Frontiers in Oncology最新文献

{"title":"Development and validation of a prostate cancer risk prediction model for the elevated PSA population.","authors":"Junhui Wu, Xiaodong Jin, Jiali Li, Lingqian Zhao, Chenkai Zhao, Nengfeng Yu, Yubing Li, Jiasheng Yan, Junlong Wang, Fei Yang, Wenhao Zhang","doi":"10.3389/fonc.2025.1599266","DOIUrl":"10.3389/fonc.2025.1599266","url":null,"abstract":"<p><strong>Introduction: </strong>To develop and validate a dynamic clinical prediction model integrating prostate-specific antigen (PSA) and peripheral blood biomarkers for distinguishing benign from malignant prostate diseases in patients with elevated PSA levels.</p><p><strong>Methods: </strong>A retrospective study was conducted of clinicopathological data and preoperative blood specimen information of patients who underwent ultrasound-guided prostate biopsy in The First Affiliated Hospital of Zhejiang Chinese Medical University due to elevated PSA between January 2018 and November 2024.Univariate analysis, Least Absolute Shrinkage and Selection Operator regression, and multifactorial logistic regression analysis were utilized to identify independent risk factors associated with benign or malignant prostate disease in patients with elevated PSA (PSA > 4.0ng/ml). The construction of a clinical prediction model was then undertaken, with the subsequent calibration and integration into a network calculator.</p><p><strong>Results: </strong>A total of 529 patients were included based on predefined inclusion and exclusion criteria, comprising 268 (50.7%) with benign pathology and 261 (49.3%) with malignancy. After analysis, independent risk factors associated with benign or malignant prostatic diseases in patients with elevated PSA levels were identified, including PSA, white blood cell, neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, eosinophil count, basophil count, and serum albumin. Utilizing these independent risk factors, a clinical prediction model for the risk of PSA-elevated prostate benign-malignant disease was constructed, yielding an area under the curve of 0.906, a predictive model specificity of 77.6%, and a sensitivity of 95%. The calibration curve and clinical decision curve indicated that the model exhibited superior calibration ability. A dynamic prediction model was formulated based on the clinical prediction model integrated into a network calculator.</p><p><strong>Conclusion: </strong>This study establishes a non-invasive prediction model integrating PSA and peripheral blood biomarkers, providing a clinically practical tool for risk stratification in patients with elevated PSA levels.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1599266"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case Report: Apatinib combined with IMRT concurrent therapy achieves rapid response and significantly prolongs progression-free survival in elderly patients with inoperable locally advanced esophageal squamous cell carcinoma.","authors":"Juan Liu, Wei Li, Ning Zan, Tingwu Yi, Cheng Li, Shi Liang","doi":"10.3389/fonc.2025.1655976","DOIUrl":"10.3389/fonc.2025.1655976","url":null,"abstract":"<p><p>Patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC) typically have a poor prognosis, and concurrent chemoradiotherapy is the primary treatment modality. However, elderly patients often exhibit lower completion rates of concurrent chemoradiotherapy due to multiple comorbidities and reduced treatment tolerance, which directly affects their prognosis. Although apatinib combined with radiotherapy has demonstrated synergistic potential in treating certain solid tumors, its efficacy in elderly patients with LA-ESCC remains unclear. This case report involves an elderly patient with unresectable LA-ESCC who was treated with apatinib combined with intensity-modulated radiation therapy, The patient had comorbidities, including hypertension and diabetes, and declined chemotherapy. We implemented a treatment regimen consisting of intensity-modulated radiation therapy combined with low-dose apatinib. After nine sessions of radiotherapy, the patient's dysphagia significantly improved, and follow-up computed tomography revealed marked tumor shrinkage. As of July 2024, the patient has achieved a progression-free survival of 71 months without experiencing severe (Grade III/IV) adverse reactions. Therefore, apatinib combined with radiotherapy suggests potential benefits as a concurrent treatment option for elderly patients with unresectable LA-ESCC.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1655976"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing breast cancer relapse prediction with radiomics and neural networks: a clinically interpretable framework.","authors":"Adnan Khalid, Muhammad Mursil, Carlos López Pablo, Ramon Bosch, Domenec Puig, Hatem A Rashwan","doi":"10.3389/fonc.2025.1593806","DOIUrl":"10.3389/fonc.2025.1593806","url":null,"abstract":"<p><p>Early assessment of breast cancer relapse can significantly impact survival rates and overall oncological outcomes, highlighting the need to use sophisticated diagnostic strategies in clinical trials. This work utilizes clinically relevant radiomic features extracted from digital mammograms to develop a deep learning-based model for forecasting breast cancer relapse. Features, including tumor size, shape, margin characteristics, molecular subtype, and breast density, were systematically extracted from our private, in-house dataset, providing a comprehensive representation of intrinsic tumor properties and assisting in relapse prediction. The predictive model demonstrated outstanding performance with an average area under the curve (AUC) of 0.957, highlighting its effectiveness in identifying possible relapse. This approach not only underscores the abilities of radiomics in enhancing the granularity of tumor assessment but also assists in identifying cancer recurrence during the treatment stage, promising significant strides toward personalized cancer therapy.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1593806"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Histologic grade and STAS as key predictors of distant recurrence in resected early-stage lung adenocarcinoma: a single-center study.","authors":"Alessandro Bonis, Giulia Pagliarini, Giovanni Maria Comacchio, Marco Mammana, Federica Pezzuto, Vincenzo Verzeletti, Enrica Pellizzer, Alessandro Berni, Stefano Silvestrin, Giorgio Cannone, Eleonora Faccioli, Alessandro Rebusso, Marco Schiavon, Samuele Nicotra, Andrea Dell'Amore, Fiorella Calabrese, Federico Rea","doi":"10.3389/fonc.2025.1626863","DOIUrl":"10.3389/fonc.2025.1626863","url":null,"abstract":"<p><strong>Introduction: </strong>Early-stage lung adenocarcinoma (ADC) is curable by surgical resection in most cases. However, unexpectedly, some patients experience distant disease relapse. Emerging evidence suggests that microscopic tumor characteristics may increase the risk of tumor relapse. Consequently, we aimed to test different microscopic variables to assess their association with distant recurrence (DR).</p><p><strong>Materials and methods: </strong>We retrieved all cases of radically treated stage I-IIA ADCs from 2016 to 2020. Clinical and pathological variables were assessed for their association with DR using univariable and multivariable logistic regression. An EGFR-adjusted model was also provided.</p><p><strong>Results: </strong>A total of 259 patients were treated (214 lobectomies and 45 segmentectomies). After resection, 54 patients relapsed, 28 of whom had distant recurrences (DR). Spread through air spaces (STAS) was detected in 48% of samples, while vascular invasion (VI) was present in 53%, occurring 17% more frequently in those with DR. Tumor size was larger in patients with recurrence, with the largest tumors observed in those with local recurrence (25.5 mm in local vs. 23.5 mm in DR; p=0.028). Dedifferentiated (G3) ADCs were more prevalent in DR cases, accounting for 48% of samples. In univariate regression, surgical margins, LVI, necrosis, G3 primary tumors, and STAS were significant factors. In multivariate analysis, STAS showed a trend towards significance (p=0.07) while G3 remained decisive (p<0.01). The EGFR-adjusted model for DR yielded slightly better results (p=0.05 and p<0.01 respectively).</p><p><strong>Conclusions: </strong>Dedifferentiation and partially STAS are key pathological predictor of distant recurrence in resected stage I-IIA ADCs. The contribution of LVI and tumor necrosis in DR needs to be further clarified. Tumor aggressiveness goes beyond the simple size measurement, claiming for a reassessment of risk models for recurrence after surgery.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1626863"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy and safety of the S-1, nab-paclitaxel, and gemcitabine triplet regimen in patients with resected pancreatic ductal adenocarcinoma.","authors":"Donghui Ran, Cheng Geng, Zhongming Cha, Xiaohan Nie, Abudouwaili Atigu, Chuankui Zhao, Xinjian Xu","doi":"10.3389/fonc.2025.1622215","DOIUrl":"10.3389/fonc.2025.1622215","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the safety, feasibility, and efficacy of an S-1-based triplet regimen (with nab-paclitaxel and gemcitabine) as adjuvant therapy following curative resection for pancreatic ductal adenocarcinoma (PDAC).</p><p><strong>Methods: </strong>We retrospectively analyzed 3-year postoperative clinical data from 92 patients with PDAC who underwent curative resection between March 2020 and March 2022. Participants were allocated to either a control group (n = 40) receiving nab-paclitaxel plus gemcitabine (nab-P/GEM) or an experimental group (n = 52) receiving S-1 plus nab-paclitaxel plus gemcitabine. We compared overall survival (OS), disease-free survival (DFS), and adverse event (AE) incidence between groups.</p><p><strong>Results: </strong>The experimental group showed significantly longer median OS (28.9 vs. 20.9 months; HR 0.62, 95% CI 0.38-0.99; P = 0.049 by log-rank test) and DFS (19.5 vs. 13.6 months; HR 0.59, 95% CI 0.36-0.97; P = 0.036) compared with controls. The incidence of grade ≥3 AEs was significantly lower in the experimental group, including leukopenia (13.5% vs. 47.5%; P < 0.001) and neutropenia (15.4% vs. 70.0%; P < 0.001). Fewer patients in the experimental group required treatment discontinuation (1.9% vs. 12.5%) or dose modifications (13.5% vs. 65.0%).</p><p><strong>Conclusion: </strong>The S-1/nab-paclitaxel/gemcitabine triplet regimen appears to improve survival outcomes while demonstrating potentially favorable tolerability as adjuvant therapy for resected PDAC.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1622215"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12484532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145212040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors affecting response and resistance to venetoclax in acute myeloid leukemia.","authors":"Michael D Diamantidis","doi":"10.3389/fonc.2025.1577908","DOIUrl":"10.3389/fonc.2025.1577908","url":null,"abstract":"<p><p>The use of the BCL2 inhibitor venetoclax in combination with hypomethylating agents (HMA) is a revolution for the treatment of frail and elderly acute myeloid leukemia (AML) patients. This effective treatment strategy is increasingly more and more applicable for other subsets of AML patients and is currently being tested in numerous clinical trials in combination with other drugs in all treatment lines. In particular, venetoclax combinations can also serve as a definitive therapy or as an effective bridge to allogeneic hematopoietic stem cell transplantation (HSCT). However, the factors affecting response to venetoclax in the abovementioned AML patients are not completely clear and understood until today. The aim of this review is to describe the molecular and clinical patterns of response and durable remission of venetoclax-based combinations in AML patients. Hence, mutations in IDH1, IDH2, ASXL1, NPM1, DDX41, chromatin-cohesin complex and splicing-factor genes predict superior response to venetoclax, while inferior response to the drug has been observed for FLT3-ITD, KRAS, NRAS and TP53 gene mutations. Intriguingly, the achievement of measurable residual disease (MRD) negativity in the first four cycles of venetoclax administration characterizes a subgroup of NPM1-mutated AML patients with a more favorable outcome. Even though focus will be given on factors influencing response to the drug in this review, the main mechanisms of resistance to venetoclax in AML patients will also be discussed.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1577908"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification.","authors":"Shuping Wang, Min Li","doi":"10.3389/fonc.2025.1596718","DOIUrl":"10.3389/fonc.2025.1596718","url":null,"abstract":"<p><strong>Introduction: </strong>Accurate classification of brain tumors from MRI scans is a critical task for improving patient outcomes. Machine learning (ML) and deep learning (DL) methods have shown promise in this domain, but their relative performance remains unclear.</p><p><strong>Methods: </strong>This study evaluates several ML and DL techniques using the BraTS 2024 dataset. The models assessed include traditional algorithms such as Random Forest and advanced deep learning architectures including Simple CNN, VGG16, VGG19, ResNet50, Inception-ResNetV2, and EfficientNet. Preprocessing strategies were applied to optimize model performance.</p><p><strong>Results: </strong>The Random Forest classifier achieved the highest accuracy of 87%, outperforming all deep learning models, which achieved accuracy in the range of 47% to 70%. This indicates that traditional ML approaches can sometimes surpass state-of-the-art DL methods in tumor classification tasks.</p><p><strong>Discussion: </strong>The findings highlight the importance of model selection and parameter tuning in automated brain tumor diagnosis. While deep learning models are generally considered standard for image analysis, Random Forest demonstrated superior performance in this context. This underscores the need for fine-grained consideration of dataset characteristics, computational resources, and diagnostic requirements.</p><p><strong>Conclusion: </strong>The study shows that carefully selected and optimized ML approaches can improve tumor classification and support more accurate and efficient diagnostic systems for brain tumor patients.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1596718"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The potential of antibody-drug conjugates in immunotherapy for non-small cell lung cancer: current progress and future.","authors":"Hongyu Lin, Xinyu Ma, Xinhai Zhu, Linru Zhong","doi":"10.3389/fonc.2025.1630056","DOIUrl":"10.3389/fonc.2025.1630056","url":null,"abstract":"<p><p>Antibody-drug conjugates (ADCs) have gained significant attention as a promising therapeutic strategy for non-small cell lung cancer (NSCLC), combining the precision of monoclonal antibodies with the potent cytotoxic effects of chemotherapy. This review summarizes recent advancements in the development of ADCs for NSCLC, focusing on their mechanism of action, key components, and progress in clinical applications. By specifically targeting tumor-associated antigens, ADCs deliver cytotoxic agents directly to cancer cells, thereby enhancing therapeutic efficacy while minimizing systemic toxicity. Several ADCs, such as trastuzumab deruxtecan and sacituzumab govitecan, have shown encouraging results in clinical trials, particularly in tumors with molecular alterations like HER2 and TROP2. Additionally, the combination of ADCs with immune checkpoint inhibitors (ICIs) offers a novel and promising therapeutic avenue, potentially enhancing immune responses and overcoming tumor resistance. Despite these promising outcomes, challenges such as drug resistance, immune evasion, and toxicity persist. The novelty and focus of this article are to discuss the significance of optimizing ADCs design, exploring combination therapies, and enhancing safety management in improving treatment outcomes, with the aim of promoting the research and application of ADCs in the immunotherapy of NSCLC.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1630056"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A synthetic cohort analysis of postoperative management of primary cardiac angiosarcoma and a case report.","authors":"Ying Cai, Hang Yang, Dan Yuan, Su-Han Jin, Junzhu Xu, Wei Hu, Yuju Bai, Xinjuan Li, Zejin Wang, Dengshen Zhang, Ke Guo, Shixiang Wang, Udo S Gaipl, Yuan Liu, Hu Ma, Jian-Guo Zhou","doi":"10.3389/fonc.2025.1625049","DOIUrl":"10.3389/fonc.2025.1625049","url":null,"abstract":"<p><strong>Background: </strong>Primary cardiac angiosarcoma is a rare and aggressive malignancy originating from the endothelial lining of cardiac blood vessels. The prognosis remains extremely poor. The study was to evaluate postoperative survival in patients with primary cardiac angiosarcoma after treated with adjuvant therapy.</p><p><strong>Methods: </strong>A systematic review of PubMed from January 1985 to December 2023 was performed to establish a synthetic cohort of patients undergoing surgery for primary cardiac angiosarcoma. Survival analysis was used to assess the relationship between postoperative adjuvant therapy and prognosis. Univariable and multivariable cox regression analyses were used to identify prognostic factors. We then established and validated a nomogram by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Moreover, we present a case of 49-year-old patient with primary cardiac angiosarcoma.</p><p><strong>Results: </strong>In the synthetic cohort, the patients with postoperative adjuvant therapy reached longer overall survival (OS) and progression-free survival (PFS) than those without postoperative adjuvant therapy (median OS: 14 VS 8 months, HR = 5.62, 95%CI: 1.66-19.08, P<0.001; median PFS: 12 VS 6 months, HR = 2.98, 95%CI: 1.03-8.66, P = 0.007; Log rank test). Radiotherapy (HR = 0.14, 95% CI: 0.04-0.54, P = 0.004) and chemotherapy (HR = 0.03, 95% CI: 0.00-0.27, P = 0.002) were significantly correlated with better OS. DCA and ROC curves confirmed the nomogram can predict postoperative 6-month survival in patients with primary cardiac angiosarcoma. OS was indistinguishable between patients with R0 or R1 resection (10 VS 10 months, HR = 0.99; 95%CI: 0.34-2.86; P = 0.986). However, compared to patients underwent R1 resection, patients undergoing R0 resection have longer but not statistically significant PFS (10 VS 7 months, HR = 2.16; 95%CI: 0.83-5.61; P = 0.090).</p><p><strong>Conclusion: </strong>The prognosis of patients with primary cardiac angiosarcoma remains extremely poor, even with surgical resection. Postoperative adjuvant therapy was associated with significantly better survival in a small cohort of patients with primary cardiac angiosarcoma. Further studies are warranted to guide future recommendations.</p><p><strong>Systematic review registration: </strong>https://www.crd.york.ac.uk/prospero/, identifier CRD420251139779.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1625049"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The predictive value of PSMA PET/CT in determining pathological upgrading of prostate cancer: A pooling up analysis.","authors":"Yue Liu, Shu-Pei Qu, Ling-Yun Zhai","doi":"10.3389/fonc.2025.1525890","DOIUrl":"10.3389/fonc.2025.1525890","url":null,"abstract":"<p><strong>Purpose: </strong>The issue of pathological upgrading following radical prostatectomy poses a significant challenge for urologists, and prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography(PET/CT) has gained increasing prominence as a preoperative assessment tool for patients with prostate cancer in recent years. This study aims to assess the diagnostic accuracy of PSMA PET/CT in predicting pathological upgrading after radical prostatectomy.</p><p><strong>Methods: </strong>We conducted a meta-analysis of diagnostic studies using data from the Cochrane CENTRAL, PubMed, Embase, Scopus, and Web of Science databases through March 2024. We strictly adhered to the guidelines outlined in the PRISMA statement for conducting this diagnostic meta-analysis. We computed the pooled diagnostic accuracy and evaluated heterogeneity while exploring potential sources of heterogeneity through subgroup analysis.</p><p><strong>Results: </strong>A total of 7 studies involving 507 patients were included in the final analysis. All participants had biopsy-confirmed prostate cancer and underwent radical prostatectomy. Prior to surgery, all patients underwent PSMA PET/CT imaging. The pooled diagnostic accuracy yielded a sensitivity of 0.68 (95% CI, 0.60 - 0.76) and specificity of 0.74 (95% CI, 0.59 - 0.85). The area under the summary receiver operating characteristic curve was calculated as 0.74 (95%CI, 0.70 - 0.78). Although heterogeneity was observed, its source remained unclear.</p><p><strong>Conclusion: </strong>The PSMA PET/CT demonstrates a moderate level of accuracy in predicting pathological upgrading following radical prostatectomy, making it a tool with potential clinical application value, particularly in the field of radiotherapy. However, further studies are warranted to enhance its relevance and applicability.</p><p><strong>Systematic review registration: </strong>https://www.crd.york.ac.uk/prospero/, identifier CRD42024503406.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"15 ","pages":"1525890"},"PeriodicalIF":3.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}