Cancer medicine · 2024
A nomogram based on TFE3 IHC results and clinical factors as a preliminary screening scheme for TFE3-rearranged renal cell carcinoma.
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Li Pengju, Xu Quanhui, Chen Minyu, Zhu Jiangquan, Wang Yinghan, Mumin Mukhtar A, Huang Kangbo, Jiang Zeying, Liang Hui, Deng Qiong, Wang Zhu, Liao Bing, Chen Wenfang, Cao Yun, Cao Jiazheng, Luo Junhang
Аннотация
BACKGROUND: TFE3 immunohistochemistry (TFE3-IHC) is controversial in the diagnosis of TFE3-rearranged renal cell carcinoma (TFE3-rearranged RCC). This study is to investigate the accuracy and sensitivity of IHC and establish a predictive model to diagnose TFE3-rearranged RCC.
METHODS: Retrospective analysis was performed by collecting IHC and fluorescence in situ hybridization (FISH) results from 228 patients. IHC results were evaluated using three scoring systems. Scoring system 1 is graded based on nuclear staining intensity, scoring system 2 is graded based on the percentage of stained tumor cell nuclei, and scoring system 3 is graded based on both the nuclear staining intensity and the percentage. We collected patients' IHC results and clinical information. Important variables were screened based on univariate logistic regression analysis. Then, independent risk factors were established through multivariate logistic regression, and a nomogram model was constructed. The model was validated in internal test set and external validation set. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) were generated to assess discriminative ability of the model.
RESULTS: The accuracy of IHC based on three scoring systems were 0.829, 0.772, and 0.807, respectively. The model included four factors including age, gender, lymph node metastasis and IHC results. Area under the curve (AUC) values were 0.935 for the training set, 0.934 for the internal test set, 0.933 for all 228 patients, and 0.916 for the external validation set.
CONCLUSIONS: TFE3 IHC has high accuracy in the diagnosis of TFE3-rearranged RCC. Clinical information such as age and lymph node metastasis are independent risk factors, which can be used as a supplement to the results of TFE3 IHC. This study confirms the value of IHC in the diagnosis of TFE3-rearranged RCC. The accuracy of the diagnosis can be improved by incorporating IHC with other clinical risk factors.
METHODS: Retrospective analysis was performed by collecting IHC and fluorescence in situ hybridization (FISH) results from 228 patients. IHC results were evaluated using three scoring systems. Scoring system 1 is graded based on nuclear staining intensity, scoring system 2 is graded based on the percentage of stained tumor cell nuclei, and scoring system 3 is graded based on both the nuclear staining intensity and the percentage. We collected patients' IHC results and clinical information. Important variables were screened based on univariate logistic regression analysis. Then, independent risk factors were established through multivariate logistic regression, and a nomogram model was constructed. The model was validated in internal test set and external validation set. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) were generated to assess discriminative ability of the model.
RESULTS: The accuracy of IHC based on three scoring systems were 0.829, 0.772, and 0.807, respectively. The model included four factors including age, gender, lymph node metastasis and IHC results. Area under the curve (AUC) values were 0.935 for the training set, 0.934 for the internal test set, 0.933 for all 228 patients, and 0.916 for the external validation set.
CONCLUSIONS: TFE3 IHC has high accuracy in the diagnosis of TFE3-rearranged RCC. Clinical information such as age and lymph node metastasis are independent risk factors, which can be used as a supplement to the results of TFE3 IHC. This study confirms the value of IHC in the diagnosis of TFE3-rearranged RCC. The accuracy of the diagnosis can be improved by incorporating IHC with other clinical risk factors.