Faculty, Staff and Student Publications
Publication Date
2-6-2025
Journal
The Oncologist
Abstract
Background: The objective of this study was to develop and validate a radiomics-based machine learning (ML) model to differentiate between renal medullary carcinoma (RMC) and clear cell renal carcinoma (ccRCC).
Methods: This retrospective Institutional Review Board -approved study analyzed CT images and clinical data from patients with RMC (n = 87) and ccRCC (n = 93). Patients without contrast-enhanced CT scans obtained before nephrectomy were excluded. A standard volumetric software package (MIM 7.1.4, MIM Software Inc.) was used for contouring, after which 949 radiomics features were extracted with PyRadiomics 3.1.0. Radiomics analysis was then performed with RadAR for differential radiomics analysis. ML was then performed with extreme gradient boosting (XGBoost 2.0.3) to differentiate between RMC and ccRCC. Three separate ML models were created to differentiate between ccRCC and RMC. These models were based on clinical demographics, radiomics, and radiomics incorporating hemoglobin electrophoresis for sickle cell trait, respectively.
Results: Performance metrics for the 3 developed ML models were as follows: demographic factors only (AUC = 0.777), calibrated radiomics (AUC = 0.915), and calibrated radiomics with sickle cell trait incorporated (AUC = 1.0). The top 4 ranked features from differential radiomic analysis, ranked by their importance, were run entropy (preprocessing filter = original, AUC = 0.67), dependence entropy (preprocessing filter = wavelet, AUC = 0.67), zone entropy (preprocessing filter = original, AUC = 0.67), and dependence entropy (preprocessing filter = original, AUC = 0.66).
Conclusion: A radiomics-based machine learning model effectively differentiates between ccRCC and RMC. This tool can facilitate the radiologist's ability to suspicion and decrease the misdiagnosis rate of RMC.
Keywords
Humans, Machine Learning, Carcinoma, Renal Cell, Female, Male, Middle Aged, Kidney Neoplasms, Retrospective Studies, Aged, Diagnosis, Differential, Tomography, X-Ray Computed, Adult, Radiomics, renal medullary carcinoma, clear cell renal carcinoma, radiomics
DOI
10.1093/oncolo/oyae337
PMID
39963829
PMCID
PMC11833245
PubMedCentral® Posted Date
2-18-2025
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons