Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer
| Autor: | Dong-Yue Wen, Hui Qin, Jing Huang, Rui-Zhi Gao, Yun He, Rong Wen, Hong Yang, Xin-Rong Wang, Xin Li |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Tumor stage medicine Humans Radiology Nuclear Medicine and imaging Pathological Grading (tumors) Retrospective Studies Ultrasonography 030219 obstetrics & reproductive medicine Bladder cancer Radiological and Ultrasound Technology Receiver operating characteristic business.industry Ultrasound Area under the curve medicine.disease Random forest ROC Curve Urinary Bladder Neoplasms Area Under Curve Radiology business |
| Zdroj: | Journal of Ultrasound in Medicine. 40:2685-2697 |
| ISSN: | 1550-9613 0278-4297 |
| DOI: | 10.1002/jum.15659 |
| Popis: | Objectives To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients. Methods We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z-score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort. Results A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort. Conclusions The ultrasound-based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA. |
| Databáze: | OpenAIRE |
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