Cancer Imaging最新文献

{"title":"AI-assisted ascites volumetry for prognostic stratification in HCC treated with Atezolizumab/Bevacizumab.","authors":"Maximilian Moos, Daniel Bender, Friedrich Foerster, Lucas Wiesmann, Arndt Weinmann, Roman Kloeckner, Constantin Scholz, André Euler, Aline Mähringer-Kunz, Tobias Bäuerle, Fabian Stoehr, Felix Hahn, Lukas Müller","doi":"10.1186/s40644-026-01105-w","DOIUrl":"https://doi.org/10.1186/s40644-026-01105-w","url":null,"abstract":"<p><strong>Purpose: </strong>Portal hypertension-related parameters are known to have important prognostic implications in patients with hepatocellular carcinoma (HCC). Ascites has consistently been associated with impaired outcomes. However, the prognostic relevance of quantified ascites volume in patients undergoing Atezolizumab/Bevacizumab remains unclear.</p><p><strong>Methods: </strong>90 patients with HCC treated with Atezolizumab/Bevacizumab between 2020 and 2024 were retrospectively included. Ascites was assessed on baseline contrast-enhanced CT imaging using AI-based pre-segmentation followed by manual correction to quantify ascites volume. Overall survival (OS) was analyzed using Kaplan-Meier analysis and Cox regression. Ascites volume was analyzed both as a continuous variable and using predefined volumetric cutoffs (< 613.5 mL and ≥ 613.5 mL).</p><p><strong>Results: </strong>Ascites was present in 39% of patients, with a median ascites volume of 344 mL (IQR 29-929). When analyzed as a continuous variable, ascites volume was significantly associated with impaired OS in univariate analysis (Hazard Ratio (HR) 2.88, p < 0.001) but not after multivariate adjustment (p = 0.214). Stratification by ascites volume revealed a stepwise reduction in OS, with patients without ascites showing the best survival, followed by patients with low-volume ascites (< 613.5 ml), and patients with high-volume ascites (≥ 613.5 ml) demonstrating the poorest outcomes (log-rank p < 0.001). In multivariate analysis, high-volume ascites remained independently associated with impaired OS (HR 4.01, p < 0.001), whereas low-volume ascites did not.</p><p><strong>Conclusion: </strong>Ascites volume showed a threshold-dependent prognostic effect in patients with HCC treated with Atezolizumab/Bevacizumab. Volumetric staging identifies high-volume ascites as an independent marker of poor outcome and may provide more clinically relevant risk stratification than binary ascites assessment.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13471367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148758015","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":"Correction: [¹⁸F]FDG PET/CT performs better than CT in determining the bone biopsy site : randomized controlled clinical trial.","authors":"Yujie Chang, Yifeng Gu, Shunyi Ruan, Shengyuan Xu, Jing Sun, Zhiyuan Jiang, Guangyu Yao, Zhiyu Wang, Hui Zhao","doi":"10.1186/s40644-026-01092-y","DOIUrl":"10.1186/s40644-026-01092-y","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148683254","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":"Machine learning based on preoperative CT for noninvasive prediction of recurrence-free survival in gastrointestinal stromal tumors.","authors":"Lian Zhao, Liming Zhao, Xiaonan Yin, Enyu Yuan, Yili Gu, Xiaohua Zheng, Bing Wu, Yuan Yin, Xijiao Liu","doi":"10.1186/s40644-026-01103-y","DOIUrl":"10.1186/s40644-026-01103-y","url":null,"abstract":"<p><strong>Objective: </strong>Clinicians need a reliable, noninvasive tool that can predict the risk of gastrointestinal stromal tumor (GIST) recurrence preoperatively. We aimed to develop a machine learning model based on preoperative contrast-enhanced CT (CECT) to predict recurrence-free survival (RFS) in GIST patients who underwent radical resection.</p><p><strong>Materials and methods: </strong>A total of 192 patients with intermediate- and high-risk GISTs who underwent radical resection and subsequently received adjuvant imatinib were included, with a minimum follow-up duration of 24 months. A machine learning model (Model_radiomic) based on preoperative CECT was built to predict RFS, which was compared with the Armed Forces Institute of Pathology (AFIP) index (Model_AFIP). The C-index and time-dependent receiver operating characteristic curves were estimated via the Kaplan-Meier method and compared via the log-rank test.</p><p><strong>Results: </strong>The C-index values of the Model_radiomic in the training, testing and validation cohorts ranged from 0.678 to 0.763, whereas the areas under the curves (AUCs) at 3 and 5 years were 0.744-0.790 and 0.703-0.833, respectively. The C-index values of Model_AFIP in the above datasets were 0.539-0.660, and the AUCs at 3 and 5 years were 0.497-0.649 and 0.503-0.630. Model_radiomic demonstrated a higher C-index than Model_AFIP did in the training and external validation cohorts (p = 0.010 and 0.042, respectively). Model_radiomic presented a greater AUC value than Model_AFIP did at the 5th year in the training cohort (p = 0.009).</p><p><strong>Conclusion: </strong>We found the machine learning-based preoperative CECT performed better than the AFIP index in prediction of RFS of GIST patients, especially at the 5th year, predicting recurrence risk in patients who underwent radical resection and receiving adjuvant therapy. This model may serve as a non-invasive tool to identify high-risk individuals who require more intensive surveillance and personalized management following radical resection.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13545761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891199","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 of a nomogram for predicting extramural venous invasion in colon cancer using spectral CT multiparameter imaging.","authors":"Wen Zhao, Zhaowu Shen, Gangjing Ge, Xiaoying Liu, Chao Peng, Fang Rui, Cong Huang, Haiyan Yang, Rui Zhang, Jie Jiang","doi":"10.1186/s40644-026-01098-6","DOIUrl":"10.1186/s40644-026-01098-6","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a predictive model based on spectral CT-derived parameters for preoperative assessment of extramural venous invasion (EMVI) in patients with colon cancer.</p><p><strong>Methods: </strong>This retrospective study included 235 patients with pathologically confirmed colon cancer from two medical centers between September 2022 and July 2024 (192 from center 1 and 43 from center 2). Virtual monoenergetic images (VMIs) ranging from 40 to 120 keV (20 keV increments) were evaluated to identify the optimal energy level based on tumor CT value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). On the optimal VMI, spectral CT quantitative parameters were measured in the arterial, venous, and delay phases, and dynamic multiphase parameters were calculated. Patients from center 1 were randomly divided into a training cohort and an internal testing cohort at a 7:3 ratio. The nomogram model was developed using the training cohort and subsequently evaluated in the internal testing cohort and an independent external validation cohort from center 2.</p><p><strong>Results: </strong>(1) The 60 keV VMI demonstrated superior image quality, showing significantly higher tumor CT value, SNR, and CNR compared with other monoenergetic images (all P < 0.05). (2) Compared with EMVI negative patients, positive patients exhibited significantly higher values of ΔHU40 _{(delay-arterial)}, ΔHU60 _{(delay-arterial)}, ΔIC _{(delay-arterial)}, delay enhancement fraction (DEF), and ΔZeff _{(venous-arterial)} (all P < 0.05). Multivariable analysis further identified these five parameters as independent predictors of EMVI. (3) A nomogram was constructed based on these parameters and demonstrated AUCs of 0.93, 0.91, and 0.84 in the training, testing, and external validation cohorts, respectively.</p><p><strong>Conclusions: </strong>The 60 keV VMI provides improved lesion delineation in colon cancer. A nomogram incorporating dynamic multiphase spectral CT parameters enables accurate preoperative prediction of EMVI and may support individualized clinical decision-making.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148966266","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":"CEUS with VEGFR2-targeted microbubbles for monitoring of early immunotherapy effects in a colorectal cancer model.","authors":"Felix L Herr, Jonathan K Stock, Verena Gräfin Zu Solms-Baruth, Larissa V Blume, Sandra Kloiber-Langhorst, Heidrun Hirner-Eppeneder, Jennifer Stueckl, Barbara Akla, Jens Ricke, Thomas Geyer, Wolfgang Kunz, Maurice M Heimer, Dirk-Andre Clevert, Melissa J Antons, Clemens C Cyran","doi":"10.1186/s40644-026-01101-0","DOIUrl":"10.1186/s40644-026-01101-0","url":null,"abstract":"<p><strong>Background: </strong>To evaluate contrast-enhanced ultrasound (CEUS) with VEGFR2-targeted microbubbles for longitudinal monitoring of early treatment effects during combined anti-PD-L1/anti-CTLA-4 immunotherapy in a murine colorectal cancer model.</p><p><strong>Methods: </strong>Murine colorectal cancer allografts (CT26) were established subcutaneously in 29 female Balb/c mice (therapy n = 15; control n = 14). Baseline CEUS with VEGFR2-targeted microbubbles was performed on day 7. The therapy group received intraperitoneal anti-PD-L1 and anti-CTLA-4 antibodies between days 7-15; controls received sham treatment. Follow-up CEUS was performed on days 14 and 21. Tumor perfusion (WiAUC) and VEGFR2 binding (SI_8min, SI_10min) were quantified, and immunohistochemistry assessed CD8, Ki-67, TUNEL, CD31, and VEGFR2.</p><p><strong>Results: </strong>At FU1, WiAUC was significantly lower in the therapy group compared with controls (4,029 ± 61 vs. 6,391 ± 412; p < 0.001), and remained significantly lower at FU2 (1,028 ± 27 vs. 2,049 ± 39; p < 0.001). VEGFR2-targeted microbubble binding was consistently lower under therapy. At FU1, SI_8min was 407 ± 11 vs. 626 ± 13 and SI_10min was 409 ± 10 vs. 634 ± 28 (both p < 0.001). At FU2, SI_8min was 106 ± 6 vs. 207 ± 4 and SI_10min was 107 ± 5 vs. 207 ± 4 (both p < 0.001). Immunohistochemistry confirmed higher apoptosis and tumor-infiltrating lymphocytes, and lower proliferation, microvascular density, and VEGFR2 expression in treated tumors (all p < 0.05).</p><p><strong>Conclusions: </strong>VEGFR2-targeted CEUS enabled longitudinal monitoring of early response to dual anti-PD-L1/anti-CTLA-4 immunotherapy in colorectal cancer. Treated tumors showed lower perfusion and VEGFR2-targeted binding, paralleled by reduced vessel density and VEGFR2 expression as well as increased CD8 infiltration and apoptosis. These findings support VEGFR2-targeted CEUS as a promising noninvasive imaging biomarker for monitoring early immunotherapy-associated vascular changes.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13428429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148648305","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":"Intratumor habitat aware deep learning framework for preoperative lymph node metastasis prediction in laryngeal carcinoma.","authors":"Wencan Wang, Lingyu Zhang, Junhong Zhang, Xinyu Li, Wenfeng Wei, Li Liang, Shiwen Li, Haomiao Ma, Xuhui Zhao, Qian Zhang, Huina Guo, Yanhong Hao, Hua Zhang, Feng Gao, Jianxin Zhang, Lingjie Wang","doi":"10.1186/s40644-026-01096-8","DOIUrl":"10.1186/s40644-026-01096-8","url":null,"abstract":"<p><strong>Objectives: </strong>To develop an intratumor heterogeneity (ITH)-aware deep learning (DL) framework for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC).</p><p><strong>Methods: </strong>This multi-center retrospective study, which included 381 patients, proposed a ITH-aware DL framework integrated with adaptive habitat mapping to automatically decode spatial ITH from primary tumor contrast-enhanced CT images.The predictive performance of the ITH-aware DL model was compared against conventional radiomics and 3D DL model. A stacking ensemble model combining these three methods was also constructed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). The prognostic value for overall survival (OS) stratification was evaluated.</p><p><strong>Results: </strong>The ITH-aware DL model achieved superior preoperative LNM prediction in external testing (AUC: 0.745-0.766), outperforming both radiomics (AUC: 0.682-0.694) and 3D DL models (AUC: 0.718-0.732). The stacking ensemble integrating these models attained the highest diagnostic accuracy (AUC: 0.786-0.804). Critically, both the ITH-aware DL and stacking models effectively stratified patients into distinct risk groups with significantly different OS outcomes.</p><p><strong>Conclusion: </strong>The proposed ITH-aware DL framework provides a robust, automated tool for preoperative LNM prediction in LSCC. By enabling accurate risk stratification, it holds significant potential to inform personalized surgical planning and improve treatment strategies.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13536815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148878847","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":"Provider and patient perceptions on the usage of whole-body magnetic resonance imaging in oncology: a scoping review.","authors":"Nazm Berry, Dow-Mu Koh, Anne Miles","doi":"10.1186/s40644-026-01093-x","DOIUrl":"https://doi.org/10.1186/s40644-026-01093-x","url":null,"abstract":"<p><p>Whole-body MRI (WB-MRI) is increasingly used for early diagnosis, cancer staging and treatment monitoring, but adoption remains sub-optimal. The aim of this scoping review was to map research on patient and provider perspectives of WB-MRI in oncology, and identify methodological limitations and evidence gaps. The review followed the JBI methodological framework and the PRISMA-ScR checklist. Five electronic databases were searched from inception to 29th March 2025: Cochrane Library, PubMed, APA PsycINFO, APA Psych Articles, and Academic Search Complete. Screening, data extraction, and quality assessment were performed by two authors independently, using COVIDENCE software, with results summarized via narrative synthesis. Twenty-eight studies met eligibility criteria, covering four distinct populations: general population (n = 8), people with cancer pre-disposition syndromes (n = 7), or suspected or confirmed cancer (n = 10), and radiologists (n = 3). Few studies examined beliefs about WB-MRI. Limited evidence shows awareness of WB-MRI attributes, such as the absence of ionising radiation, predicts preferences among cancer patients. Research on radiologists shows low levels of WB-MRI adoption in the USA and UK, attributed to limited scanner availability and a lack of trained radiologists. Among patients, uptake is modest (60%) in both population cohort studies and those recommended for surveillance, but the role of scan beliefs in predicting uptake has not been explored. Undergoing WB-MRI, or being notified of incidental findings, does not appear to cause adverse psychological consequences, but cancer specific concerns were not always assessed and research not always adequately powered. Evidence of provider and patient perspectives towards WB-MRI is limited and of varying quality. The development of validated measures examining WB-MRI beliefs and experience would enhance research and facilitate future data synthesis.Clinical trial number Not applicable.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"26 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223242","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":"Multimodal PET-MR segmentation for glioblastoma: complementarity for treatment planning and recurrence definition.","authors":"Alejandro Mora-Rubio, Dejan Kuhn, Carolina Bravo-Vergara, Tobias Fechter, Melissa Mauer, Maria Beser-Robles, Irene Marín Radoszynski, Rocío Catalán Flores, Johannes Fostitsch, Pablo García Verdú, Michael Mix, Dimos Baltas, Luis Martí-Bonmatí, Anca L Grosu, Ilinca Popp, Montserrat Carles","doi":"10.1186/s40644-026-01086-w","DOIUrl":"10.1186/s40644-026-01086-w","url":null,"abstract":"<p><strong>Background: </strong>In patients with Glioblastoma (GBM), Magnetic Resonance (MR) is used for tumour diagnosis and treatment planning. Positron Emission Tomography (PET) with O-(2)-18 F-Fluoroethyl-L-Tyrosine (FET) has been recommended to distinguish local recurrence from radiogenic alterations. However, clinical practice remains hindered by the time and expertise required for tumour and organs-at-risk (OARs) segmentation and the limited evidence of the added value of PET and its restricted availability across clinical centres. This study presents automatic segmentation models and a comprehensive evaluation of PET/MR complementary biological information for recurrent disease definition.</p><p><strong>Methods: </strong>The nnU-Net was employed for segmentation using manually defined contours on 1,610 patients from 33 institutions. Model performance was evaluated by Dice-Sørensen-Coefficient (DSC). PET/MR recurrence complementarity was evaluated in 185 patients by Wilcoxon-Signed-Rank test (WSRT), DSC and radiomic features (RF). In RF analysis, MR-Enhancing subregions were classified as MR∩PET or MR-Only, based on overlap with PET. For MR-RF showing significant MR∩PET/MR-Only differences (WSRT), discrimination was further assessed by classifying RF for 3 × 3 × 3-voxel subregions within MR-Enhancing in two volumes (greater/less than the RF cohort median) and evaluating Positive-Predictive-Value and Sensitivity with MR∩PET/MR-Only.</p><p><strong>Results: </strong>Models' performance in the test set resulted in DSC(MR-Enhancing) = 0.76 ± 0.24, DSC(MR-Edema) = 0.69 ± 0.23, DSC(PET-Uptake) = 0.71 ± 0.20, DSC(Planning-Target-Volume) = 0.93 ± 0.05, DSC(OARs) = 0.70 ± 0.13. Manual MR and FET-PET based GBM recurrence delineations differed significantly in size (p = 0.0497) and location (DSC = 0.45 ± 0.20). From the 37 MR-RF showing significant differences between MR∩PET and MR-Only (p < 0.05), none of the MR-Enhancing based RF-maps allowed spatial identification of PET findings (positive-predictive-value and sensitivity < 0.6).</p><p><strong>Conclusions: </strong>The resulted segmentation models could facilitate PET/MR integration in GBM treatment. PET/MR comparison supports the complementarity of FET-PET.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148535034","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":"Habitat-based imaging and peritumoral radiomics on ultrasound images for predicting lymphovascular invasion in breast invasive ductal carcinoma: a two-center study.","authors":"Taixia Liu, Guojia Zhao, Wei Wei, Qingling Zhang, Jing Wu, Xuying Chen, Dong Liu, Xiangming Zhu","doi":"10.1186/s40644-026-01088-8","DOIUrl":"10.1186/s40644-026-01088-8","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate the feasibility of employing habitat-based radiomic distributions in ultrasound (US) images to quantitatively characterize intratumoral heterogeneity. It also explored the potential of this approach to predict lymphovascular invasion (LVI) in breast invasive ductal carcinoma (IDC) patients and to identify the optimal extent of multiple peritumoral regions.</p><p><strong>Methods: </strong>A total of 408 women diagnosed with IDC from January 2020 to October 2023 were enrolled in this retrospective cohort study from two medical centers. Intratumoral areas were partitioned into four distinct habitat areas using K-means cluster analysis, while peritumoral regions were expanded at increments of 2, 4, and 6 mm. Radiomic features were independently extracted from the intra- and peri-tumoral areas, and habitat subregions for developing predictive models. These models incorporated three machine learning classifiers: Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), respectively. We subsequently established an integrated model encompassing intra- and peri-tumoral areas, and habitat radiomic features, and clinicopathological factors. The model performance was assessed through receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA). Finally, SHapley Additive exPlanations (SHAP) and nonogram were applied to enhance model interpretability.</p><p><strong>Results: </strong>The Random Forest model exhibited superior performance in terms of the area under the curve (AUC) values of 0.861 (95% CI: 0.809-0.912), 0.832 (95% CI: 0.746-0.919), and 0.810 (95% CI: 0.684-0.935) for the training, validation, and test sets, separately. Additionally, the peri-2 mm model surpassed the performance of the other models (peri-4 mm, peri-6 mm) in LVI prediction. The integrated model, encompassing peri-2 mm features, clinicopathological factors, and habitat models, achieved robust predictive performance with AUC values of 0.940 (95% CI: 0.907-0.973), 0.924 (95% CI: 0.875-0.973), and 0.852 (95% CI: 0.732-0.972) for each respective set.</p><p><strong>Conclusion: </strong>The integrated model yields the improved predictive performance in predicting LVI status, and the model offer a reliable and feasible preoperative prediction method to enhance the clinical management and therapeutic planning for IDC patients.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13663170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148497194","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":"Magnetic resonance biomarkers in detection and characterisation of ovarian cancer: is magnetic resonance spectroscopy ready for clinical use?","authors":"Marianthi Vasiliki Papoutsaki, Marc Eric Miquel, Joe Martin, Katherine Ordidge, Anju Sahdev","doi":"10.1186/s40644-026-01087-9","DOIUrl":"10.1186/s40644-026-01087-9","url":null,"abstract":"<p><p>Ovarian cancer is a major cause of cancer-related mortality in women, largely due to late-stage diagnosis and challenges in preoperative characterisation of adnexal masses. Multiparametric MRI, incorporating diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) imaging, has improved lesion characterisation and led to the development of the Ovarian-Adnexal Reporting and Data System (O-RADS) MRI for standardised malignancy risk stratification. Despite its demonstrated clinical value, O-RADS MRI relies on qualitative assessment and lacks metabolic information. Magnetic resonance spectroscopy (MRS) provides non-invasive insight into tissue biochemical composition. It offers potential for differentiating benign and malignant ovarian lesions that are indeterminate on ultrasound or MRI. This review discusses current functional MRI biomarkers within the O-RADS framework and evaluates the emerging role of MRS as potential biomarker for ovarian cancer characterisation. Quantitative metrics derived from DWI, including apparent diffusion coefficient (ADC) and histogram analyses, and semi-quantitative and quantitative DCE parameters, have demonstrated utility in differentiating benign, borderline, and malignant lesions. Variability in acquisition protocols, post-processing, and region-of-interest selection, along with motion artifacts and magnetic field inhomogeneities, limits routine clinical implementation. Advanced diffusion models and whole-lesion histogram approaches improve lesion characterization, particularly in indeterminate O-RADS MRI 4 masses, but no single parameter reliably discriminates all lesion types. Early MRS studies identified metabolites such as choline (Cho), lactate (Lac), lipids (Lip), and N-acetylaspartate (NAA) as potential malignancy indicators, with Cho/Cr ratio frequently investigated. Diagnostic performance has been inconsistent due to small cohorts, variable metabolite thresholds, and heterogeneity in acquisition and analysis protocols. Technical limitations, including low signal-to-noise ratio, motion artifacts, field inhomogeneities, and voxel mislocalisation, further limit clinical adoption. Standardized MRS acquisition, voxel localization, shimming, denoising, and post-processing are needed. Community-endorsed consensus guidelines, similar to those in brain MRS, could improve reproducibility, comparability, and reliability. Currently, MRS remains investigational but may serve as a complementary biomarker for O-RADS MRI 4 lesions. Harmonised protocols and consensus guidelines are required to support its clinical translation.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13656407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148454718","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}