/ Carcinoma, Renal Cell - pathology
/ Clear cell-type renal cell carcinoma
/ Datasets
/ Female
/ Humans
/ Imaging
/ Kidney Neoplasms - classification
/ Kidney Neoplasms - diagnostic imaging
/ Kidney Neoplasms - pathology
/ Male
/ Medicine
/ Papillary renal cell carcinoma
/ Patients
/ Radiographic Image Interpretation, Computer-Assisted - methods
/ Tomography, X-Ray Computed - methods
/ Training
/ Tumors
