Preoperative CT features to predict risk stratification of non-muscle invasive bladder cancer.
Purpose: To investigate whether preoperative CT features can be used to predict risk stratification of non-muscle invasive bladder cancer (NMIBC). Methods: The 168 patients with pathologically confirmed NMIBC who underwent preoperative CT urography were retrospectively analyzed and were divided into...
| Publicado en: | Abdominal Radiology Vol. 48; no. 2; pp. 659 - 669 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161717330&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161717330 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Feb2023 vid: 48 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161717330 160613907 10.1007/s00261-022-03730-y 161717330 ppf: 659 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Preoperative CT features to predict risk stratification of non-muscle invasive bladder cancer. aug: au: Chen, Li Zhang, Gumuyang Xu, Lili Zhang, Xiaoxiao Zhang, Jiahui Bai, Xin Jin, Ru Mao, Li Xiao, Xin Li, Xiuli Xie, Yi Jin, Zhengyu Sun, Hao affil: Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100730, Beijing, China sug: ab: Purpose: To investigate whether preoperative CT features can be used to predict risk stratification of non-muscle invasive bladder cancer (NMIBC). Methods: The 168 patients with pathologically confirmed NMIBC who underwent preoperative CT urography were retrospectively analyzed and were divided into training (n = 117) and testing (n = 51) sets. According to the European Association of Urology Guidelines, patients were classified into low-risk (n = 50), medium-risk (n = 23), and high-risk (n = 95) groups. A random over-sample was performed to handle the offset caused by the unbalanced groups. We measured some CT features that may help stratify which for modeling were determined using an F-test-based feature selection with a tenfold cross-validation procedure, and the Gaussian Naive Bayes model was trained on the entire training set. In the testing set, the performance of the model was evaluated. Results: The selected CT features were the maximum and the minimum diameter of the largest tumor, whether the largest tumor is located at the trigone, and tumor number. In the testing set, the model reached a macro- and micro- AUC of 0.783 and 0.745 with an accuracy of 0.529. As for the one-vs-rest problem, the model was most effective in identifying low-risk individuals, with an AUC, accuracy, sensitivity, and specificity of 0.870, 0.647, 1.000, and 0.438, respectively; the medium-risk group reached 0.814, 0.882, 0.250, and 0.936, respectively; the identification of the high-risk group was harder, going 0.665, 0.529, 0.250, and 0.870, respectively. Conclusion: It is feasible to predict the risk stratification of NMIBC using preoperative CT features. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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