Cancer Imaging最新文献

{"title":"Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: CT vs MRI.","authors":"Feifei Yu, Junhui Yuan, Xiaoye Lin, Feng Wang, Lan Yu, Shujie Yu, Youquan Zhu, Yang Song, Dairong Cao, Jieyun Chen, Zhen Xing","doi":"10.1186/s40644-026-01018-8","DOIUrl":"10.1186/s40644-026-01018-8","url":null,"abstract":"<p><p>BACKGROUND: To systematically evaluate and compare the diagnostic efficacy of radiomics models derived from noncontrast CT (NCCT) versus multiparametric MRI in differentiating glioblastoma (GBM) from primary central nervous system lymphoma (PCNSL). METHODS: In this retrospective, multicenter study, 543 patients with pathologically confirmed GBM (n = 401) or PCNSL (n = 142) were divided into 3 cohorts. 1084 quantitative features were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions across NCCT and five MRI sequences (T2WI, T1WI, ADC, FLAIR, and CE-T1WI). Feature selection employed ANOVA, Kruskal-Wallis test, and recursive feature elimination, followed by nested cross-validation (5-fold outer, 3-fold inner) to construct four machine learning classifiers: support vector machine, linear discriminant analysis, logistic regression, and decision tree. Model performance was rigorously assessed through AUC, accuracy, sensitivity, specificity with bootstrap-derived 95% confidence intervals. The Shapley Additive Explanation (SHAP) analysis was employed to explore the interpretability of models. RESULTS: The CE-T1WI radiomics model demonstrated superior diagnostic capability, with its AUCs of train/internal test/external test in CE regions and NE regions were 0.962/0.963/0.907 and 0.966/0.892/0.867, respectively. Notably, the CT-based model was not significantly different from other MRI models except for CE-T1WI model. The AUCs of train/internal test/external test for CT model in CE and NE regions were 0.941/0.906/0.822 and 0.902/0.891 /0.782, respectively. CONCLUSIONS: Both NCCT and multiparametric MRI are valuable in identifying GBM and PCNSL. The CE-T1WI radiomics model has the best diagnostic efficacy. </p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13104201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147466830","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 result prediction of fluorescence in situ hybridization for breast cancer patients based on machine learning and deep learning models: a multicenter study.","authors":"Cong Jiang, Dong Chen, Xiao Yu, Shentao Zhang, Yuting Xiu, Ningbin Luo, Jingjing Wu, Xuefang Zhang, Man Chen, Dechun Yang, Ziyu Zhu, Yuanxi Huang, Shipeng Ning, Shicong Tang","doi":"10.1186/s40644-026-01015-x","DOIUrl":"10.1186/s40644-026-01015-x","url":null,"abstract":"<p><p>OBJECTIVE: To develop and validate a multicenter ultrasound-based predictive model for fluorescence in situ hybridization (FISH) results in HER2 (2+) breast cancer patients, aiming to provide a convenient and cost-effective tool to support clinical decision-making. MATERIALS AND METHODS: In this retrospective multicenter study, 5,888 breast cancer patients from six institutions were included. Radiomics features were extracted from ultrasound images using PyRadiomics, and deep learning features were obtained using a Vision Transformer (ViT). Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression. Multiple machine learning models were developed, and their performance was evaluated with the area under the curve (AUC). The DeLong test was used for model comparison. RESULTS: The proportion of FISH-positive cases ranged from 9.7% to 20.0% across the six centers. The fusion model combining ViT and radiomics signatures consistently outperformed the individual models in the training, test, and all external validation cohorts. The AUCs of the fusion model were 0.887 in the training cohort, 0.799 in the test cohort, and 0.763, 0.796, 0.734, and 0.632 in the four external validation cohorts, respectively (all P < 0.05). CONCLUSION: The proposed ultrasound-based fusion model enables accurate prediction of FISH assay results in HER2 (2+) breast cancer patients and may serve as a reliable decision-support tool to reduce unnecessary FISH testing in clinical practice. </p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13097979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147455180","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":"Response assessment in rectal cancer undergoing total neoadjuvant therapy: decision tree analysis integrating contrast enhanced T1-weighted MRI.","authors":"An-Hsin Chen, Eric Yi-Liang Shen, Chih-Ying Deng, Jeng-Fu You, Tai-Di Chen, Chien-Ming Chen, Gigin Lin, Ying-Chieh Lai","doi":"10.1186/s40644-026-01016-w","DOIUrl":"10.1186/s40644-026-01016-w","url":null,"abstract":"<p><p>BACKGROUND: This study assesses the role of contrast enhanced T1-weighted imaging (CE-T1WI) in improving response assessment for rectal cancer patients undergoing total neoadjuvant therapy (TNT) and its integration with T2-weighted imaging (T2WI) and DWI using decision tree analysis. METHODS: A retrospective analysis was conducted on rectal cancer patients between January 2020 and December 2022 who underwent TNT followed by MRI-based response assessment, including T2WI, DWI, and CE-T1WI. Complete response (CR) was defined as pathologic CR or sustained clinical CR for ≥2 years. Diagnostic performance was assessed using receiver operating characteristic analysis. Logistic regression was performed to identify predictors of CR. Finally, a decision tree model was constructed to determine the optimal combination of sequences for predicting CR. RESULTS: A total of 124 rectal cancer patients (mean age 58 ± 12 years; 89 men) were analyzed, of whom 45 (36%) achieved CR. CE-T1WI demonstrated superior predictive performance (AUC = 82.3% [75.0−88.7]) compared to DWI (78.6% [71.1−85.9]) and T2WI (74.5% [66.5−82.4]). In the two sequences combinations, the combination of DWI and CE-T1WI achieved the highest AUC of 82.9% (75.7−89.8), with an accuracy of 85.5% (79.0−91.9), sensitivity of 73.4% (60.0−86.0) and specificity of 92.4% (85.9−97.6), outperforming the other combinations. Finally, the decision tree model integrating three sequences achieved an AUC of 90.1% (83.7−95.6), with an accuracy of 86.4% (80.6−91.9), a sensitivity of 77.6% (62.5−91.8) and a specificity of 91.2% (81.7−97.6). CONCLUSION: Integrating CE-T1WI with other sequences enhances CR prediction in rectal cancer patients undergoing TNT, supporting its inclusion in routine MRI for improved response assessment. </p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13094038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147430779","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":"Precision medicine in myeloma demands precision imaging: is whole body MRI the solution?","authors":"Christina Messiou, Martin Kaiser","doi":"10.1186/s40644-026-01017-9","DOIUrl":"10.1186/s40644-026-01017-9","url":null,"abstract":"<p><p>Outcomes for patients with multiple myeloma have improved substantially over the past two decades, with median overall survival more than doubling in many cohorts. Management of smouldering myeloma has also shifted, with randomised trials demonstrating that early intervention in high-risk disease delays progression and improves survival compared with observation alone. Imaging diagnostics must therefore evolve in parallel to provide minimally invasive, sensitive and quantitative insights into disease for patients who will live with myeloma for many years. Here we review the latest evidence for WB-MRI in myeloma, outline current guideline recommendations, and consider future directions.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12977864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147430744","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":"Prediction of MYC/BCL-2 co-expression in diffuse large B-cell lymphoma using a multimodal fusion model: a retrospective study based on PET/CT habitat radiomics and deep learning.","authors":"Yu He, Shirong Chen, Xinyang Li, Jingkai Yi, Dan Wang, Kailin Qi, Xiao Jiang, Ping Wu, Meng Zhao, Hao Lu, Ying Kou, Yutang Yao, Zhuzhong Cheng","doi":"10.1186/s40644-026-01014-y","DOIUrl":"10.1186/s40644-026-01014-y","url":null,"abstract":"<p><p>BACKGROUND: The co-expression of MYC and BCL-2 proteins in diffuse large B-cell lymphoma (DLBCL) is linked to poor prognosis and resistance to standard therapies. Thus, a non-invasive and accurate method to detect this co-expression before treatment is essential for pre-treatment risk stratification and assisting in personalized patient management. METHODS: This retrospective study included DLBCL patients who underwent baseline 18F-FDG PET/CT between December 2018 and August 2024. Clinical data were collected. Habitat radiomics features were extracted by segmenting tumors into distinct subregions, and 3D deep learning features were obtained using convolutional neural networks, both derived from PET/CT images. Two individual models were built: A habitat radiomics model and a 3D deep learning model. A multimodal fusion model was also constructed by integrating dimensionally reduced features from habitat radiomics, 3D deep learning, clinical data, and PET-derived metabolic parameters. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). DeLong’s test was used to compare AUCs, and net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to assess net benefit. RESULTS: A total of 242 patients were enrolled (95 DEL-positive [39.3%] and 147 DEL-negative [60.7%]) and were stratified-randomly split by DEL status into a training set (n = 193) and test set (n = 49) in an 8:2 ratio. This was a retrospective single-center study with an internal hold-out test cohort. All feature selection and model development were performed in the training cohort only, and the test cohort was used solely for final evaluation. The habitat radiomics model showed better performance than the deep learning model, with AUCs of 0.869 (95% CI: 0.820–0.918) and 0.812 (95% CI: 0.661–0.964) vs. 0.844 (95% CI: 0.787–0.902) and 0.715 (95% CI: 0.562–0.869) in the training and test sets, respectively. The fusion model outperformed both, achieving AUCs of 0.946 (95% CI: 0.917–0.974) in the training and 0.890 (95% CI: 0.793–0.987) in the test set. Calibration curves demonstrated strong agreement between predicted and observed outcomes. DCA confirmed higher clinical benefit for the fusion model. DeLong’s test showed the fusion model significantly outperformed both individual models in the training set and the deep learning model in the test set (P < 0.05). NRI and IDI further supported improved discrimination, suggesting potential incremental value. CONCLUSIONS: The multimodal fusion model based on 18F-FDG PET/CT and clinical data provides a non-invasive and reliable tool for predicting MYC/BCL-2 co-expression in DLBCL, providing complementary prognostic information to assist personalized treatment planning. TRIAL REGISTRATION: This study was retrospectively registered. </p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13101366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147430767","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":"Deep learning prediction model based on multi-modal transvaginal ultrasound scan images for endometrial cancer.","authors":"Xingzhe Liu, Yuanjia Wen, Jiahao Liu, Wenzhi Lv, Yuan Wu, Yue Gao, Shaoqing Zeng, Guannan Li, Yu Xia, Shennan Shi, Qiuyang Xu, Xiaofei Jiao, Wenjian Gong, Ding Ma, Guang-Nian Zhao, Yong Fang, Xiaodong Cheng, Xiao-Yan Xu, Dan Liu, Qinglei Gao","doi":"10.1186/s40644-026-01013-z","DOIUrl":"10.1186/s40644-026-01013-z","url":null,"abstract":"<p><p>BACKGROUND: Ultrasound-based deep learning (DL) models for the precise diagnosis of endometrial cancer are insufficient. Our aim is to develop and validate an automatic multi-modal ultrasound DL prediction model for the accurate identification of benign versus malignant endometrial diseases. METHODS: The retrospective dataset of patients with endometrial diseases from two hospitals was segregated into an internal set (n = 696) and an external set (n = 78). All patients underwent grayscale, color Doppler, and 3D reconstructed ultrasound scans. We established ResNet-18 DL models using individual sequences (DLgray, DLCDFI, DL3D) and multi-modal sequences (DLfusion), with comparative performance evaluation through area under the receiver operating characteristic curve (AUC) analysis. We compared the best-performing model with 12 radiologists and assessed the diagnostic performance of radiologists with model assistance. RESULTS: The DLfusion model exhibits precise discrimination capabilities for benign and malignant endometrial diseases, achieving an AUC of 0.92. This performance significantly surpasses that of DLgray (0.78, p < 0.01), DLCDFI (0.79, p = 0.01), and assessments by radiologists (0.64, p < 0.001). The DLfusion model is more accurate than radiologists (0.86 vs. 0.78, p < 0.001) at detecting endometrial cancer. With the assistance of the DLfusion model, the average diagnostic accuracy of twelve radiologists significantly improves from 0.77 to 0.85 (p < 0.001). CONCLUSIONS: The DLfusion model based on multi-modal ultrasonic images exhibits a better capacity in diagnosing endometrial cancer compared with radiologists and DL models based on single modal, and significantly improves the diagnostic accuracy of radiologists, offering valuable support for clinical decision-making. </p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13049860/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147321433","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":"Preclinical evaluation of FAP-targeted PET imaging to investigate CAF responses to radiotherapy.","authors":"Kristin Lode, Sindhu Kancherla, Yngve Guttormsen, Rodrigo Berzaghi, Vera Susana Maia, Angel Moldes-Anaya, Turid Hellevik, Mathias Kranz, Inigo Martinez-Zubiaurre","doi":"10.1186/s40644-026-01010-2","DOIUrl":"10.1186/s40644-026-01010-2","url":null,"abstract":"<p><p>BACKGROUND: Cancer-associated fibroblasts (CAFs) are influential elements of the tumor microenvironment with significant roles in tumor progression and therapy resistance. However, the effects that radiotherapy has on CAFs dynamics remain understudied. This study aimed to provide a non-invasive PET imaging approach to quantify radiation-induced changes in cancer-associated fibroblasts in preclinical tumor models. METHODS: The FAP-specific radiotracer [18F]AlF-FAPI-74 was used to monitor CAFs dynamics following precision external beam radiotherapy (RT) in two syngeneic subcutaneous murine tumor models (LLC and CT26). Tumors were irradiated via two radiation regimens (1 × 12 Gy or 2 × 6 Gy), and dynamic PET/MR imaging was performed 7 days after RT. Additionally, the dynamics of FAP+ CAFs in tumors were quantified ex vivo via flow cytometry and immunohistochemistry. RESULTS: Tumor-targeted irradiation led to a significant reduction in tumor size. Uptake of [18F]AlF-FAPI-74 in subcutaneous tumors was low but significantly above muscle-background values. Quantification of standardized uptake values (SUVs) from static PET images revealed a twofold increase in the PET signal in LLC tumors irradiated with two fractions of medium-dose RT (2 × 6 Gy). Ex vivo analysis confirmed the low abundance of FAP+ cells in tumors and demonstrated similar RT-induced changes in CAF levels across the different models. CONCLUSIONS: Our findings suggest that CAFs constitute a relatively small cell population in subcutaneously transplanted tumor models, and that fractionated radiotherapy may induce a moderate increase in FAP+ cells in LLC tumors. Additionally, we demonstrated that [18F]AlF-FAPI-74 is a reliable biomarker for evaluating the number of FAP+ stromal cells in tumors and for addressing potential therapy-induced changes in CAFs. </p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13049841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147316448","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":"Evaluation of RECIST v1.1 for predicting overall survival in sarcoma patients with pulmonary metastasis.","authors":"Lukas Gold, Konstantin Klambauer, Christian Dascalescu, Luca Klinge, Michael Winkelmann, Nabeel Mansour, Dirk Mehrens, Jens Ricke, Michael von Bergwelt-Baildon, Dorit Di Gioia, Lars H Lindner, Wolfgang G Kunz","doi":"10.1186/s40644-026-01012-0","DOIUrl":"10.1186/s40644-026-01012-0","url":null,"abstract":"<p><strong>Purpose: </strong>Response assessment in the treatment of metastatic sarcoma primarily depends on imaging, as no established clinical or serological biomarkers reliably predict survival outcomes. This study evaluates the utility of Response Evaluation Criteria in Solid Tumors (RECIST v1.1) in predicting overall survival (OS) in sarcoma patients with pulmonary metastases.</p><p><strong>Methods: </strong>We selected consecutive study subjects from a prospective registry based on the following criteria: (1) available CT imaging at first diagnosis of pulmonary metastases from sarcoma, (2) available follow-up CT imaging within 16 weeks of systemic therapy initiation, (3) documentation of OS. Volumetric segmentation of up to 5 lung metastases was performed over time. Progressive disease (PD) was defined as increase of the unidimensional sum of lesions ≥ 20% or appearance of new metastases according to RECIST v1.1. Kaplan-Meier survival analyses were performed. P values < 0.05 were considered statistically significant.</p><p><strong>Results: </strong>Ninety-two patients were included (median age: 58 years; 50% female). Average time of follow-up CT was 67 days after baseline imaging. Patients with PD on first follow-up imaging (n = 24; 26%) showed significantly shorter OS (13.9 months vs. 29.3 months; p = 0.014). The unidimensional growth threshold of 20% proposed by RECIST did not stratify OS (14.6 months vs. 26.8 months, p = 0.221). The appearance of new metastases (n = 16; 17%) indicated significantly shorter OS (7.8 months vs. 27.0 months; p < 0.001) and was frequently observed even in patients with decreasing size of existing metastases (n = 7; 8%).</p><p><strong>Conclusion: </strong>Imaging progression patterns of pulmonary metastatic sarcoma demonstrate distinct associations with OS, highlighting the need for sarcoma-specific adaptations to established response criteria.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147302626","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 based unsuperviced clustering on MIBC reveals intratumor heterogeneity phenotypes and neoadjuvant chemotherapy efficacy.","authors":"Yuheng Zhang, Lingkai Cai, Yuhan Chen, Rongjie Bai, Chenghao Wang, Zhengye Tan, Chang Chen, Xiaotong Liu, Bo Liang, Meihua Jiang, Gongcheng Wang, Qiang Shao, Xuping Jiang, Meiling Bao, Hao Yu, Qiang Cao, Xiao Yang, Qiang Lu","doi":"10.1186/s40644-026-01007-x","DOIUrl":"10.1186/s40644-026-01007-x","url":null,"abstract":"<p><strong>Background: </strong>To develop and validate a non-invasive imaging-based intratumor heterogeneity (IITH) framework for risk stratification in muscle-invasive bladder cancer (MIBC) and to investigate its association with survival outcomes and molecular subtypes.</p><p><strong>Methods: </strong>In this retrospective multicenter study, 437 MIBC patients were enrolled (237 patients of discovery cohort and 102 patients of validation cohort predominantly underwent radical cystectomy (RC); 98 patients of NAC cohort received three to four cycles of gemcitabine-cisplatin chemotherapy prior to surgery). From pretreatment MRI scans, 42 radiomic features were extracted. Unsupervised consensus clustering was performed using the \"ConsensusClusterPlus\" R package, a resampling-based method which generates a consensus matrix to robustly stratify cohorts into stable IITH subtypes. Prognostic significance for OS, CSS, RFS, PFS was then evaluated based on data reviewed up to July 30, 2024. Molecular correlates were assessed via immunohistochemistry and transcriptomic data from The Cancer Genome Atlas.</p><p><strong>Results: </strong>The IITH framework significantly stratified patient survival in the discovery cohort (OS: HR = 1.646, p < 0.05; CSS: HR = 1.792, p < 0.05; RFS: HR = 2.06, p < 0.01; PFS: HR = 1.721, p = 0.01), with consistent results in external validation cohorts (OS: HR = 2.604, p < 0.05; RFS: HR = 2.422, p < 0.05; PFS: HR = 2.72, p < 0.05). High-IITH tumors demonstrated greater histological diversity and enriched mixed subtypes. In the NAC cohort, high IITH was associated with markedly worse outcomes (CSS: HR = 7.795, p < 0.05; RFS: HR = 9.529, p < 0.001; PFS: HR = 8.893, p < 0.001). Transcriptomic analysis revealed elevated inflammatory activity and enhanced proliferative/migratory potential in high-IITH groups.</p><p><strong>Conclusions: </strong>The MRI-based IITH framework effectively stratified MIBC patients by prognosis and treatment response, with high heterogeneity being associated with adverse outcomes and aggressive molecular tumor biology.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13040948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147302676","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":"From radiologic morphology to component proportion: a multicenter study on the progression and prognosis of lung adenocarcinoma with cystic airspaces.","authors":"Chenghao Fu, Chengyu Bian, Haonan Du, Yuheng Wang, Jia Zhang, Xiang Li, Lei Cao, Jian Zhu, Jun Wang","doi":"10.1186/s40644-026-01009-9","DOIUrl":"10.1186/s40644-026-01009-9","url":null,"abstract":"<p><p>BACKGROUND: Lung adenocarcinoma with cystic airspaces (LACA) poses clinical challenges due to its irregular morphology and uncertain biological behavior. We aimed to elucidate its progression and prognosis through radiologic morphology and component proportion. METHODS: This multicenter retrospective study included 378 patients with LACA confirmed by serial imaging and pathology. Among them, a subgroup of 86 patients with clear transformation of nodule from ground-glass opacity (GGO) to solidity was selected to identify progression phases and patterns. Clinicopathological features were compared across different progression phases and radiologic morphologies. Two imaging-based ratios -the cystic airspace-to-lesion ratio (CLR) and the solid component-to-lesion ratio (SLR)- were evaluated for their prognostic value. Kaplan-Meier curves and log-rank tests were used to analyze overall survival (OS) and recurrence-free survival (RFS). RESULTS: Four progression phases were identified, with higher progression phases correlating with advanced T/N stages, increased malignant features, and poorer prognosis. We discovered that LACA may originate from either pre-existing cystic airspaces or pure GGO, and identified eight distinct progression patterns. The walled and massed type showed similar progression and prognosis. Cox regression analyses identified high SLR, but not low CLR, as a significant independent risk factor for recurrence. SLR demonstrated a steady increase with progression, while CLR was low in both early and advanced phases. CONCLUSION: This study established a unified four-phase progression model for LACA encompassing different morphologies and delineated eight distinct progression patterns reflecting heterogeneous initiation pathways. It also highlighted SLR as a more reliable prognostic indicator than CLR. These findings provide insight into the clinical management of LACA. </p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13032347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146776095","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}