基于 CT 纹理分析预测肾透明细胞癌的 Fuhrman 病理分级

IF 1.5 Q3 UROLOGY & NEPHROLOGY American journal of clinical and experimental urology Pub Date : 2024-02-15 eCollection Date: 2024-01-01
Zhuang Dong, Chao Guan, Xuezhen Yang
{"title":"基于 CT 纹理分析预测肾透明细胞癌的 Fuhrman 病理分级","authors":"Zhuang Dong, Chao Guan, Xuezhen Yang","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To study the predictive performance of the imaging model based on the texture analysis of CT plain scan in distinguishing between low (grade I and II) and high (grade III and IV) of Fuhrman pathological grade of renal clear cell carcinoma.</p><p><strong>Methods: </strong>The clinical data of 94 patients with ccRCC who underwent CT scan and were confirmed by biopsy or surgery in TCGA-KIRC public database were retrospectively analyzed. There were 32 cases of low-grade ccRCC and 62 cases of high-grade ccRCC. The patients were randomly divided into training set and verification set according to the proportion of 7:3 by stratified sampling method. The imaging characteristics of ccRCC were calculated in the plain CT images. Lasso regression was used to reduce the dimensionality of the imaging characteristics of the training set, and binary logistic regression was used to construct the prediction model. Bootstrap method was used to verify the training set model and the validation set model, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated respectively.</p><p><strong>Results: </strong>Binary logistic regression showed that only imaging features were independent risk factors for predicting the Furhman classification of ccRCC. The predictive model was y = 1/[1 + exp (-z)], z = 1.274 × imaging risk score + 0.072. The results of bootstrap internal validation showed that the AUC of the training group was 0.961 (95% CI: 0.900-0.913). The Hosmer-Lemeshow goodness of fit test showed that the prediction model had a good calibration in the training group (P = 0.416). The AUC of prediction model in validation group was 0.731 (95% CI: 0.500-1.000). The Hosmer-Lemeshow goodness of fit test results showed that the prediction model had a good calibration in the validation group (P = 0.592).</p><p><strong>Conclusion: </strong>The model based on CT texture analysis has a good predictive effect in differentiating low-grade and high-grade ccRCC and can provide reference for the treatment and prognosis of patients.</p>","PeriodicalId":7438,"journal":{"name":"American journal of clinical and experimental urology","volume":"12 1","pages":"28-35"},"PeriodicalIF":1.5000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10944366/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prediction of Fuhrman pathological grade of renal clear cell carcinoma based on CT texture analysis.\",\"authors\":\"Zhuang Dong, Chao Guan, Xuezhen Yang\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To study the predictive performance of the imaging model based on the texture analysis of CT plain scan in distinguishing between low (grade I and II) and high (grade III and IV) of Fuhrman pathological grade of renal clear cell carcinoma.</p><p><strong>Methods: </strong>The clinical data of 94 patients with ccRCC who underwent CT scan and were confirmed by biopsy or surgery in TCGA-KIRC public database were retrospectively analyzed. There were 32 cases of low-grade ccRCC and 62 cases of high-grade ccRCC. The patients were randomly divided into training set and verification set according to the proportion of 7:3 by stratified sampling method. The imaging characteristics of ccRCC were calculated in the plain CT images. Lasso regression was used to reduce the dimensionality of the imaging characteristics of the training set, and binary logistic regression was used to construct the prediction model. Bootstrap method was used to verify the training set model and the validation set model, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated respectively.</p><p><strong>Results: </strong>Binary logistic regression showed that only imaging features were independent risk factors for predicting the Furhman classification of ccRCC. The predictive model was y = 1/[1 + exp (-z)], z = 1.274 × imaging risk score + 0.072. The results of bootstrap internal validation showed that the AUC of the training group was 0.961 (95% CI: 0.900-0.913). The Hosmer-Lemeshow goodness of fit test showed that the prediction model had a good calibration in the training group (P = 0.416). The AUC of prediction model in validation group was 0.731 (95% CI: 0.500-1.000). The Hosmer-Lemeshow goodness of fit test results showed that the prediction model had a good calibration in the validation group (P = 0.592).</p><p><strong>Conclusion: </strong>The model based on CT texture analysis has a good predictive effect in differentiating low-grade and high-grade ccRCC and can provide reference for the treatment and prognosis of patients.</p>\",\"PeriodicalId\":7438,\"journal\":{\"name\":\"American journal of clinical and experimental urology\",\"volume\":\"12 1\",\"pages\":\"28-35\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10944366/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American journal of clinical and experimental urology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of clinical and experimental urology","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

目的研究基于CT平扫纹理分析的成像模型在区分肾透明细胞癌Fuhrman病理分级低(I级和II级)和高(III级和IV级)时的预测性能:回顾性分析了 TCGA-KIRC 公共数据库中 94 例接受 CT 扫描并经活检或手术确诊的 ccRCC 患者的临床数据。其中 32 例为低级别 ccRCC,62 例为高级别 ccRCC。通过分层抽样法,按照 7:3 的比例将患者随机分为训练集和验证集。计算普通 CT 图像中 ccRCC 的成像特征。采用拉索回归法降低训练集成像特征的维度,并采用二元逻辑回归法构建预测模型。使用 Bootstrap 方法验证了训练集模型和验证集模型,并分别计算了接收者操作特征曲线(ROC)下面积(AUC):二元逻辑回归结果显示,只有影像学特征是预测ccRCC Furhman分类的独立风险因素。预测模型为 y = 1/[1 + exp (-z)],z = 1.274 × 影像学风险评分 + 0.072。引导内部验证结果显示,训练组的 AUC 为 0.961(95% CI:0.900-0.913)。Hosmer-Lemeshow 拟合优度检验表明,预测模型在训练组具有良好的校准性(P = 0.416)。验证组预测模型的 AUC 为 0.731(95% CI:0.500-1.000)。Hosmer-Lemeshow拟合优度检验结果显示,预测模型在验证组具有良好的校准性(P = 0.592):结论:基于CT纹理分析的模型在区分低级别和高级别ccRCC方面具有良好的预测效果,可为患者的治疗和预后提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Prediction of Fuhrman pathological grade of renal clear cell carcinoma based on CT texture analysis.

Objective: To study the predictive performance of the imaging model based on the texture analysis of CT plain scan in distinguishing between low (grade I and II) and high (grade III and IV) of Fuhrman pathological grade of renal clear cell carcinoma.

Methods: The clinical data of 94 patients with ccRCC who underwent CT scan and were confirmed by biopsy or surgery in TCGA-KIRC public database were retrospectively analyzed. There were 32 cases of low-grade ccRCC and 62 cases of high-grade ccRCC. The patients were randomly divided into training set and verification set according to the proportion of 7:3 by stratified sampling method. The imaging characteristics of ccRCC were calculated in the plain CT images. Lasso regression was used to reduce the dimensionality of the imaging characteristics of the training set, and binary logistic regression was used to construct the prediction model. Bootstrap method was used to verify the training set model and the validation set model, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated respectively.

Results: Binary logistic regression showed that only imaging features were independent risk factors for predicting the Furhman classification of ccRCC. The predictive model was y = 1/[1 + exp (-z)], z = 1.274 × imaging risk score + 0.072. The results of bootstrap internal validation showed that the AUC of the training group was 0.961 (95% CI: 0.900-0.913). The Hosmer-Lemeshow goodness of fit test showed that the prediction model had a good calibration in the training group (P = 0.416). The AUC of prediction model in validation group was 0.731 (95% CI: 0.500-1.000). The Hosmer-Lemeshow goodness of fit test results showed that the prediction model had a good calibration in the validation group (P = 0.592).

Conclusion: The model based on CT texture analysis has a good predictive effect in differentiating low-grade and high-grade ccRCC and can provide reference for the treatment and prognosis of patients.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
8.30%
发文量
0
期刊最新文献
A Mendelian randomisation approach to explore genetic factors associated with erectile dysfunction based on pooled genomic data. Administering antibiotic-loaded irrigation fluid as an alternative for prophylactic intravenous antibiotics in transurethral ureterolithotripsy (TUL): a randomized controlled trial. Decreased expression of LncRNA CRYM-AS1 promotes apoptosis through the Hippo-YAP1 signaling pathway leading to diabetic erectile dysfunction. Minimally invasive management of extraperitoneal bladder injury with extension to the trigone of the bladder with bilateral external ureteral catheterization: innovative approach instead of open surgical treatment. N4-acetyl-sulfamethoxazole stone in a patient on chronic trimethoprim/sulfamethoxazole therapy: a case report and literature review.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1