Cancer ImagingPub Date : 2026-04-17DOI: 10.1186/s40644-026-01030-y
Ahmed Marey, Hamza Elkeden, Yahia Alrohaibani, Mohamed Khalaf, Jose A Karam, Surena Matin, Peiman Habibiollahi, Bruno C Odisio, Kamran Ahrar, Mohamed E Abdelsalam
{"title":"Percutaneous ablation of T1b renal tumors: a review.","authors":"Ahmed Marey, Hamza Elkeden, Yahia Alrohaibani, Mohamed Khalaf, Jose A Karam, Surena Matin, Peiman Habibiollahi, Bruno C Odisio, Kamran Ahrar, Mohamed E Abdelsalam","doi":"10.1186/s40644-026-01030-y","DOIUrl":"https://doi.org/10.1186/s40644-026-01030-y","url":null,"abstract":"<p><p>Surgical excision is the gold standard treatment for T1b renal cell carcinoma (RCC). However, due to the comorbidities especially in elderly patients, not all are candidates for surgery or prefer surgical approaches. This has raised the interest in minimally invasive ablation therapy for such patient population given that ablation techniques have shown favorable long term oncologic and survival outcomes for T1 a renal tumor. This review article delves into the application of ablation technologies, including percutaneous cryoablation (PCA), radiofrequency ablation (RFA), and microwave ablation (MWA), for managing T1b renal tumors (4.1-7 cm). This review comprehensively explores patient selection, contraindications, clinical evaluation, ablation procedures, imaging modalities, technical considerations, complications, and follow-up care. Additional insights of the efficacy, safety, and outcomes of ablation techniques for treating T1b renal tumors are presented. As the field continues to evolve, understanding the nuances of ablation as an alternative treatment modality for T1b renal tumors becomes essential for optimizing patient care and decision-making.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147716090","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}
Cancer ImagingPub Date : 2026-04-16DOI: 10.1186/s40644-026-01032-w
Deondre D Do, Mariluz Rojo Domingo, Christopher C Conlin, Ian Matthews, Karoline Kallis, Madison T Baxter, Courtney Ollison, Yuze Song, George Xu, Allison Y Zhong, Aditya Bagrodia, Tristan Barrett, Matthew Cooperberg, Felix Feng, Michael E Hahn, Mukesh Harisinghani, Gary M Hollenberg, Juan Javier-Desloges, Sophia C Kamran, Christopher J Kane, Dimitri Kessler, Joshua Kuperman, Kang-Lung Lee, Jonathan Levine, Michael A Liss, Daniel J A Margolis, Paul M Murphy, Nabih Nakrour, Michael A Ohliger, Thomas Osinski, Anthony James Pamatmat, Isabella R Pompa, Rebecca Rakow-Penner, Jacob L Roberts, Karan Santhosh, Ahmed S Shabaik, David Song, Clare M Tempany, Shaun Trecarten, Natasha Wehrli, Eric P Weinberg, Sean Woolen, Anders M Dale, Tyler M Seibert
{"title":"Systematic effects of patient factors and scanner/protocol factors on a Restriction Spectrum Imaging (RSI) quantitative MRI biomarker for prostate cancer.","authors":"Deondre D Do, Mariluz Rojo Domingo, Christopher C Conlin, Ian Matthews, Karoline Kallis, Madison T Baxter, Courtney Ollison, Yuze Song, George Xu, Allison Y Zhong, Aditya Bagrodia, Tristan Barrett, Matthew Cooperberg, Felix Feng, Michael E Hahn, Mukesh Harisinghani, Gary M Hollenberg, Juan Javier-Desloges, Sophia C Kamran, Christopher J Kane, Dimitri Kessler, Joshua Kuperman, Kang-Lung Lee, Jonathan Levine, Michael A Liss, Daniel J A Margolis, Paul M Murphy, Nabih Nakrour, Michael A Ohliger, Thomas Osinski, Anthony James Pamatmat, Isabella R Pompa, Rebecca Rakow-Penner, Jacob L Roberts, Karan Santhosh, Ahmed S Shabaik, David Song, Clare M Tempany, Shaun Trecarten, Natasha Wehrli, Eric P Weinberg, Sean Woolen, Anders M Dale, Tyler M Seibert","doi":"10.1186/s40644-026-01032-w","DOIUrl":"https://doi.org/10.1186/s40644-026-01032-w","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697893","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":"Magnetic resonance imaging-based radiomics of mesorectum for predicting extramural venous invasion in patients with rectal cancer: a bi-centric study.","authors":"Jia He, Xianzheng Tan, Huashan Lin, Jing Fang, Diejuan Liu, Jiabei Liu, Xiang Feng, Xiaoping Yu, Peng Liu","doi":"10.1186/s40644-026-01020-0","DOIUrl":"https://doi.org/10.1186/s40644-026-01020-0","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate a magnetic resonance imaging (MRI)-based radiomics model of the mesorectum for predicting extramural venous invasion (EMVI) in patients with rectal cancer (RC).</p><p><strong>Methods: </strong>A retrospective study included 238 patients with RC from two hospitals between May 2020 and March 2023. Patients were divided into a training set (n = 114, from institution 1), an internal validation set (n = 48, from institution 1), and an external validation set (n = 76, from institution 2). A total of 963 radiomics features were extracted from the mesorectum region using T2-weighted imaging (T2WI). The radiomics model was developed using the methods of the minimum redundancy of the maximum relevance (mRMR) and the least absolute shrinkage (LASSO) regression. After univariate and multivariate logistic analysis, a clinical model was constructed based on clinical characteristics. A combined model was built and demonstrated as a nomogram. These models were evaluated by discrimination, calibration, and clinical application.</p><p><strong>Results: </strong>Among 238 patients, 98 (41.1%) were EMVI-positive. The Area Under the Curve (AUC) values for the clinical, radiomics, and combined models, respectively, were 0.65, 0.85, and 0.88 for the training set (95% CI: 0.81-0.94); 0.60, 0.81, and 0.81 for the internal validation set (95% CI: 0.68-0.95); and 0.60, 0.78, and 0.82 for the external validation set (95% CI: 0.72-0.91).</p><p><strong>Conclusion: </strong>This study presents a model for predicting the EMVI status in patients with rectal cancer. The combined model, which incorporates both a mesorectal radiomics signature from T2WI and the clinical predictor of serum neutrophil count, demonstrated superior discrimination, calibration, and clinical utility compared to models based on either clinical or radiomics features alone. The non-invasive tool shows promise for aiding in preoperative risk stratification and guiding clinical decision-making.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147688074","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}
Cancer ImagingPub Date : 2026-04-13DOI: 10.1186/s40644-026-01031-x
Ana S F Ribeiro, Olga Husson, Sheila Matharu, Saffron Cox, Davide Meo, Richard Sidebottom, Robby Emsley, Karen Thomas, Joshua Shur, Francesca Castagnoli, Sharmin Malekout, Elizabeth Robinson, Chin Lian Ng, Christian Kelly-Morland, Wim J G Oyen, Winette T A van der Graaf, Christina Messiou
{"title":"Improving patient understanding of oncology imaging: radiologist and patient evaluation of summarised versus full-length AI-simplified reports from a tertiary cancer centre.","authors":"Ana S F Ribeiro, Olga Husson, Sheila Matharu, Saffron Cox, Davide Meo, Richard Sidebottom, Robby Emsley, Karen Thomas, Joshua Shur, Francesca Castagnoli, Sharmin Malekout, Elizabeth Robinson, Chin Lian Ng, Christian Kelly-Morland, Wim J G Oyen, Winette T A van der Graaf, Christina Messiou","doi":"10.1186/s40644-026-01031-x","DOIUrl":"https://doi.org/10.1186/s40644-026-01031-x","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147670501","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}
Cancer ImagingPub Date : 2026-04-07DOI: 10.1186/s40644-026-01025-9
Noah B Manz, Jordan Reed, Christopher D Kanner, Jeremy A Dressler
{"title":"Temporal change in skeletal muscle index as a predictor of recurrence for patients with locally advanced colorectal malignancy: a retrospective cohort study.","authors":"Noah B Manz, Jordan Reed, Christopher D Kanner, Jeremy A Dressler","doi":"10.1186/s40644-026-01025-9","DOIUrl":"10.1186/s40644-026-01025-9","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer remains a leading cause of cancer-related morbidity and mortality with stage III disease carrying a substantial risk of recurrence despite curative resection. Accurate risk stratification is essential to optimize surveillance and guide adjuvant therapy. Traditional risk models rely heavily on pathologic features, but recent studies suggest that body composition metrics, particularly imaging-based assessments of skeletal muscle mass, may offer additional prognostic value. The skeletal muscle index (SMI), derived from routine CT imaging, has emerged as a promising surrogate marker of frailty. However, the relationship between temporal changes in SMI and cancer recurrence remains poorly understood.</p><p><strong>Methods: </strong>A retrospective cohort study was performed using single-institution data from over 500 patients who underwent resection for stage III colorectal malignancy. SMI at diagnosis (SMI<sub>dx</sub>) was measured using staging CT imaging and follow-up measurements (SMI<sub>fu</sub>) were obtained at the most recent scan prior to recurrence to capture changes preceding radiologic evidence of disease. Patients were paired using propensity score matching and temporal changes in SMI were evaluated using conditional logistic regressions. Receiver operating characteristics and decision curve analysis were performed to determine whether change in SMI can stratify patients into meaningful risk groups to guide surveillance.</p><p><strong>Results: </strong>A decrease in SMI was independently associated with increased risk of disease recurrence with an odds ratio of 2.23 per 10% decline (95% CI: 1.15-4.35, p = 0.018). Baseline muscle status was not associated with recurrence (OR 1.01 per cm<sup>2</sup>/m<sup>2</sup>, 95% CI: 0.97-1.05, p = 0.585). The optimal risk stratification threshold was θ = 2.11%, (θ = SMI<sub>fu</sub> / SMI<sub>dx</sub> - 1) with a sensitivity of 90% and specificity of 60%. Decision curve analysis showed net clinical benefit over a wide range of thresholds, θ = ±11%. These results were reproduced in an internally validated cohort.</p><p><strong>Conclusions: </strong>Postoperative decline in skeletal muscle index is a significant, independent predictor of colorectal cancer recurrence. Clinically relevant risk stratification thresholds have been proposed and support the conclusion that longitudinal monitoring of SMI may have value to escalate surveillance intensity rather than being a passive prognostic marker. Future studies should focus on validating these thresholds in large, multi-center cohorts.</p>","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147632525","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}
Cancer ImagingPub Date : 2026-04-02DOI: 10.1186/s40644-026-01029-5
Yun Wang, Deng Lyu, Xiaoli Deng, Lei Hu, Junhong Wu, Xiuxiu Zhou, Yi Xiao, Li Fan, Shiyuan Liu
{"title":"CT predictors of visceral pleural invasion in subsolid nodular pulmonary adenocarcinoma: differences between direct and indirect tumor-pleura contact.","authors":"Yun Wang, Deng Lyu, Xiaoli Deng, Lei Hu, Junhong Wu, Xiuxiu Zhou, Yi Xiao, Li Fan, Shiyuan Liu","doi":"10.1186/s40644-026-01029-5","DOIUrl":"https://doi.org/10.1186/s40644-026-01029-5","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147590239","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}
Cancer ImagingPub Date : 2026-03-28DOI: 10.1186/s40644-026-01022-y
Nikolaus von Knebel Doeberitz, Petr Menshchikov, Florian Kroh, Laila König, Svenja Graß, Cora Bauspieß, Philip S Boyd, Sebastian Regnery, Thomas Zeyen, Stephen Schaumann, Ralf Floca, Daniel Hasson, Moritz Scherer, Martin Bendszus, Wolfgang Wick, Jürgen Debus, Peter Bachert, Mark E Ladd, Heinz-Peter Schlemmer, Andreas Korzowski, Daniel Paech
{"title":"Assessment of multimodal CEST, perfusion and diffusion MRI for predicting clinical outcome of patients with diffuse glioma following surgery at baseline before radiotherapy.","authors":"Nikolaus von Knebel Doeberitz, Petr Menshchikov, Florian Kroh, Laila König, Svenja Graß, Cora Bauspieß, Philip S Boyd, Sebastian Regnery, Thomas Zeyen, Stephen Schaumann, Ralf Floca, Daniel Hasson, Moritz Scherer, Martin Bendszus, Wolfgang Wick, Jürgen Debus, Peter Bachert, Mark E Ladd, Heinz-Peter Schlemmer, Andreas Korzowski, Daniel Paech","doi":"10.1186/s40644-026-01022-y","DOIUrl":"10.1186/s40644-026-01022-y","url":null,"abstract":"","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13151286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147571988","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}