Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.
Eur Radiol
; 32(4): 2340-2350, 2022 Apr.
Article
en En
| MEDLINE
| ID: mdl-34636962
ABSTRACT
OBJECTIVE:
To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT.METHODS:
This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed.RESULTS:
Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI.CONCLUSION:
Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading. KEY POINTS ⢠Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. ⢠Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. ⢠Shape features and first-order statistics features showed superior discriminative capability compared to texture features.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Carcinoma de Células Renales
/
Neoplasias Renales
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Eur Radiol
Asunto de la revista:
RADIOLOGIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
China