Cancer Imaging最新文献

{"title":"Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.","authors":"Seyyed Ali Hosseini, Ghasem Hajianfar, Brandon Hall, Stijn Servaes, Pedro Rosa-Neto, Pardis Ghafarian, Habib Zaidi, Mohammad Reza Ay","doi":"10.1186/s40644-025-00857-1","DOIUrl":"10.1186/s40644-025-00857-1","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to select robust features against lung motion in a phantom study and use them as input to feature selection algorithms and machine learning classifiers in a clinical study to predict the lymphovascular invasion (LVI) of non-small cell lung cancer (NSCLC). The results of robust features were also compared with conventional techniques without considering the robustness of radiomic features.</p><p><strong>Methods: </strong>An in-house developed lung phantom was developed with two 22mm lesion sizes based on a clinical study. A specific motor was built to simulate motion in two orthogonal directions. Lesions of both clinical and phantom studies were segmented using a Fuzzy C-means-based segmentation algorithm. After inducing motion and extracting 105 radiomic features in 4 feature sets, including shape, first-, second-, and higher-order statistics features from each region of interest (ROI) of the phantom image, statistical analyses were performed to select robust features against motion. Subsequently, these robust features and a total of 105 radiomic features were extracted from 126 clinical data. Various feature selection (FS) and multiple machine learning (ML) classifiers were implemented to predict the LVI of NSCLC, followed by comparing the results of predicting LVI using robust features with common conventional techniques not considering the robustness of radiomic features.</p><p><strong>Results: </strong>Our results demonstrated that selecting robust features as input to FS algorithms and ML classifiers surges the sensitivity, which has a gentle negative effect on the accuracy and the area under the curve (AUC) of predictions compared with commonly used methods in 12 of 15 outcomes. The top performance of the LVI prediction was achieved by the NB classifier and RFE FS without considering the robustness of radiomic features with 95% area under the curve of AUC, 67% accuracy, and 100% sensitivity. Moreover, the top performance of the LVI prediction using robust features belonged to the NB classifier and Boruta feature selection with 92% AUC, 86% accuracy, and 100% sensitivity.</p><p><strong>Conclusion: </strong>Robustness over various influential factors is critical and should be considered in a radiomic study. Selecting robust features is a solution to overcome the low reproducibility of radiomic features. Although setting robust features against motion in a phantom study has a minor negative impact on the accuracy and AUC of LVI prediction, it boosts the sensitivity of prediction to a large extent.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"33"},"PeriodicalIF":3.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613250","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 interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.","authors":"Lihuan Dai, Jinxue Yin, Xin Xin, Chun Yao, Yongfang Tang, Xiaohong Xia, Yuanlin Chen, Shuying Lai, Guoliang Lu, Jie Huang, Purong Zhang, Jiansheng Li, Xiangguang Chen, Xi Zhong","doi":"10.1186/s40644-025-00855-3","DOIUrl":"10.1186/s40644-025-00855-3","url":null,"abstract":"<p><strong>Background: </strong>Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhanced computed tomography (CECT) radiomics for preoperatively predicting PD-L1 expression status in patients with gastric cancer (GC).</p><p><strong>Methods: </strong>We retrospectively recruited 285 GC patients who underwent CECT and PD-L1 detection from two medical centers. A PD-L1 combined positive score (CPS) of ≥ 5 was considered to indicate a high PD-L1 expression status. Patients from center 1 were divided into training (n = 143) and validation sets (n = 62), and patients from center 2 were considered a test set (n = 80). Radiomics features were extracted from venous-phase CT images. After feature reduction and selection, 11 ML algorithms were employed to develop predictive models, and their performance in predicting PD-L1 expression status was evaluated using areas under receiver operating characteristic curves (AUCs). SHapley Additive exPlanations (SHAP) were used to interpret the optimal model and visualize the decision-making process for a single individual.</p><p><strong>Results: </strong>Nine features significantly associated with PD-L1 expression status were ultimately selected to construct the predictive model. The light gradient-boosting machine (LGBM) model demonstrated the best performance for PD-L1 high expression status prediction in the training, validation, and test sets, with AUCs of 0.841(95% CI: 0.773, 0.908), 0.834 (95% CI:0.729, 0.939), and 0.822 (95% CI: 0.718, 0.926), respectively. The SHAP summary and bar plots illustrated that a feature's value affected the feature's impact attributed to the model. The SHAP waterfall plots were used to visualize the decision-making process for a single individual.</p><p><strong>Conclusion: </strong>Our CT radiomics-based LGBM model may aid in preoperatively predicting PD-L1 expression status in GC patients, and the SHAP method may improve the interpretability of this model.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"31"},"PeriodicalIF":3.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613227","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 value of whole-body MRI instead of only brain MRI in addition to 18 F-FDG PET/CT in the staging of advanced non-small-cell lung cancer.","authors":"Hanna Holmstrand, M Lindskog, A Sundin, T Hansen","doi":"10.1186/s40644-025-00852-6","DOIUrl":"10.1186/s40644-025-00852-6","url":null,"abstract":"<p><strong>Background: </strong>Non-small cell lung cancer (NSCLC) is a common neoplasm with poor prognosis in advanced stages. The clinical work-up in patients with locally advanced NSCLC mostly includes ¹⁸F-fluorodeoxyglucose positron emission tomography computed tomography (¹⁸F-FDG PET/CT) because of its high sensitivity for malignant lesion detection; however, specificity is lower. Diverging results exist whether whole-body MRI (WB-MRI) improves the staging accuracy in advanced lung cancer. Considering WB-MRI being a more time-consuming examination compared to brain MRI, it is important to establish whether or not additional value is found in detecting and characterizing malignant lesions. The purpose of this study is to investigate the value of additional whole-body magnetic resonance imaging, instead of only brain MRI, together with ¹⁸F-FDG PET/CT in staging patients with advanced NSCLC planned for curative treatment.</p><p><strong>Material and methods: </strong>In a prospective single center study, 28 patients with NSCLC stage 3 or oligometastatic disease were enrolled. In addition to ¹⁸F-FDG PET/CT, they underwent WB-MRI including the thorax, abdomen, spine, pelvis, and contrast-enhanced examination of the brain and liver. ¹⁸F-FDG PET/CT and WB-MRI were separately evaluated by two blinded readers, followed by consensus reading in which the likelihood of malignancy was assessed in detected lesions. Imaging and clinical follow-up for at least 12 months was used as reference standard. Statistical analyses included Fischer's exact test and Clopped-Pearson.</p><p><strong>Results: </strong>28 patients (mean age ± SD 70.5 ± 8.4 years, 19 women) were enrolled. WB-MRI and FDG-PET/CT both showed maximum sensitivity and specificity for primary tumor diagnosis and similar sensitivity (p = 1.00) and specificity (p = 0.70) for detection of distant metastases. For diagnosis of lymph node metastases, WB-MRI showed lower sensitivity, 0.65 (95% CI: 0.38-0.86) than FDG-PET/CT, 1.00 (95% CI: 0.80-1.00) (p < 0.05), but similar specificity (p = 0.59).</p><p><strong>Conclusions: </strong>WB-MRI in conjunction with ¹⁸F-FDG PET/CT provides no additional value over MRI of the brain only, in staging patients with advanced NSCLC.</p><p><strong>Trial registration: </strong>Registered locally and approved by the Uppsala University Hospital committee, registration number ASMR020.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"30"},"PeriodicalIF":3.5,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603995","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":"Additional findings in prostate MRI.","authors":"Fabio Porões, Paraskevi Karampa, Thomas Sartoretti, Hugo Najberg, Johannes M Froehlich, Carolin Reischauer, Harriet C Thoeny","doi":"10.1186/s40644-025-00846-4","DOIUrl":"10.1186/s40644-025-00846-4","url":null,"abstract":"<p><strong>Background: </strong>Despite the increasing interest in abbreviated protocols, we adopted an extended protocol for all prostate MRIs. In this study, we assessed the benefits of an extended prostate MRI protocol, measured by the number and the clinical importance of additional findings (AFs) and their impact on patient management.</p><p><strong>Methods: </strong>In a single-center study, we retrospectively included 1282 patients undergoing prostate MRI between 01.10.2018 and 30.04.2022. Additional findings were defined as any pathology not located in the prostate or the seminal vesicles. These were classified as related or unrelated to prostate cancer (PCa). The latter were divided into groups based on low, moderate, or high clinical significance (group 1, 2, and 3). A finding unrelated to PCa was judged to be clinically significant (group 2: moderate, group 3: high) if further diagnostic investigations, or treatment was necessary. The degree of urgency of the latter determined moderate and high significance. For group 3 findings, a change in management was defined as further workup.</p><p><strong>Results: </strong>A total of 5206 AFs was recorded in 1240/1282 patients. One hundred and twenty-three (2.4% of all findings) extra-prostatic PCa related AFs were found in 106 (8.3% of all patients) patients. The remaining 5083 (97.6% of all findings) findings were not related to PCa, of which 3155 (60.6%), 1770 (34.0%), and 158 (3.0%) were assigned to groups 1, 2, and 3, respectively. A management shift was identified in 49 (3.8% of all patients) patients of group 3.</p><p><strong>Conclusion: </strong>The extended prostate MRI protocol shows a considerable prevalence of AFs of which more than a third are clinically significant, related or unrelated to PCa (groups 2 and 3). A substantial percentage (8.3%) of patients have extra-prostatic PCa-related AFs that change the patient's disease stage and management. However, a change in management due to AFs unrelated to PCA that belong to group 3 is observed in less than 4% of all patients. The choice between extended and abbreviated prostate MRI protocols should be made based on available resources.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"29"},"PeriodicalIF":3.5,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603993","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":"Innovative optimization of greater omentum imaging report and data system for enhanced risk stratification of omental lesions.","authors":"Zhiguang Chen, Liang Sang, Yuan Cheng, Xuemei Wang, Mutian Lv, Yanjun Liu, ZhiQun Bai","doi":"10.1186/s40644-025-00848-2","DOIUrl":"10.1186/s40644-025-00848-2","url":null,"abstract":"<p><strong>Background: </strong>In 2020, we introduced the Greater Omentum Imaging-Reporting and Data System (GOI-RADS), a novel classification system related to peritoneal lesions. However, its clinical application remained unvalidated.</p><p><strong>Objective: </strong>This study aimed to validate GOI-RADS, optimize its parameters for a new grading system, and explore its clinical usefulness.</p><p><strong>Methods: </strong>A retrospective-prospective study was conducted to validate and refine the GOI-RADS system. The study consisted of two phases: a retrospective validation phase and a prospective application phase. The first phase included patients with peritoneal lesions from 2019 to 2021, classified by GOI-RADS and verified against pathology. Contrast-enhanced ultrasound (CEUS) and real-time elastography (RTE) data were collected for developing a new grading system. Odds ratios optimized parameters. The second phase (2021-2024) assessed diagnostic consistency among sonographers and performance of grading systems.</p><p><strong>Results: </strong>Among 215 patients with peritoneal lesions, the actual malignancy rates for GOI-RADS 2 (40.00%) and GOI-RADS 3 (61.22%) were much higher than predicted (5.56% and 37.25%). Combining CEUS and RTE parameters showed varying sensitivity and specificity: RTE + GOI-RADS (95.35%, 55.56%) and CEUS + GOI-RADS (96.51%, 44.44%). However, the grading system based on multiple ultrasound parameters, specifically when incorporating RTE, CEUS parameters, and GOI-RADS (Multi-GOIRADS), exhibited the highest diagnostic sensitivity and specificity of 88.37% and 83.33%, respectively. Its simplified version, sMulti-GOIRADS, had sensitivity of 73.26% and specificity of 94.44%. In the prospective study involving three sonographers of different qualifications, the use of sMulti-GOIRADS was found to be the most time-efficient and showed excellent diagnostic consistency among them. In contrast, Multi-GOIRADS required more time for scoring but offered superior diagnostic performance, particularly among senior sonographers (88.35% and 91.43%).</p><p><strong>Conclusions: </strong>This study proposes a multiparametric ultrasound-based imaging-reporting and data system for risk stratification of omental malignancy, Multi-GOIRADS, and presents an optimized and simplified version, sMulti-GOIRADS, which demonstrates excellent diagnostic consistency and performance in clinical applications.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"28"},"PeriodicalIF":3.5,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11892154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595758","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 CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study.","authors":"Xiaomeng Han, Jing Guan, Li Guo, Qiyan Jiao, Kexin Wang, Feng Hou, Shunli Liu, Shifeng Yang, Chencui Huang, Wenbin Cong, Hexiang Wang","doi":"10.1186/s40644-025-00849-1","DOIUrl":"10.1186/s40644-025-00849-1","url":null,"abstract":"<p><strong>Background: </strong>To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).</p><p><strong>Methods: </strong>This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models. We then compared the performance of the DL signature with the radiomics machine learning signature and selected the optimal signature to build a nomogram with the clinical model. Finally, the internal forecasting process of the DL signature was explained using Shapley additive explanation technology.</p><p><strong>Results: </strong>On the external validation set, the DL signature had an area under the curve of 0.857 (95% confidence interval: 0.745-0.932), and demonstrated superior prediction performance in comparison with the other models. SHAP expression images revealed that the prediction of PD-L1 expression status is mainly influenced by the tumor edge region, particularly the area close to the bladder wall.</p><p><strong>Conclusions: </strong>The DL signature performed well in comparison with other models and proved to be a valuable, dependable, and interpretable tool for predicting programmed cell death ligand 1 expression status in patients with BCa.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"27"},"PeriodicalIF":3.5,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11892212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596400","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":"DCE-MRI quantitative analysis and MRI-based radiomics for predicting the early efficacy of microwave ablation in lung cancers.","authors":"Chen Yang, Fandong Zhu, Jing Yang, Min Wang, Shijun Zhang, Zhenhua Zhao","doi":"10.1186/s40644-025-00851-7","DOIUrl":"10.1186/s40644-025-00851-7","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the feasibility and value of dynamic contrast-enhanced MRI (DCE-MRI) quantitative analysis and MRI-based radiomics in predicting the efficacy of microwave ablation (MWA) in lung cancers (LCs).</p><p><strong>Methods: </strong>Forty-three patients with LCs who underwent DCE-MRI within 24 h of receiving MWA were enrolled in the study and divided into two groups according to the modified response evaluation criteria in solid tumors (m-RECIST) criteria: the effective treatment (complete response + partial response + stable disease, n = 28) and the ineffective treatment (progressive disease, n = 15). DCE-MRI datasets were processed by Omni. Kinetics software, using the extended tofts model (ETM) and exchange model (ECM) to yield pharmacokinetic parameters and their histogram features. Changes in quantitative perfusion parameters were compared between the two groups. Scientific research platform ( https://medresearch.shukun.net/ ) was used for radiomics analysis. A total of 1874 radiomic features were extracted for each tumor by manually segmentation of T1WI and Contrast-enhanced of T1WI (Ce-T1WI) fat inhibition sequence. The performances of radiomics models were evaluated by the receiver operating characteristic curve. Based on radiomics features, survival curves were generated by Kaplan-Meier survival analysis to evaluate patient outcomes. P < 0.05 was set for the significance threshold.</p><p><strong>Results: </strong>The V_p value of ECM was significantly higher in the ineffective group compared to the effective group (p = 0.027). Additionally, the skewness, and kurtosis of V_p (p = 0.020 and 0.013, respectively) derived from ETM and F_p (p = 0.027 and 0.030, respectively) from ECM as well as the quantiles were higher in the ineffective group than in the effective group. Significant statistical differences were observed in the energy and entropy of V_e (p = 0.044 and 0.025, respectively) and V_p (p = 0.025 and 0.026, respectively) between the two groups. In the process of model construction, seven features from T1WI, five features from Ce-T1WI, and ten features from combined sequences were ultimately selected. The area under the curve (AUC) values for the T1WI model, Ce-T1WI model, and combined model were 0.910, 0.890, 0.965 in the training group, and 0.850, 0.700, 0.850 in the test group, respectively.</p><p><strong>Conclusions: </strong>DCE-MRI quantitative analysis and MRI-based radiomics may be helpful in assessing the early response to MWA in patients with LCs.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"26"},"PeriodicalIF":3.5,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11892232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596404","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 multiclass classification of thymic mass lesions based on radiomics and machine learning.","authors":"Yan Zhu, Li Wang, Aichao Ruan, Zhiyu Peng, Zhenzhong Zhang","doi":"10.1186/s40644-025-00839-3","DOIUrl":"10.1186/s40644-025-00839-3","url":null,"abstract":"<p><strong>Background: </strong>Apart from rare cases such as lymphomas, germ cell tumors, neuroendocrine neoplasms, and thymic hyperplasia, thymic mass lesions (TMLs) are typically categorized into cysts, and thymomas. However, the classification results cannot be determined in advance and can only be confirmed through postoperative pathology. Therefore, the objective of this study is to rely on clinical parameters and radiomic features extracted from chest computed tomography (CT) scans to facilitate the preoperative classification of TMLs. The model development specifically focused on thymic cysts and thymomas, as these are the most commonly encountered anterior mediastinal tumors in clinical practice.</p><p><strong>Materials and methods: </strong>This retrospective study included 400 participants from 3 hospitals between September 2017 and September 2024 due to TMLs. The participants were classified into 7 groups based on the ultimately confirmed etiology: thymic cysts and thymomas, including types A, AB, B1, B2, B3, and C. All participants underwent contrast-enhanced chest CT scans, with senior radiologists delineating regions of interest to extract radiomic features. Additionally, the participants' ages were also collected as clinical parameters for analysis. The participants were randomly allocated into a training set and a validation set at a 7:3 ratio. A classifier models were developed using the data from the training set, and their performances were evaluated on the validation set.</p><p><strong>Results: </strong>The model exhibited good classification performance with accuracies of 0.8547.</p><p><strong>Conclusion: </strong>The model can assist in early diagnosis and the development of personalized treatment strategies for patients with TMLs.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"25"},"PeriodicalIF":3.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11884038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572288","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":"Imaging genomics of cancer: a bibliometric analysis and review.","authors":"Xinyi Gou, Aobo Feng, Caizhen Feng, Jin Cheng, Nan Hong","doi":"10.1186/s40644-025-00841-9","DOIUrl":"10.1186/s40644-025-00841-9","url":null,"abstract":"<p><strong>Background: </strong>Imaging genomics is a burgeoning field that seeks to connections between medical imaging and genomic features. It has been widely applied to explore heterogeneity and predict responsiveness and disease progression in cancer. This review aims to assess current applications and advancements of imaging genomics in cancer.</p><p><strong>Methods: </strong>Literature on imaging genomics in cancer was retrieved and selected from PubMed, Web of Science, and Embase before July 2024. Detail information of articles, such as systems and imaging features, were extracted and analyzed. Citation information was extracted from Web of Science and Scopus. Additionally, a bibliometric analysis of the included studies was conducted using the Bibliometrix R package and VOSviewer.</p><p><strong>Results: </strong>A total of 370 articles were included in the study. The annual growth rate of articles on imaging genomics in cancer is 24.88%. China (133) and the USA (107) were the most productive countries. The top 2 keywords plus were \"survival\" and \"classification\". The current research mainly focuses on the central nervous system (121) and the genitourinary system (110, including 44 breast cancer articles). Despite different systems utilizing different imaging modalities, more than half of the studies in each system employed radiomics features.</p><p><strong>Conclusions: </strong>Publication databases provide data support for imaging genomics research. The development of artificial intelligence algorithms, especially in feature extraction and model construction, has significantly advanced this field. It is conducive to enhancing the related-models' interpretability. Nonetheless, challenges such as the sample size and the standardization of feature extraction and model construction must overcome. And the research trends revealed in this study will guide the development of imaging genomics in the future and contribute to more accurate cancer diagnosis and treatment in the clinic.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"24"},"PeriodicalIF":3.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555884","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":"Head-to-head comparison of ¹⁸F-FDG and ⁶⁸Ga-FAPI PET/CT in common gynecological malignancies.","authors":"Tengfei Li, Jintao Zhang, Yuanzhuo Yan, Yue Zhang, Wenjie Pei, Qingchu Hua, Yue Chen","doi":"10.1186/s40644-025-00843-7","DOIUrl":"10.1186/s40644-025-00843-7","url":null,"abstract":"<p><strong>Background: </strong>⁶⁸Ga-FAPI (fibroblast activation protein inhibitor) is a novel and highly promising radiotracer for PET/CT imaging. It has shown significant tumor uptake and high sensitivity in lesion detection across a range of cancer types. We aimed to compare the diagnostic value of ⁶⁸Ga-FAPI and ¹⁸F-FDG PET/CT in common gynecological malignancies.</p><p><strong>Methods: </strong>This retrospective study included 35 patients diagnosed with common gynecological tumors, including breast cancer, ovarian cancer, and cervical cancer. Among the 35 patients, 27 underwent PET/CT for the initial assessment of tumors, while 8 were assessed for recurrence detection. The median and range of tumor size and maximum standardized uptake values (SUV_max) were calculated.</p><p><strong>Results: </strong>Thirty-five patients (median age, 57 years [interquartile range], 51-65 years) were evaluated. In treatment-naive patients (n = 27), ⁶⁸Ga-FAPI PET/CT led to upstaging of the clinical TNM stage in five (19%) patients compared with ¹⁸F-FDG PET/CT. No significant difference in tracer uptake was observed between ¹⁸F-FDG and ⁶⁸Ga-FAPI for primary lesions: breast cancer (7.2 vs. 4.9, P = 0.086), ovarian cancer (16.3 vs. 15.7, P = 0.345), and cervical cancer (18.3 vs. 17.1, P = 0.703). For involved lymph nodes, ⁶⁸Ga-FAPI PET/CT demonstrated a higher SUV_max for breast cancer (9.9 vs. 6.1, P = 0.007) and cervical cancer (6.3 vs. 4.8, P = 0.048), while no significant difference was noted for ovarian cancer (7.0 vs. 5.9, P = 0.179). Furthermore, ⁶⁸Ga-FAPI PET/CT demonstrated higher specificity and accuracy compared to ¹⁸F-FDG PET/CT for detecting metastatic lymph nodes (100% vs. 66%, P < 0.001; 94% vs. 80%, P < 0.001). In contrast, sensitivity did not differ significantly (97% vs. 86%, P = 0.125). For most distant metastases, ⁶⁸Ga-FAPI exhibited a higher SUV_max than ¹⁸F-FDG in bone metastases (12.9 vs. 4.9, P = 0.036).</p><p><strong>Conclusions: </strong>⁶⁸Ga-FAPI PET/CT demonstrated higher tracer uptake and was superior to ¹⁸F-FDG PET/CT in detecting primary and metastatic lesions in patients with common gynecological malignancies.</p><p><strong>Trial registration: </strong>ChiCTR, ChiCTR2100044131. Registered 10 October 2022, https://www.chictr.org.cn , ChiCTR2100044131.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"25 1","pages":"21"},"PeriodicalIF":3.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11869701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531314","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}