Cancer Imaging最新文献

{"title":"O-RADS for assessment of adnexal lesions: current status, challenges and future directions.","authors":"Yang Guo, Lori M Strachowski, Atul B Shinagare, Katherine E Maturen, Maitray D Patel, Elizabeth A Sadowski, Rochelle F Andreotti","doi":"10.1186/s40644-026-01061-5","DOIUrl":"10.1186/s40644-026-01061-5","url":null,"abstract":"<p><p>The Ovarian-Adnexal Reporting and Data System (O-RADS), developed by the American College of Radiology (ACR), provides a standardized, evidence-based framework for risk stratification and management of ovarian and adnexal lesions. The O-RADS system is comprised of two complementary imaging arms: ultrasound (US) and magnetic resonance imaging (MRI). O-RADS US serves as the primary imaging modality for adnexal lesion assessment, given its wide availability in outpatient and inpatient settings, relatively low cost, lack of ionizing radiation and the ability for real-time dynamic assessment. Using defined lexicon terms, lesions are stratified into five risk categories corresponding to an estimated malignancy risk with associated management recommendations. Validation studies have demonstrated high sensitivities for malignancy detection, good specificities, and strong interobserver agreement. The 2022 update refined selected descriptors to improve specificity while otherwise maintaining diagnostic performance and reproducibility, reinforcing the system's clinical utility. O-RADS MRI functions as a secondary, problem-solving tool for lesions that are technically limited or when MRI evaluation may add value. Validation studies have consistently shown excellent performance of O-RADS MRI with studies showing high specificities approaching 90-95% related to better characterization of solid tissue based on signal and dynamic enhancement characteristics. Together, O-RADS US and MRI establish a comprehensive, standardized approach that improves diagnostic confidence and optimizes patient management. Ongoing efforts focus on improving congruence between these two arms by addressing discrepancies, understanding and lowering barriers for adoption, and providing management recommendations for O-RADS MRI. Emerging evidence and evolving technologies, such as contrast-enhanced ultrasound (CEUS), quantitative imaging biomarkers and artificial intelligence (AI), may further advance the system. This review summarizes the background and validation of O-RADS US and O-RADS MRI, discusses their complementary roles, current challenges and barriers to adoption, and outlines future priorities and research directions.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148337985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based radiomics as a non-invasive virtual biopsy for high PD-L1 expression prediction in non-small cell lung cancer.","authors":"Michela Destito, Caterina Battaglia, Paolo Zaffino, Giulio Caridà, Maria Cucè, Alessandro Pullano, Martina Frangipane, Maria Francesca Spadea, Domenico Laganà, Pierfrancesco Tassone, Pierosandro Tagliaferri, Carlo Cosentino","doi":"10.1186/s40644-026-01079-9","DOIUrl":"10.1186/s40644-026-01079-9","url":null,"abstract":"<p><strong>Purpose: </strong>Non-small cell lung cancer (NSCLC) remains a major clinical challenge, with Programmed death-ligand 1 (PD-L1) expression serving as a crucial biomarker to guide immunotherapy. However, its current assessment through invasive biopsies may not capture tumor heterogeneity. This study explores the feasibility of a CT-based radiomics approach, combined with machine learning (ML), as a potential non-invasive virtual biopsy to predict high PD-L1 expression (≥50%) in NSCLC patients.</p><p><strong>Methods: </strong>Contrast-enhanced CT scans from 55 patients with histologically confirmed NSCLC were retrospectively analyzed. Radiomic features were extracted from tumor volumes, and multiple ML classifiers were trained and evaluated through repeated stratified k-fold cross-validation.</p><p><strong>Results: </strong>Among the models evaluated, the Support Vector Machine (SVM) classifier demonstrated the best performance, achieving a median accuracy of 0.77 (quartiles: 0.66-0.82) and an area under the curve (AUC) of 0.83 (0.63-0.92). Feature importance analysis using SHAP (Shapley Additive Explanations) revealed that texture features were the most informative in predicting PD-L1 expression levels. Notably, the integration of clinical data did not improve model performance, highlighting the dominant predictive value of radiomic features alone.</p><p><strong>Conclusion: </strong>Our findings support the feasibility of CT-based radiomics as a potential tool for virtual biopsy to identify NSCLC patients with high PD-L1 expression (≥50%), potentially serving as a complementary or alternative tool to tissue biopsy, especially in cases where biopsy is contraindicated or insufficient.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148337926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic nomogram for predicting axillary lymph node metastasis in breast cancer based on MRI analysis and the platelet-to-lymphocyte ratio (PLR).","authors":"Jie Wang, Qinan Geng, Manman Wang, Mengmeng Yang, Yilan Zhang","doi":"10.1186/s40644-026-01083-z","DOIUrl":"10.1186/s40644-026-01083-z","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to develop a dynamic nomogram in which clinical indicators are integrated with magnetic resonance imaging (MRI) radiomics to predict axillary lymph node metastasis (ALNM) in patients with breast cancer.</p><p><strong>Methods: </strong>Our retrospective study included 221 pathologically confirmed patients with breast cancer. Radiomic features were extracted from dynamic contrast-enhanced MRI (DCE-MRI) and fat-suppressed T2-weighted imaging (FS-T2WI) datasets. After feature screening, a support vector machine (SVM) algorithm was employed to establish radiomic models and calculate radiomic scores. Clinical independent predictors were identified through univariate and multivariate logistic regression analyses. A nomogram was established on the basis of the radiomic scores obtained with the optimal SVM model and clinically independent predictors, and it was subsequently transformed into a dynamic nomogram. Model performance was evaluated by the receiver operating characteristic curve and area under the curve (AUC). Shapley additive explanation (SHAP) was applied to interpret the contribution of clinical predictors. Calibration and decision curves were employed to assess nomogram performance.</p><p><strong>Results: </strong>The platelet-to-lymphocyte ratio (PLR), breast imaging reporting and data system (BI-RADS) classification, and ALN status on MRI were identified as independent predictors of ALNM (all p < 0.05). SHAP analysis identified PLR as the top contributor. The clinical model developed with the three predictors achieved AUCs of 0.845 and 0.706 in the training and validation cohorts, respectively. Among the SVM models, the DCE-MRI and FS-T2WI fusion sequence model outperformed the single-sequence models, with AUCs of 0.868 and 0.875, respectively, in the training and validation cohorts. When clinical predictors were incorporated into the fusion sequence model, the nomogram achieved AUC values of 0.930 and 0.928 in the training and validation cohorts. Decision curve analysis demonstrated that the nomogram has significant clinical value.</p><p><strong>Conclusions: </strong>A nomogram model integrating MRI-derived radiomic scores, BI-RADS classification, ALN status, and PLR demonstrated good performance in predicting ALNM in patients with breast cancer.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13595517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148337989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative prediction of Ki-67 expression and prognosis in breast cancer based on contrast-enhanced cone-beam breast CT: a two-centre study.","authors":"Wenxiu Guo, Binglin Lyu, Yue Ma, Yafei Wang, Keyi Bian, Jiaqi Wu, Yaopan Wu, Jie Jiang, Yueqiang Zhu, Zhaoxiang Ye","doi":"10.1186/s40644-026-01082-0","DOIUrl":"10.1186/s40644-026-01082-0","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to develop and validate preoperative contrast-enhanced cone-beam breast CT (CE-CBBCT)-based radiomics, deep learning, and combined models for predicting Ki-67 expression status and to preliminarily explore their prognostic value, with the goal of informing preoperative treatment decisions.</p><p><strong>Methods: </strong>This two-centre study enrolled 636 breast cancer patients, divided into training (n = 310), internal validation (n = 77), external test (n = 78), and prognostic (n = 171) cohorts. Two Ki-67 cut-offs (14% and 30%) were applied. Radiomics models were constructed using five machine learning classifiers, and deep learning models using four three-dimensional architectures (ResNet50, ResNet101, DenseNet121, and ShuffleNet). The best-performing models from each category were integrated with the clinical model using logistic regression to establish combined models. Prognostic value was evaluated using disease-free survival (DFS) in the prognostic cohort.</p><p><strong>Results: </strong>Among the radiomics models, those based on ExtraTrees and eXtreme Gradient Boosting performed best for the 14% and 30% cut-offs, respectively, with areas under the receiver operating characteristic curves (AUCs) of 0.885 and 0.831 in the external test cohort. Among the deep learning architectures, ShuffleNet performed best, with corresponding AUCs of 0.630 and 0.825. Combined models achieved the highest AUCs of 0.895 and 0.869 for the 14% and 30% cut-offs, respectively, in the external test cohort. However, they did not significantly outperform the corresponding radiomics models. In survival analysis, the radiomics score for predicting Ki-67 ≥ 30% was an independent prognostic factor for DFS (hazard ratio: 1.661; 95% confidence interval: 1.142-2.417; P = 0.008).</p><p><strong>Conclusions: </strong>CE-CBBCT-based radiomics models demonstrated utility as non-invasive tools for the preoperative prediction of high Ki-67 expression defined at both the 14% and 30% cut-offs. The prognostic value of the radiomics score for predicting Ki-67 ≥ 30% was preliminarily demonstrated.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148337917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic value of IVIM-DWI parameters for overall and recurrence-free survival in resected esophageal squamous cell carcinoma.","authors":"Yifan Hu, Jiaye Pan, Qian Xia, Xin Han, Zeyu Tang, Jin Chen, Lin Kong","doi":"10.1186/s40644-026-01077-x","DOIUrl":"10.1186/s40644-026-01077-x","url":null,"abstract":"<p><strong>Purpose: </strong>Prognostic stratification after surgical resection remains challenging in esophageal squamous cell carcinoma (ESCC), as pathological staging cannot quantitatively capture intratumoral heterogeneity. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) enables noninvasive quantification of tumor diffusion and perfusion characteristics. To evaluate the prognostic value of IVIM-derived parameters for predicting overall survival (OS) and recurrence-free survival (RFS) in resected ESCC and to compare their performance with conventional clinicopathological factors.</p><p><strong>Methods: </strong>Eighty patients with pathologically confirmed ESCC who underwent preoperative IVIM-DWI MRI and surgical resection were retrospectively included. IVIM parameters (apparent diffusion coefficient (ADC), the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f)) were extracted using maximum-diameter and whole-volume tumor delineation. Cox regression with stepwise selection was performed to identify prognostic factors. Time-dependent ROC analysis was used to evaluate the predictive performance of clinicopathological, IVIM-based, and combined models, and Kaplan-Meier survival analysis was applied to assess risk stratification based on model-derived risk scores.</p><p><strong>Results: </strong>IVIM-based models showed superior prognostic performance compared with clinicopathological models, particularly for OS. For 1-year OS prediction, the IVIM model (maximum-diameter) achieved an AUC of 0.776 and an accuracy of 0.725, while the combined model further improved performance (AUC 0.827, accuracy 0.812). Similar advantages were observed for RFS, with the combined model yielding a 1-year AUC of 0.825 and accuracy of 0.812, and maintaining the best performance at 3 years for both OS (AUC 0.719, accuracy 0.738) and RFS (AUC 0.850, accuracy 0.825). In addition, the derived risk scores enabled effective risk stratification, with Kaplan-Meier analyses demonstrating significant survival differences between risk groups (log-rank P < 0.001).</p><p><strong>Conclusion: </strong>IVIM-derived parameters provide complementary prognostic information beyond conventional pathological staging in ESCC and may improve postoperative risk stratification when integrated with clinicopathological factors.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13560107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148326922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating machine learning with histopathological, immunohistochemical, and PET parameters in the study of invasive breast carcinoma, no special type.","authors":"Berkay Çağdaş, Hüseyin Şan, Kardelen Çağdaş, Ahmet Kılıçaslan, Nedim C M Gülaldı","doi":"10.1186/s40644-026-01075-z","DOIUrl":"10.1186/s40644-026-01075-z","url":null,"abstract":"<p><strong>Background: </strong>This study investigates the relationship between histopathological (HP) features, immunohistochemical (IHC) markers, ¹⁸F- FDG PET/CT parameters, and machine learning algorithms in patients diagnosed with invasive breast carcinoma, no special type.</p><p><strong>Methods: </strong>384 patients were included in the study. ¹⁸F- FDG PET/CT images after diagnosis of invasive breast carcinoma, before treatment, were analyzed for the PET metabolic parameters retrospectively. PET/CT images of patients who met the inclusion criteria and were retrospectively analysed. Metabolic parameters including SUVmax, SUVmean, SUVpeak, MTV (Metabolic Tumour Volume), and TLG (Total Lesion Glycolysis) were calculated, considering SUVmax at 42% of the threshold value in the primary lesion for the PET/CT imaging. None of the parameters fit into the normal distribution. Therefore, Mann-Whitney-U, Kruskal-Wallis, and Spearman correlation tests were applied. The machine learning algorithms were also used to describe any connection between the HP and IHC properties of the tumors.</p><p><strong>Results: </strong>Significant correlations were observed between PET-derived parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) and HP/IHC characteristics, including Nottingham grade, molecular subtypes, and Ki-67 proliferation index. Machine learning models (XGBoost, Random Forest, LightGBM, CatBoost) demonstrated moderate success in identifying high-risk pathological and phenotypic features, with SUVmean identified as the most influential predictor via SHAP analysis. The coefficient of variation revealed low to moderate variability across most performance metrics, with CV values predominantly between 0.01 and 0.09, indicating stable and reliable model outputs.</p><p><strong>Conclusion: </strong>These findings suggest that integrating 18 F-FDG PET/CT metabolic parameters and machine learning algorithms can enhance non-invasive risk stratification and guide personalized treatment strategies in breast cancer management.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13555851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148326871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A clinical-radiomic model based on best subset regression for prognostic prediction in natural killer/T-cell lymphoma.","authors":"Fangfang Tian, Longlan Chen, Xiaoliang Chen, Hua Pang","doi":"10.1186/s40644-026-01074-0","DOIUrl":"10.1186/s40644-026-01074-0","url":null,"abstract":"<p><strong>Background: </strong>The outcomes of patients with natural killer/T-cell lymphoma (NKTCL) are heterogeneous, thus, this study aimed to develop and validate prognostic models for overall survival (OS) and progression-free survival (PFS) in NKTCL by integrating clinicopathological and pre-treatment [18F]-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT)-derived variables, comparing the performance of univariate COX, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) methods.</p><p><strong>Methods: </strong>This retrospective study analyzed a cohort of 114 pre-treatment patients with NKTCL who have performed [18F]-FDG PET/CT scan. Predictors included clinical parameters (e.g., age, albumin (Alb)), pathological indices (e.g., Ki67), Epstein-Barr virus (EBV), and imaging metrics (e.g., radiomics score (Radscore), maximum standardized uptake value (SUV_max), lumbar-to-liver SUV_mean ratio (LLR), and spleen-to-liver SUV_mean ratio (SLR)). Variable selection was performed using univariate COX (P < 0.1), LASSO, and BSR techniques. The resulting models were compared using the area under the curve (AUC) and concordance index (C-index), with internal validation via bootstrapping.</p><p><strong>Results: </strong>The BSR-derived model demonstrated superior performance for predicting OS (3-year AUC: 0.883; C-index: 0.807). For OS, the optimal multivariate model identified Alb (hazard ratios (HR): 0.226, 95% confidence interval (CI): 0.073-0.697, P = 0.00961), EBV (HR: 3.826, 95% CI: 1.259-11.630, P = 0.017986), SUV_max (HR: 2.466, 95% CI: 1.123-5.416, P = 0.024517), LLR (HR: 0.195, 95% CI: 0.077-0.493, P = 0.000549), and Radscore (HR: 15.463, 95% CI: 5.313-45.001, P = 5.05e-07) as independent prognostic factors. For PFS, the optimal model comprised age (HR: 2.292, 95% CI: 1.076-4.884, P = 0.03166), EBV (HR: 4.168, 95% CI: 1.588-10.936, P = 0.00373), and Ki67 (HR: 2.608, 95% CI: 1.221-5.572, P = 0.01334).</p><p><strong>Conclusion: </strong>Comparative analysis revealed that BSR outperformed both univariate COX regression and LASSO in selecting variables for NKTCL prognosis modeling. Using BSR, we developed a robust model integrating essential clinical factors and PET/CT-derived radiomic features (e.g., Alb, EBV, SUVmax, LLR and Radscore for OS and age, EBV and Ki67 for PFS), offering a valuable tool for risk stratification and guiding individualized treatment strategies.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13560112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148326874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The feasibility of [¹⁸F]PSMA-1007 PET/CT for response assessment in prostate cancer following neoadjuvant androgen deprivation therapy compared with mpMRI.","authors":"Sangwoo Cho, Hyo Jung Seo, Hyunkyung Han, Sangwon Lee, Tae Joo Jeon, Young Taik Oh, Minsun Jung, Mijin Yun","doi":"10.1186/s40644-026-01073-1","DOIUrl":"10.1186/s40644-026-01073-1","url":null,"abstract":"<p><strong>Background: </strong>We investigated the utility of ¹⁸F-labeled prostate-specific membrane antigen positron emission tomography/computed tomography ([¹⁸F]PSMA-1007 PET/CT) for assessing residual tumor burden after neoadjuvant androgen deprivation therapy (nADT), compared with multiparametric magnetic resonance imaging (mpMRI).</p><p><strong>Methods: </strong>This retrospective study included 20 patients with prostate cancer treated with nADT followed by radical prostatectomy. All patients underwent baseline mpMRI, post-nADT mpMRI, and [¹⁸F]PSMA-1007 PET/CT. Lesion-based diagnostic performance of [¹⁸F]PSMA-1007 PET/CT and mpMRI was compared using generalized estimating equations. Apparent diffusion coefficient (ADC) and transfer constant (Ktrans) values were compared between baseline and post-nADT using Wilcoxon signed-rank tests and between tumor and benign prostatic hyperplasia (BPH) using Mann-Whitney U tests.</p><p><strong>Results: </strong>Baseline mpMRI identified 23 biopsy-confirmed lesions, and post-nADT, 21 showed residual tumor on pathology. [¹⁸F]PSMA-1007 PET/CT demonstrated significantly higher sensitivity (90.5% vs. 23.8%, p < 0.001) and accuracy (87.0% vs. 26.1%, p < 0.001) than mpMRI. Median ADC increased significantly after nADT (0.849 to 1.174 × 10^- 3 mm²/s, p = 0.005), while median Ktrans decreased (0.277 to 0.080 min^- 1, p = 0.005). Baseline ADC and Ktrans differed significantly between tumor and BPH (p = 0.001 and p = 0.012, respectively), but no significant post-nADT differences were observed (p = 0.831 and p = 0.701, respectively). Post-nADT SUVmax also did not differ between tumor and BPH (p = 0.639).</p><p><strong>Conclusions: </strong>[¹⁸F]PSMA-1007 PET showed higher sensitivity and accuracy than mpMRI in detecting residual tumor after short-term nADT and may be useful for further management planning. These preliminary findings support the feasibility of [¹⁸F]PSMA-1007 PET for post-treatment assessment.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148306976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI features outperform histologic grade for outcome prediction in translocation-driven enriched soft-tissue sarcoma.","authors":"Han-Ying Lin, Ching-Lan Wu, Chih-Hsueh Chen, Chueh-Chuan Yen, Chao-Ming Chen, Chien-An Liu, Po-Kuei Wu, Hung-Ta Wu, Chung-Jung Lin","doi":"10.1186/s40644-026-01080-2","DOIUrl":"10.1186/s40644-026-01080-2","url":null,"abstract":"<p><strong>Background: </strong>Most MR-based prognostic series in soft-tissue sarcoma (STS) were derived in cohorts dominated by complex-karyotype histologies; whether conventional MR descriptors retain prognostic value in translocation-driven enriched populations is unclear. The purpose of our study is to identify preoperative MR features independently associated with overall survival (OS), local recurrence-free survival (LRFS), and metastasis-free survival (MFS) in a translocation-driven enriched soft-tissue sarcoma cohort.</p><p><strong>Methods: </strong>In this retrospective single-center study, consecutive adults with histologically confirmed STS who underwent contrast-enhanced MRI before treatment between January 2008 and December 2024 were evaluated. Two radiologists blinded to outcome independently scored 13 prespecified MR features. Cox regression adjusted for age, sex, maximum tumor diameter, and Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) grade was used to estimate hazard ratios (HRs); a sensitivity analysis added translocation-driven histology.</p><p><strong>Results: </strong>A total of 158 patients (mean age, 48 years ± 19 [SD]; 84 men) were evaluated; 61 of 152 classifiable patients (40%) had translocation-driven tumors. After adjustment for age, sex, and maximum tumor diameter, heterogeneous gadolinium enhancement was independently associated with shorter OS (HR, 3.9 [95% CI: 1.4, 10.7]; P = .008). Peritumoral edema was independently associated with shorter OS (HR, 2.0 [95% CI: 1.0, 3.7]; P = .04) and was the single independent MR predictor of LRFS (HR, 2.3 [95% CI: 1.2, 4.5]; P = .01). Both associations persisted after adjustment for translocation-driven histology. A 13-feature MR panel did not discriminate FNCLCC grade 3 (area under the curve [AUC], 0.59).</p><p><strong>Conclusion: </strong>Peritumoral edema and heterogeneous gadolinium enhancement were independent, complementary MR predictors of outcome in translocation-driven enriched soft-tissue sarcoma, outperforming FNCLCC grade and molecular histologic class. These findings require prospective multicenter validation before clinical application.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148301040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospective exploratory pilot study of 18 F-FMISO PET/CT for assessing tumor hypoxia in soft tissue sarcomas.","authors":"Ronnie Sebro, Ashley M Fuller, T S Karin Eisenger-Matheson, Christina Dulal, Austin R Pantel, Jacob Shabason, William Levin, Kumarasen Cooper, Paul Zhang, Daniel A Pryma, Michael D Farwell, Jacob G Dubroff, David A Mankoff","doi":"10.1186/s40644-026-01069-x","DOIUrl":"10.1186/s40644-026-01069-x","url":null,"abstract":"<p><p>Hypoxia is associated with poor outcomes in soft tissue sarcoma (STS) and is linked to increased expression of hypoxia-inducible factor 1-alpha (HIF-1α), carbonic anhydrase IX (CAIX), and lysyl hydroxylase 2 (PLOD2). These factors are potential therapeutic targets. This exploratory study aimed to investigate the correlation between ¹⁸F-fluoromisonidazole (¹⁸F-FMISO) positron emission tomography (PET) imaging, a marker of hypoxia, and the expression of these biomarkers in STS. This imaging phase II clinical trial (NCT #03730077) recruited 5 patients with suspected soft tissue sarcoma (STS) and imaged these patients with ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) and ¹⁸F-FMISO within 2 weeks of each other. STS tissue was analyzed for HIF-1α, CAIX, and PLOD2 expression using immunohistochemistry and western blotting. Collagen formation was assessed by Masson's trichrome. In this small sample, a correlation was found between STS size and PLOD2 expression (r = 0.996, P = 0.004). STS grade correlated positively with ¹⁸F-FMISO T/M (tumor/muscle) SUVmax (r = 0.894, P = 0.041). ¹⁸F-FDG correlated positively with ¹⁸F-FMISO SUVmax (r = 0.994, P = 0.006). Positive but non-significant associations were noted between ¹⁸F-FMISO T/M SUVmax and CAIX (r = 0.95, P > 0.05) and PLOD2 (r = 0.93, P > 0.05). These preliminary observations in a small sample are hypothesis-generating at best and must be interpreted with substantial caution due to the extremely limited number of patients, which precludes robust statistical inference and generalizability. No definitive conclusions can be drawn regarding the relationship between 18F-FMISO uptake and hypoxia biomarker expression. Larger, adequately powered prospective studies are essential to validate these findings, clarify potential correlations, and determine the clinical utility of 18F-FMISO PET/CT as a noninvasive hypoxia biomarker in STS.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148283111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}