| Title: |
Differentiating between renal medullary and clear cell renal carcinoma with a machine learning radiomics approach |
| Authors: |
Jiwani, Rahim; Pal, Koustav; Paolucci, Iwan; Odisio, Bruno; Brock, Kristy; Tannir, Nizar M; Shapiro, Daniel D; Msaouel, Pavlos; Sheth, Rahul A |
| Contributors: |
Cancer Center Support Grant; National Cancer Institute; MD Anderson’s Prometheus; Image Guided Cancer Therapy Research Program; Andrew Sabin Family Foundation Fellowship; Gateway for Cancer Research; Translational Research Partnership Award; United States Department of Defense; Advanced Discovery Award; Kidney Cancer Association; Translational Research Award; V Foundation; MD Anderson Physician-Scientist Award; Johnson Foundation |
| Source: |
The Oncologist ; volume 30, issue 2 ; ISSN 1083-7159 1549-490X |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2025 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/oncolo/oyae337 |
| Availability: |
https://doi.org/10.1093/oncolo/oyae337; https://academic.oup.com/oncolo/article-pdf/30/2/oyae337/61934864/oyae337.pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.D683749 |
| Database: |
BASE |