Radiomics-based machine learning role in differential diagnosis between small renal oncocytoma and clear cells carcinoma on contrast-enhanced CT: A pilot study.

ABSTRACT:

Purpose

To investigate the potential role of radiomics-based machine learning in differentiating small renal oncocytoma (RO) from clear cells carcinoma (ccRCC) on contrast-enhanced CT (CECT).

Material and methods

Fifty-two patients with small renal masses who underwent CECT before surgery between January 2016 and December 2020 were retrospectively included in the study. At pathology examination 39 ccRCC and 13 RO were identified. All lesions were manually delineated unenhanced (B), arterial (A) and venous (V) phases. Radiomics features were extracted using three different fixed bin widths (bw) of 25 HU, 10 HU, and 5 HU from each phase (B, A, V), and with different combinations (B+A, B+V, B+A+V, A+V), leading to 21 different datasets. Montecarlo Cross Validation technique was

SUBMITTER: Francischello R 

PROVIDER: S-EPMC11934289 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature