Radiomics and Machine Learning for Differentiating Oncocytoma from Chromophobe Renal Cell Carcinoma on Nephrographic Phase CT
Öz
Background: This study investigated a radiomics and machine learning methodology for differentiating chromophobe renal cell carcinoma (ChRCC) from renal oncocytoma (RO) using nephrographic phase computed tomography (CT) im-ages, aiming to enhance preoperative diagnostic strategies.
Methods: A retrospective cohort of 66 patients (36 ChRCC, 30 RO) undergoing contrast-enhanced CT was analyzed. Man-ual 3D segmentation of renal masses on nephrographic phase images yielded 107 radiomics features. LASSO regression identified the most discriminative features for dimensionality reduction. Four machine learning algorithms (RFC, SVM, Decision Tree, XGBoost) were trained and validated using a 70:30 data split and 10-fold cross-validation. Diagnostic per-formance was quantified by sensitivity, specificity, and AUC.
Results: LASSO regression identified 10 pivotal radiomics parameters, including first- and second-order features (e.g., GLCM, GLRLM), reflecting subtle architectural differences. XGBoost showed superior diagnostic performance (AUC: 0.979, 95% CI: 0.967–0.991, sensitivity: 89.75%, specificity: 94.55%). SVM achieved an AUC of 0.939 (95% CI: 0.909–0.968, sensitivity: 91.5%, specificity: 89.25%). Decision Tree (AUC: 0.906, sensitivity: 92.44%, specificity: 91.75%) and RFC (AUC: 0.90, sensitivity: 91.3%, specificity: 88.2%) also performed well. No significant age or gender differences were noted be-tween cohorts (p > 0.05).
Conclusion: Integrating CT-based radiomics with machine learning, particularly XGBoost, offers a highly accurate, non-invasive paradigm for preoperative ChRCC and RO differentiation. This approach holds substantial promise for optimizing clinical decision-making, supporting nephron-sparing interventions and potentially reducing overtreatment of benign renal oncocytomas.
Anahtar Kelimeler
Radiomics, Machine Learning, Chromophobe Renal Cell Carcinoma, Renal Oncocytoma, Computed Tomography
Destekleyen Kurum
Bulunmamaktadır.
Etik Beyan
Bu çalışma, Helsinki Bildirgesi'nde belirtilen etik ilkelere uygun olarak gerçekleştirilmiştir. Çalışma, Kurumsal Etik Kurulumuz (IRB) tarafından onaylanmıştır; IRB tarafından bilgilendirilmiş onamdan muafiyet verilmiştir.
Teşekkür
Bulunmamaktadır.
Kaynakça
- Van Oostenbrugge TJ, Fütterer JJ, Mulders PF. Diagnostic imaging for solid renal tumors: a pictorial review. Kidney Cancer. 2018;2(2):79-93.
- Souza D, Bernal M. Incidence, prevalence and mortality of kidney cancer in Spain: estimates and projections for the 1998–2022 period. Actas Urol Esp (Engl Ed). 2012;36(9):521-6.
- Capitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, et al. Epidemiology of renal cell carcinoma. Eur Urol. 2019;75(1):74-84.
- Padala SA, Barsouk A, Thandra KC, Saginala K, Mohammed A, Vakiti A, et al. Epidemiology of renal cell carcinoma. World J Oncol. 2020;11(3):79.
- Ng KL, Rajandram R, Morais C, Yap NY, Samaratunga H, Gobe GC, et al. Differentiation of oncocytoma from chromophobe renal cell carcinoma (RCC): can novel molecular biomarkers help solve an old problem? J Clin Pathol 2014;67(2):97-104.
- Hsieh JJ, Le V, Cao D, Cheng EH, Creighton CJ. Genomic classifications of renal cell carcinoma: a critical step towards the future application of personalized kidney cancer care with pan-omics precision. J Pathol 2018;244(5):525-37.
- Li Y, Huang X, Xia Y, Long L. Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma. Abdom Radiol 2020;45(10):3193-201.
- Wobker SE, Williamson SR. Modern pathologic diagnosis of renal oncocytoma. J Kidney Cancer VHL. 2017;4(4):1.
- Wu J, Zhu Q, Zhu W, Chen W, Wang S. Comparative study of CT appearances in renal oncocytoma and chromophobe renal cell carcinoma. Acta Radiol 2016;57(4):500-6.
- Cochand-Priollet B, Molinie V, Bougaran J, Bouvier R. Renal chromophobe cell carcinoma and oncocytoma: a comparative morphologic, histochemical, and immunohistochemical study of 124 cases. Arch Pathol Lab Med 1997;121(10):1081.