ArticleAbdominal radiology (New York)2022
Magnetic resonance imaging (MRI) helps differentiate renal cell carcinoma with sarcomatoid differentiation from renal cell carcinoma without sarcomatoid differentiation.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 13 citations in OpenAlex.
- MRI-based habitat radiomics for assessing synchronous metastatic risk in renal cell carcinoma: a multicenter study.Abdominal radiology (New York) · 2026Article
- Renal cell carcinoma and venous tumor thrombus: predicting sarcomatoid dedifferentiation through preoperative IVIM-based MR imaging.Abdominal radiology (New York) · 2024Article
- Diagnostic performance of multiparametric magnetic resonance imaging in the differentiation of clear cell renal cell cancer.Abdominal radiology (New York) · 2023Article
- Sarcomatoid renal cell carcinoma: MRI features and their association with survival.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
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8 authors at 2 institutions in 1 country.
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Abstract
purposeThe aim of the present study is to identify predictive imaging findings and construct a diagnostic model for differentiating renal cell carcinoma (RCC) with and without sarcomatoid dedifferentiation (sRCC and non-sRCC).
methodsThis study is a single-center retrospective study. All patients had magnetic resonance imaging (MRI) with gradient-echo T1-weighted images, single-shot T2-weighted images (T2WI), and enhanced nephrographic phase images. Forty pathologically confirmed sRCCs and 80 non-sRCCs were included in this study. Control cases were selected by matching the tumor diameter and the year of MRI. Two radiologists independently evaluated the following findings: growth pattern, presence of low-intensity area on T2WI in the tumor (T2LIA), presence of non-enhancing area, local tumor stage, and presence of regional lymphadenopathy. Two radiologists measured the diameter of the tumor, T2LIA, and the non-enhancing area. Multivariable logistic regression analysis was used to identify independent predictive factors for differentiating sRCC from non-sRCC. Selected variables were entered in the logistic regression model, and the area under the curve (AUC) was calculated for each reader with 95% confidence intervals (CIs).
resultsLarger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 were associated with sRCC, and selected for the subsequent construction of a logistic regression model. With this model, the AUCs were 0.76 (95% CI, 0.66-0.85) and 0.70 (95% CI, 0.59-0.81) for prediction of sRCC.
conclusionIn conclusion, larger T2LIA-to-tumor diameter ratio, regional lymphadenopathy, and local tumor stage 4 are predictive findings of sRCC. As a result, the model constructed using these findings demonstrated a moderate degree of diagnostic accuracy.
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