Jie Gao, Yao Fu, Kuiqiang He, Qinfeng Xu, Feng Wang, Hongqian Guo
{"title":"基于68Ga-PSMA PET/ ct的全载切片预测前列腺癌神经周围浸润模型","authors":"Jie Gao, Yao Fu, Kuiqiang He, Qinfeng Xu, Feng Wang, Hongqian Guo","doi":"10.1007/s11307-024-01974-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To develop a novel risk model incorporating <sup>68</sup>Ga-PSMA PET/CT parameters for prediction of perineural invasion (PNI) of prostate cancer (PCa).</p><p><strong>Methods: </strong>The study retrospectively enrolled 192 PCa patients with preoperative multiparametric MRI, <sup>68</sup>Ga-PSMA PET/CT and radical specimen. Imaging parameters were derived from both mpMRI and PET/CT images. S100 immunohistochemistry staining was conducted to evaluate PNI of PCa. Significant predictors were derived with univariate and multivariate logistic regression analyses, and the PNI-risk nomogram was constructed with significant predictors. Internal discrimination validation was performed with receiver operating characteristic analysis. Calibration curves were plotted, decision curve and clinical impact curve analysis were performed for clinical benefit exploration.</p><p><strong>Results: </strong>With the median peritumoral nerve density of 6, patients were stratified as low-PNI group (nerve density < 6, n = 78, 40.6%) and high-PNI group (nerve density ≥ 6, n = 114, 59.4%). Compared with low-PNI PCa, high-PNI PCa harbored significantly larger imaging lesion diameter (P < 0.001), higher PI-RADS score (P = 0.009), higher SUVmax (P < 0.001), larger tumor diameter (P = 0.024) and higher Gleason grade group (P < 0.001). Further, with univariate and multivariate analyses, imaging lesion diameter (OR 2.98, 95% CI 1.73-5.16, P = 0.004) and SUVmax (OR 3.59, 95%CI 2.32-5.55, P < 0.001) and were identified as independent predictors for PNI in PCa, and a PNI-risk nomogram incorporating these two predictors was constructed. The PNI-risk nomogram demonstrated considerable calibration (mean absolute error 0.026) and discrimination (area under the curve = 0.889, sensitivity 73.1%, specificity 97.4%) abilities, harboring net benefits with threshold probabilities range from 0 to 0.80.</p><p><strong>Conclusion: </strong><sup>68</sup>Ga-PSMA PET/CT-based model could effectively predict the perineural invasion of PCa. These results may help with the decision-making on active surveillance, focal therapy and surgery approach. Additionally, patients suspicious of high-density PNI PCa should receive more radical treatment than low-PNI PCa.</p>","PeriodicalId":18760,"journal":{"name":"Molecular Imaging and Biology","volume":" ","pages":"44-53"},"PeriodicalIF":3.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"<sup>68</sup>Ga-PSMA PET/CT-Based Model Predicts Perineural Invasion of Prostate Cancer with Whole-Mount Sections.\",\"authors\":\"Jie Gao, Yao Fu, Kuiqiang He, Qinfeng Xu, Feng Wang, Hongqian Guo\",\"doi\":\"10.1007/s11307-024-01974-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To develop a novel risk model incorporating <sup>68</sup>Ga-PSMA PET/CT parameters for prediction of perineural invasion (PNI) of prostate cancer (PCa).</p><p><strong>Methods: </strong>The study retrospectively enrolled 192 PCa patients with preoperative multiparametric MRI, <sup>68</sup>Ga-PSMA PET/CT and radical specimen. Imaging parameters were derived from both mpMRI and PET/CT images. S100 immunohistochemistry staining was conducted to evaluate PNI of PCa. Significant predictors were derived with univariate and multivariate logistic regression analyses, and the PNI-risk nomogram was constructed with significant predictors. Internal discrimination validation was performed with receiver operating characteristic analysis. Calibration curves were plotted, decision curve and clinical impact curve analysis were performed for clinical benefit exploration.</p><p><strong>Results: </strong>With the median peritumoral nerve density of 6, patients were stratified as low-PNI group (nerve density < 6, n = 78, 40.6%) and high-PNI group (nerve density ≥ 6, n = 114, 59.4%). Compared with low-PNI PCa, high-PNI PCa harbored significantly larger imaging lesion diameter (P < 0.001), higher PI-RADS score (P = 0.009), higher SUVmax (P < 0.001), larger tumor diameter (P = 0.024) and higher Gleason grade group (P < 0.001). Further, with univariate and multivariate analyses, imaging lesion diameter (OR 2.98, 95% CI 1.73-5.16, P = 0.004) and SUVmax (OR 3.59, 95%CI 2.32-5.55, P < 0.001) and were identified as independent predictors for PNI in PCa, and a PNI-risk nomogram incorporating these two predictors was constructed. The PNI-risk nomogram demonstrated considerable calibration (mean absolute error 0.026) and discrimination (area under the curve = 0.889, sensitivity 73.1%, specificity 97.4%) abilities, harboring net benefits with threshold probabilities range from 0 to 0.80.</p><p><strong>Conclusion: </strong><sup>68</sup>Ga-PSMA PET/CT-based model could effectively predict the perineural invasion of PCa. These results may help with the decision-making on active surveillance, focal therapy and surgery approach. Additionally, patients suspicious of high-density PNI PCa should receive more radical treatment than low-PNI PCa.</p>\",\"PeriodicalId\":18760,\"journal\":{\"name\":\"Molecular Imaging and Biology\",\"volume\":\" \",\"pages\":\"44-53\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Imaging and Biology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11307-024-01974-2\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/16 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Imaging and Biology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11307-024-01974-2","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/16 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
68Ga-PSMA PET/CT-Based Model Predicts Perineural Invasion of Prostate Cancer with Whole-Mount Sections.
Purpose: To develop a novel risk model incorporating 68Ga-PSMA PET/CT parameters for prediction of perineural invasion (PNI) of prostate cancer (PCa).
Methods: The study retrospectively enrolled 192 PCa patients with preoperative multiparametric MRI, 68Ga-PSMA PET/CT and radical specimen. Imaging parameters were derived from both mpMRI and PET/CT images. S100 immunohistochemistry staining was conducted to evaluate PNI of PCa. Significant predictors were derived with univariate and multivariate logistic regression analyses, and the PNI-risk nomogram was constructed with significant predictors. Internal discrimination validation was performed with receiver operating characteristic analysis. Calibration curves were plotted, decision curve and clinical impact curve analysis were performed for clinical benefit exploration.
Results: With the median peritumoral nerve density of 6, patients were stratified as low-PNI group (nerve density < 6, n = 78, 40.6%) and high-PNI group (nerve density ≥ 6, n = 114, 59.4%). Compared with low-PNI PCa, high-PNI PCa harbored significantly larger imaging lesion diameter (P < 0.001), higher PI-RADS score (P = 0.009), higher SUVmax (P < 0.001), larger tumor diameter (P = 0.024) and higher Gleason grade group (P < 0.001). Further, with univariate and multivariate analyses, imaging lesion diameter (OR 2.98, 95% CI 1.73-5.16, P = 0.004) and SUVmax (OR 3.59, 95%CI 2.32-5.55, P < 0.001) and were identified as independent predictors for PNI in PCa, and a PNI-risk nomogram incorporating these two predictors was constructed. The PNI-risk nomogram demonstrated considerable calibration (mean absolute error 0.026) and discrimination (area under the curve = 0.889, sensitivity 73.1%, specificity 97.4%) abilities, harboring net benefits with threshold probabilities range from 0 to 0.80.
Conclusion: 68Ga-PSMA PET/CT-based model could effectively predict the perineural invasion of PCa. These results may help with the decision-making on active surveillance, focal therapy and surgery approach. Additionally, patients suspicious of high-density PNI PCa should receive more radical treatment than low-PNI PCa.
期刊介绍:
Molecular Imaging and Biology (MIB) invites original contributions (research articles, review articles, commentaries, etc.) on the utilization of molecular imaging (i.e., nuclear imaging, optical imaging, autoradiography and pathology, MRI, MPI, ultrasound imaging, radiomics/genomics etc.) to investigate questions related to biology and health. The objective of MIB is to provide a forum to the discovery of molecular mechanisms of disease through the use of imaging techniques. We aim to investigate the biological nature of disease in patients and establish new molecular imaging diagnostic and therapy procedures.
Some areas that are covered are:
Preclinical and clinical imaging of macromolecular targets (e.g., genes, receptors, enzymes) involved in significant biological processes.
The design, characterization, and study of new molecular imaging probes and contrast agents for the functional interrogation of macromolecular targets.
Development and evaluation of imaging systems including instrumentation, image reconstruction algorithms, image analysis, and display.
Development of molecular assay approaches leading to quantification of the biological information obtained in molecular imaging.
Study of in vivo animal models of disease for the development of new molecular diagnostics and therapeutics.
Extension of in vitro and in vivo discoveries using disease models, into well designed clinical research investigations.
Clinical molecular imaging involving clinical investigations, clinical trials and medical management or cost-effectiveness studies.