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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
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
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
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
Editor's Recognition Awards. 编辑表彰奖。
IF 4.4 Pub Date : 2024-03-01 DOI: 10.1148/rycan.240056
Gary D Luker
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引用次数: 0
It's Complicated: Managing Nonmass Enhancement Found at Breast MRI in Patients with Newly Diagnosed Cancer. 情况复杂:处理新确诊癌症患者在乳腺磁共振成像中发现的非质量增强。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-03-01 DOI: 10.1148/rycan.240003
Mary S Newell
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引用次数: 0
The Prevalence and Radiologic Features of Renal Cancers Associated with FLCN, BAP1, SDH, and MET Germline Mutations. 与 FLCN、BAP1、SDH 和 MET 基因突变相关的肾癌的患病率和放射学特征
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-03-01 DOI: 10.1148/rycan.230063
Charlotte Charbel, Pamela I Causa Andrieu, Mohamed Soliman, Sungmin Woo, Junting Zheng, Marinela Capanu, Ines Nikolovski, Hebert A Vargas, Murad Abusamra, Maria I Carlo

Purpose To investigate the prevalence of FLCN, BAP1, SDH, and MET mutations in an oncologic cohort and determine the prevalence, clinical features, and imaging features of renal cell carcinoma (RCC) associated with these mutations. Secondarily, to determine the prevalence of encountered benign renal lesions. Materials and Methods From 25 220 patients with cancer who prospectively underwent germline analysis with a panel of more than 70 cancer-predisposing genes from 2015 to 2021, patients with FLCN, BAP1, SDH, or MET mutations were retrospectively identified. Clinical records were reviewed for patient age, sex, race/ethnicity, and renal cancer diagnosis. If RCC was present, baseline CT and MRI examinations were independently assessed by two radiologists. Summary statistics were used to summarize continuous and categorical variables by mutation. Results A total of 79 of 25 220 (0.31%) patients had a germline mutation: FLCN, 17 of 25 220 (0.07%); BAP1, 22 of 25 220 (0.09%); SDH, 39 of 25 220 (0.15%); and MET, one of 25 220 (0.004%). Of these 79 patients, 18 (23%) were diagnosed with RCC (FLCN, four of 17 [24%]; BAP1, four of 22 [18%]; SDH, nine of 39 [23%]; MET, one of one [100%]). Most hereditary RCCs demonstrated ill-defined margins, central nonenhancing area (cystic or necrotic), heterogeneous enhancement, and various other CT and MR radiologic features, overlapping with the radiologic appearance of nonhereditary RCCs. The prevalence of other benign solid renal lesions (other than complex cysts) in patients was up to 11%. Conclusion FLCN, BAP1, SDH, and MET mutations were present in less than 1% of this oncologic cohort. Within the study sample size limits, imaging findings for hereditary RCC overlapped with those of nonhereditary RCC, and the prevalence of other associated benign solid renal lesions (other than complex cysts) was up to 11%. Keywords: Familial Renal Cell Carcinoma, Birt-Hogg-Dubé Syndrome, Carcinoma, Renal Cell, Paragangliomas, Urinary, Kidney © RSNA, 2024.

目的 调查肿瘤学队列中FLCN、BAP1、SDH和MET突变的发生率,并确定与这些突变相关的肾细胞癌(RCC)的发生率、临床特征和影像学特征。其次,确定所遇到的肾脏良性病变的患病率。材料与方法 从 2015 年至 2021 年期间对 25 220 名癌症患者进行了前瞻性种系分析,分析了 70 多个癌症易感基因,并回顾性地确定了 FLCN、BAP1、SDH 或 MET 基因突变的患者。对患者的年龄、性别、种族/民族和肾癌诊断进行了临床记录审查。如果存在 RCC,则由两名放射科医生独立评估基线 CT 和 MRI 检查结果。采用汇总统计法按突变对连续变量和分类变量进行汇总。结果 25 220 例患者中,共有 79 例(0.31%)发生了种系突变:其中,FLCN,25 220 例中有 17 例(0.07%);BAP1,25 220 例中有 22 例(0.09%);SDH,25 220 例中有 39 例(0.15%);MET,25 220 例中有 1 例(0.004%)。在这 79 例患者中,18 例(23%)被诊断为 RCC(FLCN,17 例中的 4 例 [24%];BAP1,22 例中的 4 例 [18%];SDH,39 例中的 9 例 [23%];MET,1 例中的 1 例 [100%])。大多数遗传性 RCC 表现为边缘不清、中央无强化区(囊性或坏死)、异质强化以及各种其他 CT 和 MR 放射学特征,与非遗传性 RCC 的放射学表现重叠。患者中其他良性肾实体病变(复杂囊肿除外)的发病率高达 11%。结论 在这组肿瘤患者中,FLCN、BAP1、SDH和MET突变的比例不到1%。在研究样本量限制范围内,遗传性RCC的成像结果与非遗传性RCC的成像结果重叠,其他相关良性实体肾病变(复杂囊肿除外)的发病率高达11%。关键词: 家族性肾细胞癌家族性肾细胞癌 Birt-Hogg-Dubé 综合征 癌症 肾细胞 副神经节瘤 泌尿 肾脏 © RSNA, 2024.
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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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