Radiology. Imaging cancer最新文献

{"title":"Prediction of Macrotrabecular-Massive Hepatocellular Carcinoma and Associated Prognosis Using Contrast-enhanced US and Clinical Features.","authors":"Jiapeng Wu, Sisi Liu, Yiqiong Zhang, WenZhen Ding, Qinxian Zhao, Yuling Wang, Fan Xiao, Xiaoling Yu, Xiaoyan Xie, Shuhong Liu, Jingmin Zhao, Jintang Liao, Jie Yu, Ping Liang","doi":"10.1148/rycan.240419","DOIUrl":"https://doi.org/10.1148/rycan.240419","url":null,"abstract":"<p><p>Purpose To develop a combined contrast-enhanced US (CEUS) clinical model for the prediction of macrotrabecular-massive hepatocellular carcinoma (MTM HCC) and evaluate its diagnostic and prognostic values. Materials and Methods This secondary analysis of a prospective multicenter study (ClinicalTrials.gov: NCT04682886) included participants from three independent cohorts who underwent CEUS and surgical resection for HCC between January 2017 and December 2022. Two radiologists independently reviewed CEUS data, and the interreader agreement was evaluated. Logistic regression was performed using the training cohort to determine the predictors associated with MTM HCC, while the validation cohort was used to evaluate the diagnostic and prognostic values of the predictors. Results A total of 387 participants (mean age, 55.09 years ± 10.33 [SD]; 342 male) were included. Four clinical and CEUS features were associated with MTM HCC: early washout (before 60 seconds) (odds ratio [OR]: 8.82 [95% CI: 4.22, 18.64], <i>P</i> < .001), hypoenhancing component (OR: 4.03 [95% CI: 1.78, 9.49], <i>P</i> < .001), tumor size (OR: 1.28 [95% CI: 1.04, 1.59], <i>P</i> = .02), and serum α-fetoprotein level greater than 100 ng/mL (OR: 3.01 [95% CI: 1.41, 6.63], <i>P</i> = .004). The combined predictive model yielded an area under the receiver operating characteristic curve of 0.89 (95% CI: 0.85, 0.93) in the training cohort and 0.81 (95% CI: 0.73, 0.89) in the validation cohort. The model also achieved a negative predictive value of 94.2% (147 of 156) in the training cohort and 88.0% (66 of 75) in the validation cohort, with high prognostic accuracy for overall survival (hazard ratio: 2.26 [95% CI: 1.07, 4.79], <i>P</i> = .03). Conclusion The combined CEUS-clinical predictive model could be used to characterize the MTM HCC subtype and determine prognosis. <b>Keywords:</b> Molecular Imaging-Angiogenesis, Ultrasound-Contrast, Liver, Macrotrabecular-Massive Hepatocellular Carcinoma, Contrast-enhanced US Clinical trial registration no. NCT04682886 <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 4","pages":"e240419"},"PeriodicalIF":5.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144554322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extraskeletal Larynx Osteosarcoma: Difficult Diagnosis and Early Resection.","authors":"Tomonori Kawasaki, Mitsuhiko Nakahira, Kojiro Onohara, Tomoko Yamazaki, Tomoaki Tashima, Jiro Ichikawa","doi":"10.1148/rycan.250045","DOIUrl":"https://doi.org/10.1148/rycan.250045","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 4","pages":"e250045"},"PeriodicalIF":5.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gastric Medullary Carcinoma.","authors":"Saumya Gurbani, Courtney Wiley, Raul Gonzalez","doi":"10.1148/rycan.250189","DOIUrl":"https://doi.org/10.1148/rycan.250189","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 4","pages":"e250189"},"PeriodicalIF":5.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pulmonary Cement Embolism after Percutaneous Vertebroplasty for Metastatic Rectal Cancer.","authors":"Divij Agarwal, Sanjay Kumar Meena, Tej Pal, Amit Gupta, S H Chandrashekhara","doi":"10.1148/rycan.250200","DOIUrl":"https://doi.org/10.1148/rycan.250200","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 4","pages":"e250200"},"PeriodicalIF":5.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Pelvic Structure Involvement and Tumor Morphology at MRI with Prognosis Following Resection in Locally Recurrent Rectal Cancer.","authors":"Davy M J Creemers, Henrik Iversen, Evi Banken, Floor Piqeur, Stijn H J Ketelaers, Alette Daniëls-Gooszen, Gabriella J Palmer, Torbjörn Holm, Harm Rutten, Chikako Suzuki, Jacobus W A Burger, Anna Martling, Joost Nederend","doi":"10.1148/rycan.240246","DOIUrl":"10.1148/rycan.240246","url":null,"abstract":"<p><p>Purpose To determine the influence of location, extent of tissue invasion, and tumor morphology at MRI on the resectability of locally recurrent rectal cancer (LRRC) and postresection oncologic outcomes of LRRC. Materials and Methods This retrospective observational study included consecutive patients diagnosed with LRRC who underwent surgery with curative intent at the Catharina Hospital Eindhoven and Karolinska University Hospital Stockholm between January 2003 and December 2017. Two expert radiologists reviewed available MR images while adhering to a standardized reviewing checklist. The effect of pelvic structure involvement, tumor morphology on the primary outcome of resection margin status, and secondary outcomes of overall survival and disease-free survival were assessed using univariable and multivariable logistic regression and Cox proportional hazard analyses. Results The final analysis included 328 patients with LRRC (mean age ± SD, 64.9 years ± 9.6; 126 female, 202 male). Resection margins were negative in 217 (66.2%) patients and positive in 111 patients (33.8%). Tumor size, tumor type, and border type on MR images were all associated with resectability. Central recurrences were associated with the lowest likelihood of positive resection margins (odds ratio [OR], 0.45; 95% CI: 0.28, 0.71; <i>P</i> < .001), whereas lateral recurrences were associated with the highest likelihood (OR, 2.00; 95% CI: 1.25, 3.19: <i>P</i> = .004). Similarly, central recurrences were associated with better disease-free survival compared with lateral recurrences (hazard ratio [HR], 0.69; 95% CI: 0.53, 0.90; <i>P</i> = .006 vs HR, 1.49; 95% CI: 1.14, 1.94; <i>P</i> = .003, respectively). Similar findings were observed after correcting for resection margin status. Conclusion Standardized MRI assessment of tumor characteristics in patients with LRRC resulted in the identification of specific prognostic factors. Central compartment involvement and well-defined tumors were associated with improved prognosis, whereas lateral compartment involvement and fibrotic spiculated tumors were associated with a worse prognosis after surgical resection. <b>Keywords:</b> Rectum, MR-Imaging, Abdomen/GI, Oncology, Surgery, Locally Recurrent Rectal Cancer, Tumor Biology <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 4","pages":"e240246"},"PeriodicalIF":5.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144507960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Digital Breast Tomosynthesis and Mammography-based Radiomics for Breast Cancer Risk Assessment: Case-Control Study.","authors":"Alex A Nguyen, Eric A Cohen, Omid Haji Maghsoudi, Raymond J Acciavatti, Lauren Pantalone, Walter C Mankowski, Christopher G Scott, Stacey Winham, Celine M Vachon, Andrew D Maidment, Emily F Conant, Anne Marie McCarthy, Despina Kontos","doi":"10.1148/rycan.240318","DOIUrl":"https://doi.org/10.1148/rycan.240318","url":null,"abstract":"<p><p>Purpose To compare the performance of volumetric radiomic parenchymal pattern analysis from three-dimensional (3D) digital breast tomosynthesis (DBT) images with that of two-dimensional (2D) digital mammography (DM) and 2D sections from DBT in assessing breast cancer risk relative to breast density measurements. Materials and Methods This was a retrospective matched case-control study among individuals who underwent concurrent DM and DBT screening from March 2011 through December 2014. The Cancer Phenomics Toolkit was used to calculate radiomic features from craniocaudal and mediolateral oblique views in all study patients, matched on race and age, for various experimental settings, including image resolution and window size. For each image type, conditional logistic regression evaluated the association of radiomic features, along with age, body mass index (BMI), and area percent density (PD) (from the Laboratory for Individualized Breast Radiodensity Assessment software), with breast cancer, using the C statistic as the measure of model predictive ability. Model fit was compared via likelihood ratio tests. Results The study included 924 female patients (median age, 61 years [IQR: 51-69 years]), with 187 cases and 737 controls. Volumetric features from 3D reconstructed DBT scans had, on average, higher C statistics across all experimental conditions. Among models using only radiomic features, C statistics were highest for models using features from 3D images (mean C statistic: 0.68, <i>P</i> < .001); models using features from 2D image types resulted in lower mean C statistics (0.60 to 0.65). A baseline model using age, BMI, and area PD had a C statistic of 0.60. The effect of higher image resolution and smaller window size were not substantial, supporting the use of less computationally intensive processing. Conclusion Fully automated 3D parenchymal analysis from DBT improved breast cancer risk estimation beyond markers derived from area breast density and 2D images. <b>Keywords:</b> Mammography, Tomosynthesis, Breast, Volume Analysis <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 4","pages":"e240318"},"PeriodicalIF":5.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144554321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The SUPE_R Trial: Posttreatment Surveillance with ¹⁸F-FDG PET/CT in Stage I to III Non-Small Cell Lung Cancer.","authors":"Netanja I Harlianto, Pim A de Jong","doi":"10.1148/rycan.259013","DOIUrl":"https://doi.org/10.1148/rycan.259013","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 4","pages":"e259013"},"PeriodicalIF":5.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144554323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive Explainable Deep Learning Model for Hepatocellular Carcinoma Diagnosis at Gadoxetic Acid-enhanced MRI: A Retrospective, Multicenter, Diagnostic Study.","authors":"Mingkai Li, Zhi Zhang, Zebin Chen, Xi Chen, Huaqing Liu, Yuanqiang Xiao, Haimei Chen, Xiaodan Zong, Jingbiao Chen, Jianning Chen, Xinying Wang, Xuehong Xiao, Zhiwei Yang, Lanqing Han, Jin Wang, Bin Wu","doi":"10.1148/rycan.240332","DOIUrl":"10.1148/rycan.240332","url":null,"abstract":"<p><p>Purpose To develop an artificial intelligence (AI) model based on gadoxetic acid-enhanced MRI to assist radiologists in hepatocellular carcinoma (HCC) diagnosis. Materials and Methods This retrospective study included patients with focal liver lesions (FLLs) who underwent gadoxetic acid-enhanced MRI between January 2015 and December 2021. All hepatic malignancies were diagnosed pathologically, whereas benign lesions were confirmed with pathologic findings or imaging follow-up. Five manually labeled bounding boxes for each FLL obtained from precontrast T1-weighted, T2-weighted, arterial phase, portal venous phase, and hepatobiliary phase images were included. The lesion classifier component, used to distinguish HCC from non-HCC, was trained and externally tested. The feature classifier, based on a post hoc algorithm, inferred the presence of the Liver Imaging Reporting and Data System (LI-RADS) features by analyzing activation patterns of the pretrained lesion classifier. Two radiologists categorized FLLs in the external testing dataset according to LI-RADS criteria. Diagnostic performance of the AI model and the model's impact on reader accuracy were assessed. Results The study included 839 patients (mean age, 51 years ± 12 [SD]; 681 male) with 1023 FLLs (594 HCCs and 429 non-HCCs). The AI model yielded area under the receiver operating characteristic curves of 0.98 and 0.97 in the training set and external testing set, respectively. Compared with LI-RADS category 5, the AI model showed higher sensitivity (91.6% vs 74.8%; <i>P</i> < .001) and similar specificity (90.7% vs 96.0%; <i>P</i> = .22). The two readers identified more LI-RADS major features and more accurately classified category LR-5 lesions when assisted versus unassisted by AI, with higher sensitivities (reader 1, 85.7% vs 72.3%; <i>P</i> < .001; reader 2, 89.1% vs 74.0%; <i>P</i> < .001) and the same specificities (reader 1, 93.3% vs reader 2, 94.7%; <i>P</i> > .99 for both). Conclusion The AI model accurately diagnosed HCC and improved the radiologists' diagnostic performance. <b>Keywords:</b> Artificial Intelligence, Deep Learning, MRI, Hepatocellular Carcinoma <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Singh et al in this issue.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240332"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Method of Detection of Breast Cancer on Clinical Outcomes in Individuals Aged 40 Years or Older.","authors":"Javeria Munir, Yashmin Nisha, Nayaar Islam, Mary Beth Bissell, Betty Anne Schwarz, Erin Cordeiro, Jean M Seely","doi":"10.1148/rycan.240046","DOIUrl":"10.1148/rycan.240046","url":null,"abstract":"<p><p>Purpose To determine the impact of method of detection (MOD) on breast cancer stage, treatment, and breast cancer-related mortality in patients aged 40 years or older. Materials and Methods This retrospective observational cohort study included consecutive female individuals aged 40 years or older diagnosed with breast cancer at a tertiary referral center between January and December 2016. Odds ratios (ORs) and 95% CIs were computed using logistic regression modeling to determine the impact of MOD (screen-detected vs symptom-detected) on pathologic stage (American Joint Commission on Cancer, eighth edition), surgical treatment, and chemotherapy, with Cox regression to find the hazard ratio (HR) and 95% CI for mortality. Significance level was set at a <i>P</i> value less than .013 using Bonferroni correction. For subgroup analyses, patients were divided into categorical age groups: 40 to 49 years, 50 to 59 years, 60 to 74 years, and 75 years or older. Results A total of 821 patients (mean age [±SD], 62.5 years ± 12.2) were included; mean duration of follow-up was 6.7 years. Symptom-detected cancers (50.1% [411 of 821]) were more frequent in less-screened age groups (72.9% [97 of 133] in women aged 40-49 years and 70.4% [95 of 135] in those aged ≥75 years) compared with screened age groups (49.5% [106 of 214] in those aged 50-59 years and 33.3% [113 of 339 in those aged 60-74 years]). The odds of advanced cancer (stage IIA or greater) were 6.60 higher (95% CI: 4.96, 8.77; <i>P</i> < .001) with symptom detection (32.5% [267 of 821]) compared with screen detection (10.2% [84 of 821]). Symptom-detected cancers had higher HR of breast cancer-related death (HR, 1.63 [95% CI: 1.17, 2.28]; <i>P</i> = .004), higher odds of mastectomy (OR, 2.20 [95% CI: 1.63, 2.96]; <i>P</i> < .001), and the same odds of chemotherapy, adjusted for age and stage at diagnosis. Multivariable analysis found that higher tumor stage was associated with symptom detection (OR, 6.40 [95% CI: 4.74, 8.70]; <i>P</i> < .001), greater risk of chemotherapy (OR, 1.97 [95% CI: 1.39, 2.78]; <i>P</i> < .001), and age at diagnosis (OR, 1.02 [95% CI: 1.01, 1.03]; <i>P</i> = .001). Conclusion Screen-detected breast cancer in patients aged 40 years or older was associated with lower odds of advanced cancer, mastectomy, and breast cancer-related death compared with symptom-detected cancer. <b>Keywords:</b> Breast Screening, Guidelines, Breast Cancer, Treatment <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240046"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Air Embolism after Percutaneous Lung Biopsy or Ablation: A Report of Six Cases.","authors":"Rebecca Choi, Haylie Kieu-Mi Phan, Varshana Gurusamy, Rahul A Sheth, Bruno C Odisio, Kelvin Hong, Alda L Tam","doi":"10.1148/rycan.240379","DOIUrl":"10.1148/rycan.240379","url":null,"abstract":"<p><p>Systemic air embolism is a rare but potentially life-threatening complication of percutaneous lung biopsy and ablation. This report describes six patients (four male, two female) with air embolism following CT-guided lung interventions at two institutions between January 2021 and March 2024. Air embolisms were identified in locations such as the pulmonary vein, left ventricle, and coronary arteries, with clinical presentations ranging from asymptomatic to life-threatening events. Prompt intervention, including oxygen therapy, patient repositioning, and intravenous administration of epinephrine in cases of cardiac arrest, resulted in favorable outcomes. This series highlights the importance of early detection of systemic air embolism and tailored management strategies based on its anatomic location to mitigate risk of serious adverse events. <b>Keywords:</b> Interventional-Body, Complications, Ablation Techniques (Radiofrequency, Thermal, Chemical), Biopsy/Needle Aspiration, Lung, Electrocardiogram © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 3","pages":"e240379"},"PeriodicalIF":5.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12130690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143780998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}