Cancer Imaging最新文献

{"title":"Development of preoperative nomograms to predict the risk of overall and multifocal positive surgical margin after radical prostatectomy.","authors":"Lili Xu, Qianyu Peng, Gumuyang Zhang, Daming Zhang, Jiahui Zhang, Xiaoxiao Zhang, Xin Bai, Li Chen, Erjia Guo, Yu Xiao, Zhengyu Jin, Hao Sun","doi":"10.1186/s40644-024-00749-w","DOIUrl":"10.1186/s40644-024-00749-w","url":null,"abstract":"<p><strong>Objective: </strong>To develop preoperative nomograms using risk factors based on clinicopathological and MRI for predicting the risk of positive surgical margin (PSM) after radical prostatectomy (RP).</p><p><strong>Patients and methods: </strong>This study retrospectively enrolled patients who underwent prostate MRI before RP at our center between January 2015 and November 2022. Preoperative clinicopathological factors and MRI-based features were recorded for analysis. The presence of PSM (overall PSM [oPSM]) at pathology and the multifocality of PSM (mPSM) were evaluated. LASSO regression was employed for variable selection. For the final model construction, logistic regression was applied combined with the bootstrap method for internal verification. The risk probability of individual patients was visualized using a nomogram.</p><p><strong>Results: </strong>In all, 259 patients were included in this study, and 76 (29.3%) patients had PSM, including 40 patients with mPSM. Final multivariate logistic regression revealed that the independent risk factors for oPSM were tumor diameter, frank extraprostatic extension, and annual surgery volume (all p < 0.05), and the nomogram for oPSM reached an area under the curve (AUC) of 0.717 in development and 0.716 in internal verification. The independent risk factors for mPSM included the percentage of positive cores, tumor diameter, apex depth, and annual surgery volume (all p < 0.05), and the AUC of the nomogram for mPSM was 0.790 in both development and internal verification. The calibration curve analysis showed that these nomograms were well-calibrated for both oPSM and mPSM.</p><p><strong>Conclusions: </strong>The proposed nomograms showed good performance and were feasible in predicting oPSM and mPSM, which might facilitate more individualized management of prostate cancer patients who are candidates for surgery.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"104"},"PeriodicalIF":3.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11312749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141906012","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":"Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study.","authors":"Ben Li, Jie Zhu, Yanmei Wang, Yuchao Xu, Zhaisong Gao, Hailei Shi, Pei Nie, Ju Zhang, Yuan Zhuang, Zhenguang Wang, Guangjie Yang","doi":"10.1186/s40644-024-00744-1","DOIUrl":"10.1186/s40644-024-00744-1","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC).</p><p><strong>Methods: </strong>Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index).</p><p><strong>Results: </strong>Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients.</p><p><strong>Conclusions: </strong>The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"103"},"PeriodicalIF":3.5,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11302839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141896853","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 and MRI features of sarcomatoid urothelial carcinoma of the bladder and its differential diagnosis with conventional urothelial carcinoma.","authors":"Jiayi Zhuo, Jingjing Han, Lingjie Yang, Yu Wang, Guangzi Shi, Zhuoheng Yan, Lu Yang, Riyu Han, Fengqiong Huang, Xiaohua Ban, Xiaohui Duan","doi":"10.1186/s40644-024-00748-x","DOIUrl":"10.1186/s40644-024-00748-x","url":null,"abstract":"<p><strong>Background: </strong>Sarcomatoid urothelial carcinoma (SUC) is a rare and highly malignant form of bladder cancer with a poor prognosis. Currently, there is limited information on the imaging features of bladder SUC and reliable indicators for distinguishing it from conventional urothelial carcinoma (CUC). The objective of our study was to identify the unique imaging characteristics of bladder SUC and determine factors that aid in its differential diagnosis.</p><p><strong>Materials and methods: </strong>This retrospective study enrolled 22 participants with bladder SUC and 61 participants with CUC. The clinical, pathologic, and CT/MRI data from both groups were recorded, and a comparison was conducted using univariate analysis and multinomial logistic regression for distinguishing SUC from CUC.</p><p><strong>Results: </strong>The majority of SUCs were located in the trigone of the bladder and exhibited large tumor size, irregular shape, low ADC values, Vesical Imaging-Reporting and Data System (VI-RADS) score ≥ 4, the presence of necrosis, and an invasive nature. Univariate analysis revealed significant differences in terms of tumor location, shape, the maximum long-axis diameter (LAD), the short-axis diameter (SAD), ADC-value, VI-RADS scores, necrosis, extravesical extension (EVE), pelvic peritoneal spread (PPS), and hydronephrosis/ureteral effusion (p < .001 ~ p = .037) between SUCs and CUCs. Multinomial logistic regression found that only SAD (p = .014) and necrosis (p = .003) emerged as independent predictors for differentiating between SUC and CUC. The model based on these two factors achieved an area under curve (AUC) of 0.849 in ROC curve analysis.</p><p><strong>Conclusion: </strong>Bladder SUC demonstrates several distinct imaging features, including a high incidence of trigone involvement, large tumor size, and obvious invasiveness accompanied by necrosis. A bladder tumor with a large SAD and evidence of necrosis is more likely to be SUC rather than CUC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"102"},"PeriodicalIF":3.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11295343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141878436","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 MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach","authors":"Haifeng Qiu, Min Wang, Shiwei Wang, Xiao Li, Dian Wang, Yiwei Qin, Yongqing Xu, Xiaoru Yin, Marcus Hacker, Shaoli Han, Xiang Li","doi":"10.1186/s40644-024-00747-y","DOIUrl":"https://doi.org/10.1186/s40644-024-00747-y","url":null,"abstract":"The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and clinical features for AC patients. Clinical and pathological information from one hundred and ninety-seven patients with cervical AC was collected and analyzed. For each patient, 107 radiomics features were extracted from T2-weighted MRI images. Feature selection was performed using Spearman correlation and random forest (RF) algorithms, and predictive models were built using support vector machine (SVM) technique. Deep learning models were also trained with T2-weighted MRI images and clinicopathological features through Convolutional Neural Network (CNN). Kaplan-Meier curve was analyzed using significant features. In addition, information from another group of 56 AC patients was used for the independent validation. A total of 107 radiomics features and 6 clinicopathological features (age, FIGO stage, differentiation, invasion depth, lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) were included in the analysis. When predicting the 3-year, 4-year, and 5-year DFS, the model trained solely on radiomics features achieved AUC values of 0.659 (95%CI: 0.620–0.716), 0.791 (95%CI: 0.603–0.922), and 0.853 (95%CI: 0.745–0.912), respectively. However, the combined model, incorporating both radiomics and clinicopathological features, outperformed the radiomics model with AUC values of 0.934 (95%CI: 0.885–0.981), 0.937 (95%CI: 0.867–0.995), and 0.916 (95%CI: 0.857–0.970), respectively. For deep learning models, the MRI-based models achieved an AUC of 0.857, 0.777 and 0.828 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. And the combined deep learning models got a improved performance, the AUCs were 0.903. 0.862 and 0.969. In the independent test set, the combined model achieved an AUC of 0.873, 0.858 and 0.914 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. We demonstrated the prognostic value of integrating MRI-based radiomics and clinicopathological features in cervical adenocarcinoma. Both radiomics and deep learning models showed improved predictive performance when combined with clinical data, emphasizing the importance of a multimodal approach in patient management.","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study.","authors":"Ruixin Yan, Siyuan Qin, Jiajia Xu, Weili Zhao, Peijin Xin, Xiaoying Xing, Ning Lang","doi":"10.1186/s40644-024-00743-2","DOIUrl":"10.1186/s40644-024-00743-2","url":null,"abstract":"<p><strong>Background: </strong>Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC.</p><p><strong>Methods: </strong>Patients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2D_intra and 3D_intra), peritumoral (2D_peri and 3D_peri), and combined models (2D_{intra + peri} and 3D_{intra + peri}) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong's test.</p><p><strong>Results: </strong>No significant differences in AUC were observed between the 2D_intra and 3D_intra models, or the 2D_peri and 3D_peri models in all prediction tasks (P > 0.05). Significant difference was observed between the 3D_intra and 3D_peri models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3D_{intra + peri} models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3D_intra model in both the training and validation cohorts (P < 0.05).</p><p><strong>Conclusions: </strong>Comparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"100"},"PeriodicalIF":3.5,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859078","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 preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy.","authors":"Jiayin Zhou, Yansong Bai, Ying Zhang, Zezhou Wang, Shiyun Sun, Luyi Lin, Yajia Gu, Chao You","doi":"10.1186/s40644-024-00746-z","DOIUrl":"10.1186/s40644-024-00746-z","url":null,"abstract":"<p><strong>Background: </strong>Triple-negative breast cancer (TNBC) is highly heterogeneous, resulting in different responses to neoadjuvant chemotherapy (NAC) and prognoses among patients. This study sought to characterize the heterogeneity of TNBC on MRI and develop a radiogenomic model for predicting both pathological complete response (pCR) and prognosis.</p><p><strong>Materials and methods: </strong>In this retrospective study, TNBC patients who underwent neoadjuvant chemotherapy at Fudan University Shanghai Cancer Center were enrolled as the radiomic development cohort (n = 315); among these patients, those whose genetic data were available were enrolled as the radiogenomic development cohort (n = 98). The study population of the two cohorts was randomly divided into a training set and a validation set at a ratio of 7:3. The external validation cohort (n = 77) included patients from the DUKE and I-SPY 1 databases. Spatial heterogeneity was characterized using features from the intratumoral subregions and peritumoral region. Hemodynamic heterogeneity was characterized by kinetic features from the tumor body. Three radiomics models were developed by logistic regression after selecting features. Model 1 included subregional and peritumoral features, Model 2 included kinetic features, and Model 3 integrated the features of Model 1 and Model 2. Two fusion models were developed by further integrating pathological and genomic features (PRM: pathology-radiomics model; GPRM: genomics-pathology-radiomics model). Model performance was assessed with the AUC and decision curve analysis. Prognostic implications were assessed with Kaplan‒Meier curves and multivariate Cox regression.</p><p><strong>Results: </strong>Among the radiomic models, the multiregional model representing multiscale heterogeneity (Model 3) exhibited better pCR prediction, with AUCs of 0.87, 0.79, and 0.78 in the training, internal validation, and external validation sets, respectively. The GPRM showed the best performance for predicting pCR in the training (AUC = 0.97, P = 0.015) and validation sets (AUC = 0.93, P = 0.019). Model 3, PRM and GPRM could stratify patients by disease-free survival, and a predicted nonpCR was associated with poor prognosis (P = 0.034, 0.001 and 0.019, respectively).</p><p><strong>Conclusion: </strong>Multiscale heterogeneity characterized by DCE-MRI could effectively predict the pCR and prognosis of TNBC patients. The radiogenomic model could serve as a valuable biomarker to improve the prediction performance.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"98"},"PeriodicalIF":3.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141854935","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":"An interactive 3D atlas of sentinel lymph nodes in breast cancer developed using SPECT/CT.","authors":"Josephine Situ, Poppy Buissink, Annie Mu, David K V Chung, Rob Finnegan, Thiranja P Babarenda Gamage, Tharanga D Jayathungage Don, Cameron Walker, Hayley M Reynolds","doi":"10.1186/s40644-024-00738-z","DOIUrl":"10.1186/s40644-024-00738-z","url":null,"abstract":"<p><strong>Background: </strong>The identification and assessment of sentinel lymph nodes (SLNs) in breast cancer is important for optimised patient management. The aim of this study was to develop an interactive 3D breast SLN atlas and to perform statistical analyses of lymphatic drainage patterns and tumour prevalence.</p><p><strong>Methods: </strong>A total of 861 early-stage breast cancer patients who underwent preoperative lymphoscintigraphy and SPECT/CT were included. Lymphatic drainage and tumour prevalence statistics were computed using Bayesian inference, non-parametric bootstrapping, and regression techniques. Image registration of SPECT/CT to a reference patient CT was carried out on 350 patients, and SLN positions transformed relative to the reference CT. The reference CT was segmented to visualise bones and muscles, and SLN distributions compared with the European Society for Therapeutic Radiology and Oncology (ESTRO) clinical target volumes (CTVs). The SLN atlas and statistical analyses were integrated into a graphical user interface (GUI).</p><p><strong>Results: </strong>Direct lymphatic drainage to the axilla level I (anterior) node field was most common (77.2%), followed by the internal mammary node field (30.4%). Tumour prevalence was highest in the upper outer breast quadrant (22.9%) followed by the retroareolar region (12.8%). The 3D atlas had 765 SLNs from 335 patients, with 33.3-66.7% of axillary SLNs and 25.4% of internal mammary SLNs covered by ESTRO CTVs.</p><p><strong>Conclusion: </strong>The interactive 3D atlas effectively displays breast SLN distribution and statistics for a large patient cohort. The atlas is freely available to download and is a valuable educational resource that could be used in future to guide treatment.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"97"},"PeriodicalIF":3.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141854936","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":"Development and validation of a machine learning-based ¹⁸F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival.","authors":"Huaiqing Zhi, Yilan Xiang, Chenbin Chen, Weiteng Zhang, Jie Lin, Zekan Gao, Qingzheng Shen, Jiancan Shao, Xinxin Yang, Yunjun Yang, Xiaodong Chen, Jingwei Zheng, Mingdong Lu, Bujian Pan, Qiantong Dong, Xian Shen, Chunxue Ma","doi":"10.1186/s40644-024-00741-4","DOIUrl":"10.1186/s40644-024-00741-4","url":null,"abstract":"<p><strong>Background: </strong>Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC.</p><p><strong>Methods: </strong>We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness.</p><p><strong>Results: </strong>On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell's concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis.</p><p><strong>Conclusions: </strong>Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"99"},"PeriodicalIF":3.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11290137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141854937","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":"Pelvic lymph node mapping in prostate cancer: examining the impact of PSMA PET/CT on radiotherapy decision-making in patients with node-positive disease.","authors":"Ben Furman, Tal Falick Michaeli, Robert Den, Simona Ben Haim, Aron Popovtzer, Marc Wygoda, Philip Blumenfeld","doi":"10.1186/s40644-024-00742-3","DOIUrl":"10.1186/s40644-024-00742-3","url":null,"abstract":"<p><strong>Introduction: </strong>Prostate Specific Membrane Antigen (PSMA) imaging with Positron Emission Tomography (PET) plays a crucial role in prostate cancer management. However, there is a lack of comprehensive data on how PSMA PET/CT (Computed Tomography) influences radiotherapeutic decisions, particularly in node-positive prostate cancer cases. This study aims to address this gap by evaluating two primary objectives: (1) Mapping the regional and non-regional lymph nodes (LNs) up to the aortic bifurcation and their distribution using conventional methods with CT compared to PSMA PET/CT, and (2) assessing the impact of PSMA PET/CT findings on radiotherapeutic decisions.</p><p><strong>Methods: </strong>A retrospective analysis of 95 node-positive prostate cancer patients who underwent both CT and PSMA PET/CT imaging prior to primary radiotherapy and androgen deprivation therapy (ADT) was conducted. The analysis focused on identifying LNs in various regions including the common iliac, external iliac, internal iliac, obturator, presacral, mesorectal, inguinal, and other stations. Treatment plans were reviewed for modifications based on PSMA PET/CT findings, and statistical analysis was performed to identify predictors for exclusive nodal positivity on PSMA PET/CT scans.</p><p><strong>Results: </strong>PSMA PET/CT identified additional positive nodes in 48% of cases, resulting in a staging shift from N0 to N1 in 29% of patients. The most frequent metastatic LNs were located in the external iliac (76 LNs; 34%), internal iliac (43 LNs; 19%), and common iliac (35 LNs; 15%) stations. In patients with nodes only detected on PSMA PET the most common nodes were in the external iliac (27, 40%), internal iliac (13, 19%), obturator (11, 15%) stations. Within the subgroup of 28 patients exclusively demonstrating PSMA PET-detected nodes, changes in radiotherapy treatment fields were implemented in 5 cases (18%), and a dose boost was applied for 23 patients (83%). However, no discernible predictors for exclusive nodal positivity on PSMA PET/CT scans emerged from the analysis.</p><p><strong>Discussion: </strong>The study underscores the pivotal role of PSMA PET/CT compared to CT alone in accurately staging node-positive prostate cancer and guiding personalized radiotherapy strategies. The routine integration of PSMA PET/CT into diagnostic protocols is advocated to optimize treatment precision and improve patient outcomes.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"96"},"PeriodicalIF":3.5,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11285572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792020","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":"Deformation-based morphometry: a sensitive imaging approach to detect radiation-induced brain injury?","authors":"Carole Brunaud, Samuel Valable, Gwenn Ropars, Fatima-Azzahra Dwiri, Mikaël Naveau, Jérôme Toutain, Myriam Bernaudin, Thomas Freret, Marianne Léger, Omar Touzani, Elodie A Pérès","doi":"10.1186/s40644-024-00736-1","DOIUrl":"10.1186/s40644-024-00736-1","url":null,"abstract":"<p><strong>Background: </strong>Radiotherapy is a major therapeutic approach in patients with brain tumors. However, it leads to cognitive impairments. To improve the management of radiation-induced brain sequalae, deformation-based morphometry (DBM) could be relevant. Here, we analyzed the significance of DBM using Jacobian determinants (JD) obtained by non-linear registration of MRI images to detect local vulnerability of healthy cerebral tissue in an animal model of brain irradiation.</p><p><strong>Methods: </strong>Rats were exposed to fractionated whole-brain irradiation (WBI, 30 Gy). A multiparametric MRI (anatomical, diffusion and vascular) study was conducted longitudinally from 1 month up to 6 months after WBI. From the registration of MRI images, macroscopic changes were analyzed by DBM and microscopic changes at the cellular and vascular levels were evaluated by quantification of cerebral blood volume (CBV) and diffusion metrics including mean diffusivity (MD). Voxel-wise comparisons were performed on the entire brain and in specific brain areas identified by DBM. Immunohistology analyses were undertaken to visualize the vessels and astrocytes.</p><p><strong>Results: </strong>DBM analysis evidenced time-course of local macrostructural changes; some of which were transient and some were long lasting after WBI. DBM revealed two vulnerable brain areas, namely the corpus callosum and the cortex. DBM changes were spatially associated to microstructural alterations as revealed by both diffusion metrics and CBV changes, and confirmed by immunohistology analyses. Finally, matrix correlations demonstrated correlations between JD/MD in the early phase after WBI and JD/CBV in the late phase both in the corpus callosum and the cortex.</p><p><strong>Conclusions: </strong>Brain irradiation induces local macrostructural changes detected by DBM which could be relevant to identify brain structures prone to radiation-induced tissue changes. The translation of these data in patients could represent an added value in imaging studies on brain radiotoxicity.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"95"},"PeriodicalIF":3.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11256482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141723122","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}