Abstract
Objective
The aim of this study was to evaluate the correlation between the tissue texture analysis and the histological subtypes, grade and stage of the disease in patients with renal cell carcinoma (RCC).
Materials and methods
Seventy-seven patients who underwent computed tomography due to renal mass and diagnosed with RCC as a result of pathological examination were retrospectively analyzed. In these analyses, the demographic characteristics, pathological and radiological findings of the patients were evaluated. The masses were introduced to the Radiomics extension of the software and the first- and second-order texture analysis parameters were obtained. The correlation of these parameters with histological subtype, Fuhrman grade and TNM stage was investigated.
Results
In the comparison of the Radiomics values by stages, “minimum”, “Long Run Low Gray-level Emphasis” values were higher in the stage 1–2 group, while “Energy”, “Total energy”, “Range”, “Joint Average”, “Sum Average”, “Gray-Level Non-Uniformity”, “Short-Run High Gray-level Emphasis “, “Run Length Non-Uniformity “and “High Gray-Level Run Emphasis “values were higher in the stage 3–4 group. Of these parameters, only “Gray-Level Non-Uniformity” and “Run Length Non-Uniformity’’ values were significantly lower in tumors with low Fuhrman grade (1–2) and low TNM stage (1–2). There was no statistically significant correlation between the parameters found to be significant in histological subtype differentiation and Fuhrman grade and TNM stage.
Conclusion
This study demonstrates that “Gray-Level Non-Uniformity” and “Run Length Non-Uniformity “parameters in the texture analysis method can be used to evaluate the prognosis in patients with RCC.
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Abbreviations
- RCC:
-
Renal cell carcinoma
- TNM:
-
Tumor, node, metastasis
- CT:
-
Computed tomography
- PACS:
-
Picture archiving and communication system
- DICOM:
-
Digital imaging and communications in medicine
- GLCM:
-
Gray-level co-occurrence matrix
- GLRLM:
-
Gray-level run length matrix
- ROC:
-
Receiver operating characteristic curve
- AUC:
-
Area under the ROC curve
- GLN:
-
Gray-level non-uniformity
- RLN:
-
Run length non-uniformity
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Servan Yaşar: Concept/ design, Data analysis/interpretation, Drafting article, Critical revision of article, Approval of article, Statistics, Data collection; Nuray Voyvoda: Concept/ design,Data analysis/interpretation, Critical revision of article, Approval of article; Bekir Voyvoda: Data collection; Tülay Özer: Concept/ design, Statistics, Critical revision of article.
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The study protocol was approved by the Ethics Committee (25/01/2019, Number: 46418926, Decision: 19/07)..
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Yaşar, S., Voyvoda, N., Voyvoda, B. et al. Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma. Abdom Radiol 45, 3821–3830 (2020). https://doi.org/10.1007/s00261-020-02495-6
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DOI: https://doi.org/10.1007/s00261-020-02495-6