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Long-term Mammography Screening Trends and Predictors of Return to Screening after the COVID-19 Pandemic: Results from a Statewide Registry. COVID-19 大流行后乳腺放射摄影筛查的长期趋势和恢复筛查的预测因素:来自全州登记处的结果。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-05-01 DOI: 10.1148/rycan.230161
Brian L Sprague, Sarah A Nowak, Thomas P Ahern, Sally D Herschorn, Peter A Kaufman, Catherine Odde, Hannah Perry, Michelle M Sowden, Pamela M Vacek, Donald L Weaver

Purpose To evaluate long-term trends in mammography screening rates and identify sociodemographic and breast cancer risk characteristics associated with return to screening after the COVID-19 pandemic. Materials and Methods In this retrospective study, statewide screening mammography data of 222 384 female individuals aged 40 years or older (mean age, 58.8 years ± 11.7 [SD]) from the Vermont Breast Cancer Surveillance System were evaluated to generate descriptive statistics and Joinpoint models to characterize screening patterns during 2000-2022. Log-binomial regression models estimated associations of sociodemographic and risk characteristics with post-COVID-19 pandemic return to screening. Results The proportion of female individuals in Vermont aged 50-74 years with a screening mammogram obtained in the previous 2 years declined from a prepandemic level of 61.3% (95% CI: 61.1%, 61.6%) in 2019 to 56.0% (95% CI: 55.7%, 56.3%) in 2021 before rebounding to 60.7% (95% CI: 60.4%, 61.0%) in 2022. Screening adherence in 2022 remained substantially lower than that observed during the 2007-2010 apex of screening adherence (66.1%-67.0%). Joinpoint models estimated an annual percent change of -1.1% (95% CI: -1.5%, -0.8%) during 2010-2022. Among the cohort of 95 644 individuals screened during January 2018-March 2020, the probability of returning to screening during 2020-2022 varied by age (eg, risk ratio [RR] = 0.94 [95% CI: 0.93, 0.95] for age 40-44 vs age 60-64 years), race and ethnicity (RR = 0.84 [95% CI: 0.78, 0.90] for Black vs White individuals), education (RR = 0.84 [95% CI: 0.81, 0.86] for less than high school degree vs college degree), and by 5-year breast cancer risk (RR = 1.06 [95% CI: 1.04, 1.08] for very high vs average risk). Conclusion Despite a rebound to near prepandemic levels, Vermont mammography screening rates have steadily declined since 2010, with certain sociodemographic groups less likely to return to screening after the pandemic. Keywords: Mammography, Breast, Health Policy and Practice, Neoplasms-Primary, Epidemiology, Screening Supplemental material is available for this article. © RSNA, 2024.

目的 评估乳腺放射摄影筛查率的长期趋势,并确定与 COVID-19 大流行后恢复筛查相关的社会人口学和乳腺癌风险特征。材料与方法 在这项回顾性研究中,我们评估了佛蒙特州乳腺癌监测系统(Vermont Breast Cancer Surveillance System)提供的 222 384 名 40 岁及以上女性(平均年龄为 58.8 岁 ± 11.7 [SD])的全州乳腺 X 线照相筛查数据,生成了描述性统计数据和 Joinpoint 模型,以描述 2000-2022 年期间的筛查模式。对数二项式回归模型估计了社会人口学特征和风险特征与COVID-19大流行后恢复筛查的相关性。结果 佛蒙特州 50-74 岁女性在过去 2 年中接受过乳房 X 线照相筛查的比例从 2019 年大流行前的 61.3% (95% CI: 61.1%, 61.6%) 下降到 2021 年的 56.0% (95% CI: 55.7%, 56.3%),然后在 2022 年回升到 60.7% (95% CI: 60.4%, 61.0%)。2022 年的筛查依从性仍大大低于 2007-2010 年筛查依从性最高峰时期的水平(66.1%-67.0%)。据连接点模型估计,2010-2022 年期间的年百分比变化为-1.1%(95% CI:-1.5%, -0.8%)。在 2018 年 1 月至 2020 年 3 月期间接受筛查的 95 644 人队列中,2020-2022 年期间重返筛查的概率因年龄(例如,40-44 岁 vs 60-64 岁的风险比 [RR] = 0.94 [95% CI: 0.93, 0.95])、种族和民族(RR = 0.84[95%CI:0.78, 0.90])、教育程度(RR=0.84[95%CI:0.81, 0.86],高中以下学历 vs 大学学历)以及 5 年乳腺癌风险(RR=1.06[95%CI:1.04, 1.08],非常高风险 vs 一般风险)。结论 尽管佛蒙特州的乳腺放射摄影筛查率已回升至接近大流行前的水平,但自2010年以来一直在稳步下降,某些社会人口群体在大流行后恢复筛查的可能性较小。关键词乳腺 X 线照相术 乳腺 健康政策与实践 肿瘤-原发性 流行病学 筛查 本文有补充材料。© RSNA, 2024.
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引用次数: 0
Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network. 乳腺癌淋巴结转移的机器学习预测:基于 MRI 的多机构 4D 卷积神经网络的性能。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-05-01 DOI: 10.1148/rycan.230107
Dogan S Polat, Son Nguyen, Paniz Karbasi, Keith Hulsey, Murat Can Cobanoglu, Liqiang Wang, Albert Montillo, Basak E Dogan

Purpose To develop a custom deep convolutional neural network (CNN) for noninvasive prediction of breast cancer nodal metastasis. Materials and Methods This retrospective study included patients with newly diagnosed primary invasive breast cancer with known pathologic (pN) and clinical nodal (cN) status who underwent dynamic contrast-enhanced (DCE) breast MRI at the authors' institution between July 2013 and July 2016. Clinicopathologic data (age, estrogen receptor and human epidermal growth factor 2 status, Ki-67 index, and tumor grade) and cN and pN status were collected. A four-dimensional (4D) CNN model integrating temporal information from dynamic image sets was developed. The convolutional layers learned prognostic image features, which were combined with clinicopathologic measures to predict cN0 versus cN+ and pN0 versus pN+ disease. Performance was assessed with the area under the receiver operating characteristic curve (AUC), with fivefold nested cross-validation. Results Data from 350 female patients (mean age, 51.7 years ± 11.9 [SD]) were analyzed. AUC, sensitivity, and specificity values of the 4D hybrid model were 0.87 (95% CI: 0.83, 0.91), 89% (95% CI: 79%, 93%), and 76% (95% CI: 68%, 88%) for differentiating pN0 versus pN+ and 0.79 (95% CI: 0.76, 0.82), 80% (95% CI: 77%, 84%), and 62% (95% CI: 58%, 67%), respectively, for differentiating cN0 versus cN+. Conclusion The proposed deep learning model using tumor DCE MR images demonstrated high sensitivity in identifying breast cancer lymph node metastasis and shows promise for potential use as a clinical decision support tool. Keywords: MR Imaging, Breast, Breast Cancer, Breast MRI, Machine Learning, Metastasis, Prognostic Prediction Supplemental material is available for this article. Published under a CC BY 4.0 license.

目的 开发一种定制的深度卷积神经网络(CNN),用于无创预测乳腺癌结节转移。材料与方法 这项回顾性研究纳入了 2013 年 7 月至 2016 年 7 月期间在作者所在机构接受动态对比增强(DCE)乳腺 MRI 检查的新诊断原发性浸润性乳腺癌患者,这些患者具有已知的病理(pN)和临床结节(cN)状态。收集了临床病理数据(年龄、雌激素受体和人类表皮生长因子 2 状态、Ki-67 指数和肿瘤分级)以及 cN 和 pN 状态。开发的四维(4D)CNN 模型整合了动态图像集的时间信息。卷积层学习预后图像特征,并将其与临床病理学指标相结合,预测 cN0 与 cN+ 以及 pN0 与 pN+ 疾病。用接收器工作特征曲线下面积(AUC)评估性能,并进行五重嵌套交叉验证。结果 分析了 350 名女性患者(平均年龄为 51.7 岁 ± 11.9 [SD])的数据。4D 混合模型区分 pN0 与 pN+ 的 AUC 值、灵敏度和特异性分别为 0.87(95% CI:0.83,0.91)、89%(95% CI:79%,93%)和 76%(95% CI:68%,88%);区分 cN0 与 cN+ 的 AUC 值、灵敏度和特异性分别为 0.79(95% CI:0.76,0.82)、80%(95% CI:77%,84%)和 62%(95% CI:58%,67%)。结论 利用肿瘤 DCE MR 图像建立的深度学习模型在识别乳腺癌淋巴结转移方面表现出较高的灵敏度,有望用作临床决策支持工具。关键词磁共振成像 乳腺癌 乳腺 MRI 机器学习 转移 预后预测 本文有补充材料。以 CC BY 4.0 许可发布。
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引用次数: 0
Theranostic Capacity of a Mucin 16-targeted Antibody for Ovarian Cancer. 针对卵巢癌的粘蛋白 16 靶向抗体的抗肿瘤能力
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-05-01 DOI: 10.1148/rycan.249013
Kel Vin Tan
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引用次数: 0
The Era of ChatGPT and Large Language Models: Can We Advance Patient-centered Communications Appropriately and Safely? ChatGPT 和大型语言模型时代:我们能否适当而安全地推进以患者为中心的交流?
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-05-01 DOI: 10.1148/rycan.240038
Wendy Tu, Bonnie N. Joe
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引用次数: 0
Prediction of Major Adverse Cardiovascular Events in Patients with Chest Pain Using Coronary Artery Calcium Score. 利用冠状动脉钙化评分预测胸痛患者的主要不良心血管事件
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-05-01 DOI: 10.1148/rycan.249008
Lauren E Burkard-Mandel
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引用次数: 0
Revisiting the "Puffed Cheek" Technique: Advantages, Fallacies, and Potential Solutions. 重新审视 "鼓腮 "技术:优势、谬误和潜在解决方案。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-05-01 DOI: 10.1148/rycan.230211
Shehbaz Ansari, Surjith Vattoth, Eric R Basappa, Pokhraj Prakashchandra Suthar, Santhosh Gaddikeri, Miral D Jhaveri

The "puffed cheek" technique is routinely performed during CT neck studies in patients with suspected oral cavity cancers. The insufflation of air within the oral vestibule helps in the detection of small buccal mucosal lesions, with better delineation of lesion origin, depth, and extent of spread. The pitfalls associated with this technique are often underrecognized and poorly understood. They can mimic actual lesions, forfeiting the technique's primary purpose. This review provides an overview of the puffed cheek technique and its associated pitfalls. These pitfalls include pneumoparotid, soft palate elevation that resembles a nasopharyngeal mass, various tongue displacements or distortions that obscure tongue lesions or mimic them, sublingual gland herniation, an apparent exacerbation of the airway edema, vocal cord adduction that hinders glottic evaluation, and false indications of osteochondronecrosis in laryngeal cartilage. Most stem from a common underlying mechanism of unintentional Valsalva maneuver engaged in by the patient while trying to perform a puffed cheek, creating a closed air column under positive pressure with resultant surrounding soft-tissue displacement. These pitfalls can thus be avoided by instructing the patient to maintain continuous nasal breathing while puffing out their cheek during image acquisition, preventing the formation of the closed air column. Keywords: CT, Head/Neck © RSNA, 2024.

在对疑似口腔癌患者进行颈部 CT 检查时,通常会采用 "鼓腮 "技术。向口腔前庭充气有助于发现小的颊粘膜病变,更好地确定病变的起源、深度和扩散范围。与这一技术相关的误区往往未被充分认识和理解。它们可能会模仿实际病变,从而失去了该技术的主要目的。本综述概述了鼓腮技术及其相关隐患。这些误区包括气胸、类似鼻咽肿块的软腭隆起、掩盖或模仿舌头病变的各种舌头移位或扭曲、舌下腺疝、气道水肿的明显加重、阻碍声门评估的声带内收以及喉软骨骨软化的错误提示。大多数情况都源于一个共同的潜在机制,即患者在试图做膨腮动作时无意中做了瓦尔萨尔瓦动作,在正压下形成了一个封闭的气柱,导致周围软组织移位。因此,可以通过指导患者在图像采集过程中保持持续的鼻腔呼吸,同时鼓起脸颊,防止形成封闭气柱,从而避免这些误区。关键词头颈部 CT © RSNA, 2024.
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引用次数: 0
Clarification of Concerns about the Demographic Composition of The Cancer Imaging Archive. 澄清对癌症成像档案人口构成的关切。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-05-01 DOI: 10.1148/rycan.240098
Janet F Eary, Lalitha K Shankar, John Freymann, Justin Kirby
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引用次数: 0
MRI-guided Stereotactic Ablative Radiotherapy versus CT-guided Irreversible Electroporation in Advanced Pancreatic Cancer: Insights from the CROSSFIRE Trial. MRI 引导下的立体定向消融放疗与 CT 引导下的不可逆电穿孔治疗晚期胰腺癌:来自 CROSSFIRE 试验的启示。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-05-01 DOI: 10.1148/rycan.249010
Yuan-Mao Lin
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引用次数: 0
Editor's Recognition Awards. 编辑表彰奖。
IF 4.4 Q1 ONCOLOGY Pub Date : 2024-03-01 DOI: 10.1148/rycan.240056
Gary D Luker
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引用次数: 0
Impact of PI-RADS Upgrading Rules on Prostate Cancer Detection and Biopsy Decision-Making. PI-RADS 升级规则对前列腺癌检测和活检决策的影响。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-03-01 DOI: 10.1148/rycan.249006
Yuan-Mao Lin
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引用次数: 0
期刊
Radiology. Imaging cancer
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