Abstract
PurposeThis article analyzes the image heterogeneity of clear cell renal cell carcinoma (ccRCC) based on positron emission tomography (PET) and positron emission tomography-computed tomography (PET/CT) texture parameters, and provides a new objective quantitative parameter for predicting pathological Fuhrman nuclear grading before surgery.MethodsA retrospective analysis was performed on preoperative PET/CT images of 49 patients whose surgical pathology was ccRCC, 27 of whom were low grade (Fuhrman I/II) and 22 of whom were high grade (Fuhrman III/IV). Radiological parameters and standard uptake value (SUV) indicators on PET and computed tomography (CT) images were extracted by using the LIFEx software package. The discriminative ability of each texture parameter was evaluated through receiver operating curve (ROC). Binary logistic regression analysis was used to screen the texture parameters with distinguishing and diagnostic capabilities and whose area under curve (AUC) > 0.5. DeLong's test was used to compare the AUCs of PET texture parameter model and PET/CT texture parameter model with traditional maximum standardized uptake value (SUVmax) model and the ratio of tumor SUVmax to liver SUVmean (SUL)model. In addition, the models with the larger AUCs among the SUV models and texture models were prospectively internally verified.ResultsIn the ROC curve analysis, the AUCs of SUVmax model, SUL model, PET texture parameter model, and PET/CT texture parameter model were 0.803, 0.819, 0.873, and 0.926, respectively. The prediction ability of PET texture parameter model or PET/CT texture parameter model was significantly better than SUVmax model (P = 0.017, P = 0.02), but it was not better than SUL model (P = 0.269, P = 0.053). In the prospective validation cohort, both the SUL model and the PET/CT texture parameter model had good predictive ability, and the AUCs of them were 0.727 and 0.792, respectively.ConclusionPET and PET/CT texture parameter models can improve the prediction ability of ccRCC Fuhrman nuclear grade; SUL model may be the more accurate and easiest way to predict ccRCC Fuhrman nuclear grade.Graphic abstract
Highlights
According to the cases announced by the American Cancer Society in 2021, the number of new kidney cancer cases was 76,080, and the number of new kidney cancer deaths was 13,780 [1]
Studies have shown that [3, 4] low grade clear cell renal cell carcinoma (ccRCC) is associated with a good prognosis and high grade ccRCC is associated with higher infiltration capacity, higher possibility of metastasis, and poor prognosis
Due to the high spatiotemporal heterogeneity of ccRCC, the biopsy tissue only represents a part of the lesion, which may lead to selection bias and cannot well reflect the Fuhrman nuclear grade of the entire tumor
Summary
According to the cases announced by the American Cancer Society in 2021, the number of new kidney cancer cases was 76,080, and the number of new kidney cancer deaths was 13,780 [1]. Due to the high spatiotemporal heterogeneity of ccRCC, the biopsy tissue only represents a part of the lesion, which may lead to selection bias and cannot well reflect the Fuhrman nuclear grade of the entire tumor This invasive operation has disadvantages such as poor repeatability and complications. Association (AUA), and European Society for Medical Oncology (ESMO) and European Association of Urology (EAU), it is generally not recommended to use FDG as a kidney tumor imaging agent [7,8,9]. This does not mean that PET/CT examination is useless for the diagnosis of ccRCC. We established PET texture parameter model, PET/CT texture parameter model, SUVmax model, and SUL model and evaluate the predictive ability of these four models
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