Nengfeng Yu , Congcong Xu , Yiwei Jiang , Dekai Liu , Lianghao Lin , Gangfu Zheng , Jiaqi Du , Kefan Yang , Qifeng Zhong , Yicheng Chen , Yichun Zheng
{"title":"基于 CT 测量的腹部脂肪特征预测 Ta/T1 期 NMIBC 初次手术后的早期复发","authors":"Nengfeng Yu , Congcong Xu , Yiwei Jiang , Dekai Liu , Lianghao Lin , Gangfu Zheng , Jiaqi Du , Kefan Yang , Qifeng Zhong , Yicheng Chen , Yichun Zheng","doi":"10.1016/j.clgc.2024.102199","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>This study aimed to assess the predictive value of abdominal fat characteristics measured by computed tomography (CT) in identifying early recurrence within one year post-initial transurethral resection of bladder tumor (TURBT) in patients with nonmuscle-invasive bladder cancer (NMIBC). A predictive model integrating fat features and clinical factors was developed to guide individualized treatment.</p></div><div><h3>Materials and Methods</h3><p>A retrospective analysis of 203 NMIBC patients from two medical centers was conducted. Abdominal CT images were analyzed using 3D Slicer software. Spearman correlation, logistic regression, and the Lasso algorithm were employed for data analysis. Predictive efficacy was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) and decision curve analysis (DCA). Calibration was evaluated using the Hosmer-Lemeshow test.</p></div><div><h3>Results</h3><p>Significant differences in abdominal fat characteristics were found between the recurrence and nonrecurrence groups. All fat features positively correlated with body mass index (BMI), with bilateral perirenal fat thickness (PrFT) showing superior predictive performance. Multivariate logistic regression identified independent predictors of early recurrence, including tumor number, early perfusion chemotherapy, left and right PrFT, and visceral fat area (VFA) at umbilical and renal hilum levels. The Lasso-based model achieved an AUC of 0.904, outperforming existing models.</p></div><div><h3>Conclusion</h3><p>Abdominal fat characteristics, especially bilateral PrFT, strongly correlate with early recurrence in NMIBC. The Lasso-based model, integrating fat and clinical factors, offers superior predictive efficacy and could improve individualized treatment strategies.</p></div>","PeriodicalId":10380,"journal":{"name":"Clinical genitourinary cancer","volume":"22 6","pages":"Article 102199"},"PeriodicalIF":2.3000,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characteristics of Abdominal Fat Based on CT Measurements to Predict Early Recurrence After Initial Surgery of NMIBC in Stage Ta/T1\",\"authors\":\"Nengfeng Yu , Congcong Xu , Yiwei Jiang , Dekai Liu , Lianghao Lin , Gangfu Zheng , Jiaqi Du , Kefan Yang , Qifeng Zhong , Yicheng Chen , Yichun Zheng\",\"doi\":\"10.1016/j.clgc.2024.102199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><p>This study aimed to assess the predictive value of abdominal fat characteristics measured by computed tomography (CT) in identifying early recurrence within one year post-initial transurethral resection of bladder tumor (TURBT) in patients with nonmuscle-invasive bladder cancer (NMIBC). A predictive model integrating fat features and clinical factors was developed to guide individualized treatment.</p></div><div><h3>Materials and Methods</h3><p>A retrospective analysis of 203 NMIBC patients from two medical centers was conducted. Abdominal CT images were analyzed using 3D Slicer software. Spearman correlation, logistic regression, and the Lasso algorithm were employed for data analysis. Predictive efficacy was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) and decision curve analysis (DCA). Calibration was evaluated using the Hosmer-Lemeshow test.</p></div><div><h3>Results</h3><p>Significant differences in abdominal fat characteristics were found between the recurrence and nonrecurrence groups. All fat features positively correlated with body mass index (BMI), with bilateral perirenal fat thickness (PrFT) showing superior predictive performance. Multivariate logistic regression identified independent predictors of early recurrence, including tumor number, early perfusion chemotherapy, left and right PrFT, and visceral fat area (VFA) at umbilical and renal hilum levels. The Lasso-based model achieved an AUC of 0.904, outperforming existing models.</p></div><div><h3>Conclusion</h3><p>Abdominal fat characteristics, especially bilateral PrFT, strongly correlate with early recurrence in NMIBC. The Lasso-based model, integrating fat and clinical factors, offers superior predictive efficacy and could improve individualized treatment strategies.</p></div>\",\"PeriodicalId\":10380,\"journal\":{\"name\":\"Clinical genitourinary cancer\",\"volume\":\"22 6\",\"pages\":\"Article 102199\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical genitourinary cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1558767324001691\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical genitourinary cancer","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1558767324001691","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Characteristics of Abdominal Fat Based on CT Measurements to Predict Early Recurrence After Initial Surgery of NMIBC in Stage Ta/T1
Introduction
This study aimed to assess the predictive value of abdominal fat characteristics measured by computed tomography (CT) in identifying early recurrence within one year post-initial transurethral resection of bladder tumor (TURBT) in patients with nonmuscle-invasive bladder cancer (NMIBC). A predictive model integrating fat features and clinical factors was developed to guide individualized treatment.
Materials and Methods
A retrospective analysis of 203 NMIBC patients from two medical centers was conducted. Abdominal CT images were analyzed using 3D Slicer software. Spearman correlation, logistic regression, and the Lasso algorithm were employed for data analysis. Predictive efficacy was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) and decision curve analysis (DCA). Calibration was evaluated using the Hosmer-Lemeshow test.
Results
Significant differences in abdominal fat characteristics were found between the recurrence and nonrecurrence groups. All fat features positively correlated with body mass index (BMI), with bilateral perirenal fat thickness (PrFT) showing superior predictive performance. Multivariate logistic regression identified independent predictors of early recurrence, including tumor number, early perfusion chemotherapy, left and right PrFT, and visceral fat area (VFA) at umbilical and renal hilum levels. The Lasso-based model achieved an AUC of 0.904, outperforming existing models.
Conclusion
Abdominal fat characteristics, especially bilateral PrFT, strongly correlate with early recurrence in NMIBC. The Lasso-based model, integrating fat and clinical factors, offers superior predictive efficacy and could improve individualized treatment strategies.
期刊介绍:
Clinical Genitourinary Cancer is a peer-reviewed journal that publishes original articles describing various aspects of clinical and translational research in genitourinary cancers. Clinical Genitourinary Cancer is devoted to articles on detection, diagnosis, prevention, and treatment of genitourinary cancers. The main emphasis is on recent scientific developments in all areas related to genitourinary malignancies. Specific areas of interest include clinical research and mechanistic approaches; drug sensitivity and resistance; gene and antisense therapy; pathology, markers, and prognostic indicators; chemoprevention strategies; multimodality therapy; and integration of various approaches.