Cancer Imaging最新文献

{"title":"Quantitative assessment of tumor burden in multiple myeloma using MY-RADS from whole-body MRI: comparison with established prognostic biomarkers.","authors":"Xing Xiong, Zhi Yan, Jia Wang, Yu He, Zhenyan Zhou, Dawei Li, Yue Xiong, Changyou Zhou","doi":"10.1186/s40644-026-01076-y","DOIUrl":"10.1186/s40644-026-01076-y","url":null,"abstract":"<p><strong>Background: </strong>To explore the value of MY-RADS from whole-body MRI in assessing tumor burden in MM with reference to established prognostic biomarkers.</p><p><strong>Methods: </strong>108 consecutive newly diagnosed MM patients were retrospectively enrolled. Bone marrow (BM) infiltration patterns were determined by MRI presentation. The whole-body total burden score using MY-RADS, apparent diffusion coefficient (ADC), and fat fraction (FF) of representative background BM were measured. Spearman correlation analysis was used for correlating these parameters against plasma cell infiltration ratio, and independent sample t-test was used to evaluate the differences between different International Staging System (ISS) and free light-chain ratio groups.</p><p><strong>Results: </strong>For diffuse, salt-and-pepper and normal patterns, patients with advanced ISS stage and high FLC-ratio had markedly lower FF and higher ADC of representative background BM. They also correlated well with plasma cell infiltration ratio. In contrast, the total burden score did not show significant difference between groups. For focal pattern, there was no significant difference of ADC and FF between groups. They didn't correlate with plasma cell infiltration ratio as well. While, patients with advanced ISS stage and high FLC-ratio had markedly higher MY-RADS total burden score and correlated well with plasma cell infiltration ratio. For mixed pattern, ADC for L3, FF for L3 and the total burden score all showed statistical difference and correlated well with plasma cell infiltration ratio.</p><p><strong>Conclusion: </strong>The present study supports the applicability of MY-RADS as a surrogate biomarker when established prognostic biomarkers were not accessible conveniently.</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/PMC13536559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148293487","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":"Node-RADS in cervical cancer: a multi-reader agreement and diagnostic performance study.","authors":"Shifang Tan, Xueyan Liu, Bairu Li, Tingting Bao, Lingjie Zhang, Zhexuan Yang, Shaomin Li, Tian Ren, Meiying Cheng, Junjie Liao, Xiaoan Zhang, Xin Zhao","doi":"10.1186/s40644-026-01072-2","DOIUrl":"10.1186/s40644-026-01072-2","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the agreement and diagnostic performance of the Node Reporting and Data System 1.0 (Node-RADS) for preoperative lymph node staging in cervical cancer across readers with different experience levels.</p><p><strong>Methods: </strong>This retrospective study enrolled 439 consecutive cervical cancer patients who underwent preoperative MRI and lymph node dissection. Target nodes were pre-specified by a most experienced consultant radiologist to unify the assessment objects. Four readers (two senior with 9-11 years of experience, two junior with 3-5 years of experience) independently assigned Node-RADS scores, blinded to histopathology. Inter-reader agreement and between-group agreement were assessed using weighted and Cohen's kappa. Diagnostic performance was evaluated against histopathology as reference standard.</p><p><strong>Results: </strong>Senior readers achieved near-perfect agreement for Node-RADS scores (k = 0.988), nodal status (k = 0.959) and all individual morphological parameters (k = 0.847-0.967). Junior readers showed moderate to substantial agreement (k = 0.554-0.754). Although junior readers requiring consensus more frequently (7.1% vs. 1.4%, p < 0.01), the between-group agreement for nodal status was substantial (k = 0.783). For nodal metastasis detection, senior readers demonstrated higher diagnostic performance (AUC 0.868; sensitivity 76.4%; specificity 97.3%) compared to junior readers (AUC 0.758; sensitivity 54.6%; specificity 97.0%), and both groups presented favorable diagnostic efficacy overall.</p><p><strong>Conclusions: </strong>Inter-reader agreement for nodal status was almost perfect among senior readers and substantial among junior readers. Diagnostic performance was slightly better in the senior group, suggesting an influence of reader experience.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13520168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148270427","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 deep learning combined with radiomics for the preoperative prediction of lymphovascular invasion in patients with bladder cancer.","authors":"Chang Chen, Zhengye Tan, Lingkai Cai, Haonan Chen, Xiaotong Liu, Bo Liang, Meihua Jiang, Gongcheng Wang, Qiang Shao, Hongliang Que, Xuping Jiang, Yuheng Zhang, Chenghao Wang, Rongjie Bai, Hao Yu, Xiao Yang, Qiang Lu, Yuhao Lin, Qiang Cao","doi":"10.1186/s40644-026-01070-4","DOIUrl":"10.1186/s40644-026-01070-4","url":null,"abstract":"<p><strong>Background: </strong>Lymphovascular invasion (LVI) signifies poor prognosis in bladder cancer, yet reliable preoperative prediction remains challenging, limiting personalized treatment planning.</p><p><strong>Methods: </strong>In this multi-center retrospective study, 543 bladder cancer patients were enrolled. Of these, 473 patients from two centers were randomly split into training and internal test sets at an 8:2 ratio, while 70 patients from six additional centers constituted an independent external test set. After tumor and peritumoral segmentation, a hybrid model that integrates deep learning and radiomics features was developed. The hybrid model was compared against ten baseline models, including image-only and radiomics-only deep learning models, as well as radiomics-based machine learning approaches. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) with five-fold cross-validation. Prognostic value was evaluated by Kaplan-Meier survival analysis with the log-rank test.</p><p><strong>Results: </strong>The hybrid model achieved AUCs of 0.77 (internal test) and 0.75 (external test), surpassing all baseline models. The model-predicted LVI status significantly stratified overall survival in the training set (p < 0.001) and the internal test set (p = 0.003), with a non-significant trend observed in the external test set (p = 0.151).</p><p><strong>Conclusion: </strong>The proposed non-invasive hybrid model can accurately predict LVI status preoperatively and demonstrates prognostic potential, thereby aiding risk stratification in bladder cancer.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13483486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148223262","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":"Risk stratification of advanced extranodal extension in retropharyngeal lymph nodes for nasopharyngeal carcinoma: a multi-center study.","authors":"Junyi Liu, Zhenzhang Chen, Bolin Lu, Tong Bu, Tong Jin, Changyong He, Hongping Zhou, Yinsu Zhu, Shengfu Huang, Xiaojing Du, Jin Gao, Xiaozhong Chen, Xia He, Lirong Wu","doi":"10.1186/s40644-026-01067-z","DOIUrl":"10.1186/s40644-026-01067-z","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to refine risk stratification of advanced extranodal extension (ENE) in retropharyngeal lymph nodes (RLNs) and evaluate its prognostic significance.</p><p><strong>Methods: </strong>A total of 1,373 non-metastatic NPC patients at three centers between 2011 and 2021 were retrospectively enrolled. For advanced RLN ENE, we documented all involved structures, maximum axial diameter (MAD), presence of lymph node necrosis (LNN), and bilaterality. High-risk structures were identified using adjusted hazard ratios and LASSO-Cox models. Multivariable Cox models and Kaplan-Meier analyses were used to evaluate associations with survival outcomes.</p><p><strong>Results: </strong>Of the 1,373 patients, 1061 had RLN metastasis, including 168 with advanced RLN ENE. Involvement of interval structures, the lower cranial nerves region, internal jugular vein, and longus capitis muscle was classified as high-risk, whereas isolated internal carotid artery involvement was low-risk. Advanced RLN ENE with high-risk structure involvement or LNN was defined as high-risk RLN ENE. High-risk RLN ENE was an independent adverse prognostic factor for NPC. N1/N2 patients with high-risk RLN ENE showed survival comparable to N3 disease.</p><p><strong>Conclusions: </strong>High-risk RLN ENE is an independent adverse prognostic factor in NPC. N1/N2 patients with high-risk RLN ENE exhibit N3-like survival, supporting its potential incorporation into future refinements of N staging.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13483748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148223399","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":"[¹⁸F] PSMA-3Q PET/CT radiomics at 45% SUVmax threshold: predicting post-surgical ISUP grade ≥ 4 and extraprostatic extension for noninvasive stratification in prostate cancer management.","authors":"Jianbo Cui, Yachao Liu, Guanyun Wang, Xiaodan Xu, Dong Zhao, Ran Xue, Wenjing Chen, Ruimin Wang, Baixuan Xu","doi":"10.1186/s40644-026-01050-8","DOIUrl":"10.1186/s40644-026-01050-8","url":null,"abstract":"<p><strong>Objective: </strong>To systematically evaluate radiomic features extracted from [¹⁸F] PSMA-3Q PET/CT using 40%, 45%, and 50% SUVmax thresholds for their ability to predict post-surgical International Society of Urological Pathology (psISUP) grading and extraprostatic extension (EPE) in prostate cancer and ultimately develop an optimal threshold-based predictive model.</p><p><strong>Materials & methods: </strong>This retrospective study included 243 prostate cancer patients undergoing [¹⁸F] PSMA-3Q PET/CT before radical prostatectomy. Patients were chronologically divided into training (n = 190) and test (n = 53) cohorts, with stratification by prostate-specific antigen density (PSAD < 0.25 vs. ≥ 0.25 ng/mL). Radiomics features were extracted using 40%, 45%, and 50% SUVmax thresholds. Nine machine learning algorithms developed integrated PET/CT radiomics-clinical models (PC40, PC45, PC50) and PET radiomics-clinical models (P40, P45, P50). Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive and negative predictive values.</p><p><strong>Results: </strong>Among the models constructed with 40%, 45%, and 50% SUVmax thresholds, the PC45 model (PET/CT radiomics + clinical parameters) performed best. For predicting psISUP grade ≥ 4, its test set AUC reached 0.917 (95% CI: 0.828-0.985), with 0.800 sensitivity and 0.921 specificity. For EPE prediction, the test set AUC was 0.772(95% CI: 0.633-0.883), sensitivity 0.700 and specificity 0.727. It showed stable performance across PSAD subgroups and higher net clinical benefit.</p><p><strong>Conclusion: </strong>The [¹⁸F] PSMA-3Q PET/CT radiomics model with a 45% SUVmax threshold may facilitate relatively accurate noninvasive prediction of psISUP grade and EPE. These findings suggest its potential value as a promising preoperative tool for precision prostate cancer management, and provide preliminary evidence supporting its stable and balanced performance as a noninvasive decision‑making aid, pending further validation in larger cohorts.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13459231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148194633","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":"Use of contrast-enhanced mammography for preoperative prediction of lymphovascular invasion status in invasive breast cancer.","authors":"Liya Gong, Muzi Wen, Ying Ye, Zhiyue Zhao, Junyan Wen, Ziqi Wu, Yanyu Hao, Xiaomei Huang","doi":"10.1186/s40644-026-01065-1","DOIUrl":"10.1186/s40644-026-01065-1","url":null,"abstract":"<p><strong>Background: </strong>To investigate the use of contrast-enhanced mammography (CEM) for preoperative prediction of lymphovascular invasion (LVI) status in invasive breast cancer.</p><p><strong>Methods: </strong>A total of 243 female patients diagnosed with invasive breast cancer (median age: 49 years; range: 27-77 years) who received preoperative CEM examination in our hospital between September 2018 and February 2024 were retrospectively collected and analyzed. The study population were chronologically divided into training and test datasets in an approximate ratio of 7:3. LVI status was determined using postoperative histopathologic examination. CEM features were analyzed on the low energy and the recombined images. To identify independent predictors for LVI status, univariable and multivariable logistic regression analyses were performed on CEM and clinicopathologic features. Logistic regression and six machine learning methods were used to construct prediction models in the training dataset, and their performance were evaluated with ROC curve in the test dataset.</p><p><strong>Results: </strong>In training and test datasets, the rates of LVI-positive were 39% (67 of 172) and 34% (24 of 71), respectively. High Ki67 index, BI-RADS category 5, breast composition category c/d, axillary adenopathy, mild to marked background parenchymal enhancement level, and lesion with complete enhancement or enhancement extending on CEM images were significantly correlated with LVI-positive (all P < 0.05) and were incorporated to construct prediction models. The AUCs of seven prediction models were in the range of 0.713-0.850 in the test datasets, where the logistic regression model yielded an AUC of 0.835 (95%CI: 0.717-0.924), showing similar or higher AUC than the six machine learning models.</p><p><strong>Conclusions: </strong>CEM could be useful for preoperative noninvasive prediction of LVI status in invasive breast cancer. The prediction model integrating contrast-enhanced mammography features and Ki67 index may serve as a complementary tool to assist clinicians in preoperative prediction of lymphovascular invasion status in patients with invasive breast cancer.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13459276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148194718","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":"Long-term prognostic value of ¹⁸F-FLT PET/CT versus ¹⁸F-FDG PET/CT in locally advanced oesophageal squamous cell carcinoma: a prospective study.","authors":"Wenyan Gao, Jingwen Peng, Xue Chen, Yi Wang, Shunji Zhi, Lei Wu, Gang Wan, Zhuzhong Cheng, Qifeng Wang, Wei Zhang","doi":"10.1186/s40644-026-01056-2","DOIUrl":"10.1186/s40644-026-01056-2","url":null,"abstract":"<p><strong>Background: </strong>Accurate prognostication in locally advanced oesophageal squamous cell carcinoma (LA-ESCC) remains a significant clinical challenge. This prospective study aimed to compare the predictive value of fluorine-18 fluorodeoxyglucose (¹⁸F-FDG) and fluorine-18 fluorothymidine (¹⁸F-FLT) positron emission tomography/computed tomography (PET/CT) regarding long-term clinical outcomes.</p><p><strong>Methods: </strong>Between June 2018 and July 2019, 31 patients with LA-ESCC were prospectively enrolled and underwent both ¹⁸F-FDG and ¹⁸F-FLT PET/CT scans prior to or during initial therapy. Quantitative parameters, including maximum standardized uptake value (SUV _max), minimum SUV (SUV _min), mean SUV (SUV _mean), tumour metabolic volume (MTV), total lesion glycolysis (TLG), and total lesion ¹⁸F-FLT expression (TLT), were measured for primary tumours and regional lymph nodes. Progression-free survival (PFS) and overall survival (OS) were analysed using Cox univariate regression, receiver operating characteristic (ROC) curve analysis, and Kaplan-Meier survival estimates.</p><p><strong>Results: </strong>In ¹⁸F-FLT PET/CT, primary tumour parameters (SUV _max, SUV _mean, TLT) and all lymph node parameters significantly distinguished between favourable and poor PFS groups (all P <0.05). Primary tumour SUV _max, SUV _mean, and all lymph node parameters demonstrated a negative correlation with both PFS and OS. Specifically, lymph node SUV max (PFS: hazard ratio (HR)=1.65, 95% confidence interval (CI):1.23-2.19, P =0.002; OS: HR = 1.81, 95% CI:1.29-2.55, P <0.001) and SUV mean showed strong predictive value. In contrast, for ¹⁸F-FDG PET/CT, only lymph node MTV differed significantly between PFS groups (P =0.03), with no parameters showing significant prognostic correlation. ROC analysis indicated that ¹⁸F-FLT lymph node SUV _max and SUV _mean yielded the highest predictive accuracy for PFS (area under the curve (AUC): 83.8% and 84.2%, respectively) and OS (AUC: 79.3% for both).</p><p><strong>Conclusions: </strong>¹⁸F-FLT PET/CT may offer superior prognostic value compared to ¹⁸F-FDG PET/CT for long-term survival prediction in LA-ESCC. Parameters derived from metastatic lymph nodes, particularly SUV _max and SUV _mean, may serve as potential biomarkers for poor clinical outcomes, independent of imaging timing.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13459923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148175501","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 ¹⁸F-FDG PET/CT metabolic parameters in patients with non-small cell lung cancer who achieved a pathological complete response after neoadjuvant chemoimmunotherapy.","authors":"Ye Tao, Rui Guo, Xiang Li, Xinrun Cui, Yaqi Wang, Haoxuan Du, Zengjin Wen, Shi Yan, Nan Li, Nan Wu","doi":"10.1186/s40644-026-01057-1","DOIUrl":"10.1186/s40644-026-01057-1","url":null,"abstract":"<p><strong>Background: </strong>Although pathological complete response (pCR) is generally associated with favorable survival outcomes, 10-15% of patients who achieve pCR after neoadjuvant chemoimmunotherapy still relapse within 3 years. This study aimed to explore the prognostic value of clinical features and metabolic parameters derived from 18-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT, hereafter referred to as PET/CT).</p><p><strong>Methods: </strong>We retrospectively analyzed 121 non-small cell lung cancer (NSCLC) patients with confirmed pCR of both primary tumors and lymph nodes after receiving neoadjuvant chemoimmunotherapy at Peking University Cancer Hospital. Univariate and multivariate Cox regression were used to identify prognostic factors, including clinical features, baseline (pre-neoadjuvant) and second (post-neoadjuvant) metabolic parameters on PET/CT-maximum standardized uptake value (SUVmax), peak standardized uptake value (SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)-as well as their percentage changes prior to surgery.</p><p><strong>Results: </strong>During a median follow-up period of 26 months, 11 patients experienced recurrence or metastasis. The 1-, 2-, and 3-year disease-free survival (DFS) rates were 98%, 92%, and 88%, respectively. PET-defined N2 (HR = 10.32; 95%CI: 1.90-55.99; p = 0.007), squamous cell carcinoma histology (HR = 0.27; 95%CI: 0.07-0.97; p = 0.05), and baseline MTV (HR = 1.02; 95%CI: 1.01-1.03; p = 0.01) were all correlated with DFS among NSCLC patients attaining pCR. When metabolic parameters were dichotomized into high and low groups, those pCR patients with higher metabolic activity had significantly shorter DFS than those with lower activity did. The corresponding hazard ratios for baseline PET/CT parameters were as follows: SUVpeak, 10.17 (95% CI: 1.3-79.51); MTV, 4.26 (95% CI: 1.13-16.1); and TLG, 8.43 (95% CI: 1.08-65.9).</p><p><strong>Conclusion: </strong>Among NSCLC patients achieving pCR after neoadjuvant chemoimmunotherapy, multivariable Cox regression analyses revealed that elevated baseline MTV and PET/CT-defined N2 status were independently associated with poor DFS, while squamous histology was associated with more favorable DFS. These findings highlight that even among pCR patients, baseline metabolic, histologic, and nodal staging characteristics retain important prognostic value.</p><p><strong>Clinical registration number: </strong>This real-world retrospective trial was registered at ClinicalTrials.gov on April 12th, 2025 (NCT06926179).</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148155420","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":"Non-invasive model for predicting future lung metastasis of hepatocellular carcinoma based on imaging heterogeneity analysis.","authors":"Han Wu, Yuxuan Yang, Yuan Chen, Zhenli Liao, Wei Zhao, Guanghui Luo, Kai Tang, Yurong Jiang, Qiyang Li, Ziyao Jia, Mengfan Pan, Wanli Huang, Sirui Fu, Chongyang Duan, Liang Chen","doi":"10.1186/s40644-026-01051-7","DOIUrl":"10.1186/s40644-026-01051-7","url":null,"abstract":"<p><strong>Background: </strong>Patients with hepatocellular carcinoma (HCC) are at high risk of lung metastasis, which is the only site significantly affecting survival among extrahepatic metastases. However, current HCC lung metastasis researches predominantly focus on biological or pathological heterogeneity through invasive biopsies, which are unsuitable for continuous and dynamic monitoring. Consequently, imaging heterogeneity should also be evaluated.</p><p><strong>Methods: </strong>From five centers, 352 HCC cases were divided into training and test datasets. Combining backward stepwise hazard models with the least absolute shrinkage and selection operator (LASSO) method, a clinical imaging heterogeneity model (CIH-Model) was constructed using clinical indicators and imaging parameters (including tumor location, peritumor, intratumor, and enhancement pattern) and tested the model's discrimination and calibration. Patients were categorized according to CIH-Model scores, and the time to lung metastasis and overall survival (OS) were compared between categories. Multivariate regression analysis was conducted to determine whether subgroup was an independent predictor of OS.</p><p><strong>Results: </strong>The areas under receiver operating characteristic curves (AUC) of the CIH-Model for predicting one-, two- and three-years lung metastasis free survival were 0.78, 0.92, and 0.87 (training) and 0.72, 0.72, and 0.71 (test), respectively, with sufficient calibration in both datasets. Subgroups separated according to the median CIH-Model score showed significant differences in time to lung metastasis (training, hazard ratio[HR]: 6.271, 95% confidence interval [CI]: 3.225-12.200, p < 0.001; test, HR: 2.862; 95% CI: 1.436-5.705, p = 0.002) and OS (training, HR: 3.723; 95% CI: 2.357-5.881, p < 0.001; test, HR: 4.003; 95% CI: 2.321-6.907, p < 0.001). Multivariate regression analysis showed that subgroup by lung metastasis risk was independently related to OS in the training (HR: 0.435; 95% CI: 0.259-0.730, p = 0.002) and test datasets (HR: 0.341; 95% CI: 0.178-0.653, p = 0.001).</p><p><strong>Conclusions: </strong>Assisted by the CIH-Model, patients with HCC at high risk of future lung metastasis could be identified, and survival risk could be distinguished for appropriate monitoring and timely prevention in these patients.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13238124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148161934","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":"Apparent diffusion coefficient and kurtosis parameters show early response to anti-angiogenic therapy in patients with colorectal liver metastases.","authors":"Mihaela Rata, Khurum Khan, David J Collins, Matthew R Orton, James d'Arcy, Nina Tunariu, Maria Antonietta Bali, Ian Chau, Nicola Valeri, David Cunningham, Dow-Mu Koh","doi":"10.1186/s40644-026-01063-3","DOIUrl":"10.1186/s40644-026-01063-3","url":null,"abstract":"<p><strong>Background: </strong>To investigate whether the diffusion kurtosis imaging (DKI) technique can identify tumour response to an anti-angiogenic therapy in a pilot study of patients with RAS-mutant colorectal liver metastases.</p><p><strong>Methods: </strong>This prospective imaging study enrolled 20 participants receiving Regorafenib treatment. A target metastasis > 2 cm in each patient was imaged before and at 15 days after treatment on a 1.5T MR scanner using a coronal, free-breathing DKI protocol. Data were motion-corrected and modelled using both the mono-exponential and DKI models. Median values derived from voxel-wise analysis of the whole delineated tumour were reported for three parameters [apparent diffusion coefficient (ADC, 10^- 3 mm²/s), apparent kurtosis (K, a.u.) and kurtosis-corrected apparent diffusion (D, 10^- 3 mm²/s)] before and after treatment. A 5-patient supplementary cohort was used to assess the repeatability of DKI parameters using the Bland-Altman analysis. Changes in pre- and post-treatment measurements of the three parameters were assessed using Wilcoxon signed-rank tests (P < 0.05 was considered significant). Diffusion weighted imaging (DWI) and DKI parameter correlations were evaluated with Spearman tests. Functional MR parameters were also compared against Response Evaluation Criteria In Solid Tumours v.1.1 (RECIST) evaluations.</p><p><strong>Results: </strong>Significant treatment-induced changes across the cohort were observed for all parameters: K decrease (0.907 vs. 0.786, P < 0.01; 13.3%), D increase (1.256 vs. 1.386 × 10^- 3 mm²/s, P < 0.001; 10.4%) and ADC increase (0.910 vs. 1.035 × 10^- 3 mm²/s, P < 0.001; 13.7%). For both visits, Spearman correlation tests found a moderate negative correlation between K and D, (r=-0.70; P < 0.001 and r=-0.58; P < 0.01) and a strong positive correlation (r = 0.84 and 0.88; P < < 0.001) between ADC and D. The repeatability R was lowest for K (51%), followed by D (12%), and ADC (9.4%). When compared to RECIST v.1.1 evaluations, K identified no clinical responders, whilst D and ADC identified 7/12 and 11/12 responders.</p><p><strong>Conclusions: </strong>Both ADC and DKI parameters showed a significant early cohort change to the anti-angiogenic effects of Regorafenib treatment in RAS-mutant colorectal liver metastases. The ADC parameter performed better than the K parameter in identifying patients benefitting from the treatment.</p><p><strong>Trial registration: </strong>NCT03010722 clinicaltrials.gov; registration date 6^th January 2015.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148161877","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}