{"title":"基于前列腺周围脂肪组织 18F-PSMA-1007 PET/CT 预测前列腺癌短期预后的动态在线提名图：一项多中心研究。","authors":"Shuying Bian, Weifeng Hong, Xinhui Su, Fei Yao, Yaping Yuan, Yayun Zhang, Jiageng Xie, Tiancheng Li, Kehua Pan, Yingnan Xue, Qiongying Zhang, Zhixian Yu, Kun Tang, Yunjun Yang, Yuandi Zhuang, Jie Lin, Hui Xu","doi":"10.1007/s00261-024-04421-6","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on <sup>18</sup>F-PSMA-1007 PET/CT of PPAT.</p><h3>Methods</h3><p>Data from 268 prostate cancer (PCa) patients who underwent <sup>18</sup>F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.</p><h3>Results</h3><p>The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78—0.91), 0.77 (95% CI: 0.62—0.91) and 0.84 (95% CI: 0.70—0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.</p><h3>Conclusion</h3><p>The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study\",\"authors\":\"Shuying Bian, Weifeng Hong, Xinhui Su, Fei Yao, Yaping Yuan, Yayun Zhang, Jiageng Xie, Tiancheng Li, Kehua Pan, Yingnan Xue, Qiongying Zhang, Zhixian Yu, Kun Tang, Yunjun Yang, Yuandi Zhuang, Jie Lin, Hui Xu\",\"doi\":\"10.1007/s00261-024-04421-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on <sup>18</sup>F-PSMA-1007 PET/CT of PPAT.</p><h3>Methods</h3><p>Data from 268 prostate cancer (PCa) patients who underwent <sup>18</sup>F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.</p><h3>Results</h3><p>The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78—0.91), 0.77 (95% CI: 0.62—0.91) and 0.84 (95% CI: 0.70—0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.</p><h3>Conclusion</h3><p>The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.</p></div>\",\"PeriodicalId\":7126,\"journal\":{\"name\":\"Abdominal Radiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Abdominal Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00261-024-04421-6\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Abdominal Radiology","FirstCategoryId":"3","ListUrlMain":"https://link.springer.com/article/10.1007/s00261-024-04421-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study
Background
Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT.
Methods
Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA.
Results
The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78—0.91), 0.77 (95% CI: 0.62—0.91) and 0.84 (95% CI: 0.70—0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram.
Conclusion
The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.
期刊介绍：
Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section.
Reasons to Publish Your Article in Abdominal Radiology:
· Official journal of the Society of Abdominal Radiology (SAR)
· Published in Cooperation with:
European Society of Gastrointestinal and Abdominal Radiology (ESGAR)
European Society of Urogenital Radiology (ESUR)
Asian Society of Abdominal Radiology (ASAR)
· Efficient handling and Expeditious review
· Author feedback is provided in a mentoring style
· Global readership
· Readers can earn CME credits
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