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Multicenter Study
. 2024 Oct;49(10):3747-3757.
doi: 10.1007/s00261-024-04421-6. Epub 2024 Jun 18.

A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study

Shuying Bian  1 , Weifeng Hong  2 , Xinhui Su  3 , Fei Yao  1 , Yaping Yuan  4 , Yayun Zhang  5 , Jiageng Xie  1 , Tiancheng Li  3 , Kehua Pan  1 , Yingnan Xue  1 , Qiongying Zhang  6 , Zhixian Yu  7 , Kun Tang  5 , Yunjun Yang  5 , Yuandi Zhuang  1 , Jie Lin #  5 , Hui Xu #  8
Affiliations
Multicenter Study

A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study

Shuying Bian et al. Abdom Radiol (NY). 2024 Oct.

Abstract

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.

Keywords: Nomogram; Periprostatic adipose tissue; Persistent prostate-specific antigen; Positron emission tomography/computed tomography; Prostate cancer.

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