Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma
- PDF / 1,288,313 Bytes
- 9 Pages / 595.276 x 790.866 pts Page_size
- 56 Downloads / 192 Views
KIDNEYS, URETERS, BLADDER, RETROPERITONEUM
Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma Yajuan Li1 · Xialing Huang1 · Yuwei Xia2 · Liling Long1
© The Author(s) 2019
Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CTenhanced quantitative features and machine learning. Keywords Renal cell carcinoma · Oncocytoma · Radiomics · Computed tomography · Machine learning · Differential diagnosis
Introduction
* Liling Long [email protected] Yajuan Li [email protected] Xialing Huang [email protected] Yuwei Xia [email protected] 1
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi, China
Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, HaiDian District, Beijing 100192, China
2
The incidence of renal cell carcinoma is increasing worldwide [1]. Chromophobe cell carcinoma (chRCC) of the kidney is second only to clear cell carcinoma of the kidney and papillary cell carcinoma of the kidney [1–3]. Renal oncocytoma (RO) is a benign renal tumor, accounting for about 3–7% of all renal tumors [4, 5]. Medical imaging plays an important role in the clinical management of renal tumors, such as detection of renal tumors, prediction of benign and malignant tumors, grading, and surgical treatment [6, 7]. Studies have shown that chRCC and RO not only overlap in morphological
Data Loading...
