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Patient Characteristics Impact False Positives in AI Interpretation of True-Negative Screening Breast Tomosynthesis Examinations. 患者特征对人工智能解读真阴性乳腺断层合成检查中假阳性的影响。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-09-01 DOI: 10.1148/rycan.249015
Nour Homsi, Maggie Chung
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引用次数: 0
Rosai-Dorfman Disease Mimicking Metastatic Disease. 模仿转移性疾病的罗赛-多夫曼病
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-09-01 DOI: 10.1148/rycan.240109
Sofia Velasco, Santiago Aristizábal-Ortiz, Angela Guarnizo
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引用次数: 0
AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance. 用数字乳腺断层合成技术进行乳腺癌检测的人工智能增强型乳腺 X 线照相术:临床价值及与人类表现的比较。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-07-01 DOI: 10.1148/rycan.230149
Daphne Resch, Roberto Lo Gullo, Jonas Teuwen, Friedrich Semturs, Johann Hummel, Alexandra Resch, Katja Pinker

Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. Keywords: Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.

目的 比较两种基于深度学习的市售人工智能(AI)系统,用于数字乳腺断层合成(DBT)乳腺放射摄影,并将它们与放射科医生的表现进行比较。材料与方法 这项回顾性研究纳入了连续接受乳腺 X 射线摄影与 DBT 的无症状患者(2019-2020 年)。两套人工智能系统(Transpara 1.7.0 和 ProFound AI 3.0)用于评估 DBT 检查。使用接收器操作特征(ROC)分析对这两种系统进行了比较,以计算基于乳腺X线照相术乳腺密度的总体和亚组内检测恶性肿瘤的ROC曲线下面积(AUC)。乳腺成像报告和数据系统(Breast Imaging Reporting and Data System)的标准人工双读结果与人工智能(AI)的DeLong检验结果进行了比较。结果 在纳入的 419 名女性患者(中位年龄 60 岁 [IQR,52-70 岁])中,有 58 人经组织学证实患有乳腺癌。Transpara、ProFound AI和人类双读的AUC分别为0.86(95% CI:0.85,0.91)、0.93(95% CI:0.90,0.95)和0.98(95% CI:0.96,0.99)。对于 Transpara,7 分或更低的排除标准可产生 100% (95% CI: 94.2, 100.0) 的灵敏度和 60.9% (95% CI: 55.7, 66.0) 的特异性。高于 9 分的规则输入标准产生了 96.6% 的灵敏度(95% CI:88.1, 99.6)和 78.1% 的特异性(95% CI:73.8, 82.5)。对于 ProFound AI,低于 51 分的排除标准可产生 100% 的灵敏度(95% CI:93.8, 100)和 67.0% 的特异性(95% CI:62.2, 72.1)。以高于 69 分为入选标准,灵敏度为 93.1%(95% CI:83.3,98.1),特异度为 82.0%(95% CI:77.9,86.1)。结论 两种人工智能系统在乳腺癌检测中都表现出较高的性能,但与人工双读相比性能较低。关键词乳腺放射摄影术、乳腺、肿瘤学、人工智能、深度学习、数字乳腺断层扫描 © RSNA, 2024.
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引用次数: 0
Calcified Primary Signet Ring Cell Carcinoma of the Colon with Metastases. 结肠钙化性原发性信号环细胞癌伴转移。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-07-01 DOI: 10.1148/rycan.240079
Sanil Garg, Amit Gupta, Krithika Rangarajan, Rajni Yadav, Mukesh Kumar
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引用次数: 0
Innovative Advances in Molecular Breast Imaging Biopsy. 分子乳腺成像活检的创新进展。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-07-01 DOI: 10.1148/rycan.240135
Amy M Fowler
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引用次数: 0
Digital Breast Tomosynthesis for Nonimplant-displaced Views May Be Safely Omitted at Screening Mammography. 筛查乳腺 X 线照相术时可安全地省略用于非植入物移位视图的数字乳腺断层合成术。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-07-01 DOI: 10.1148/rycan.249014
Brandon K K Fields, Bonnie N Joe
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引用次数: 0
Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma. 平均明显推进器磁共振成像:浸润性乳腺导管癌中肿瘤与基质比率的定量评估。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-07-01 DOI: 10.1148/rycan.230165
Xiang Zhang, Ya Qiu, Wei Jiang, Zehong Yang, Mengzhu Wang, Qin Li, Yeqing Liu, Xu Yan, Guang Yang, Jun Shen

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

目的 确定平均表观传播者(MAP)磁共振成像指标在评估乳腺癌的肿瘤-基质比(TSR)状态时是否比表观弥散系数(ADC)值表现更好。材料与方法 2021 年 8 月至 2022 年 10 月,271 名参与者前瞻性入组(ClinicalTrials.gov 标识符:NCT05159323)并接受了乳腺弥散频谱成像和弥散加权成像。MAP MRI 指标和 ADC 均来自弥散 MRI 数据。根据病理检查结果将所有参与者分为高TSR组(基质成分<50%)和低TSR组(基质成分≥50%)。收集临床病理特征,评估核磁共振成像结果。采用逻辑回归确定区分 TSR 状态的自变量。使用 DeLong 和 McNemar 检验比较了 MAP MRI 指标(单独或结合临床病理特征)与 ADC 之间的接收器操作特征曲线下面积(AUC)、灵敏度、特异性和准确性。结果 共纳入了 181 名女性参与者(平均年龄为 49 岁 ± 10 [SD])。高TSR组和低TSR组的所有弥散MRI指标均有差异(P < .001 至 P = .01)。与 ADC 相比,MAP MRI 的径向非高斯性(0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68],P < .001)和准确性(181 例中的 138 例 [76%] vs 181 例中的 106 例 [59%],P < .001)更高。结论 在评估乳腺癌的 TSR 时,MAP MRI 可能是比传统扩散加权成像更好的方法。关键词磁共振弥散加权成像 磁共振成像 乳腺癌 肿瘤学 ClinicalTrials.gov Identifier:本文有补充材料。© RSNA, 2024.
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引用次数: 0
AI Systems for Mammography with Digital Breast Tomosynthesis: Expectations and Challenges. 数字乳腺断层合成乳腺 X 线照相术的人工智能系统:期望与挑战。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-07-01 DOI: 10.1148/rycan.240171
Masako Kataoka, Takayoshi Uematsu
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引用次数: 0
Patient Positioning by Online Adaptive Radiation Therapy. 通过在线自适应放射治疗进行患者定位。
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-07-01 DOI: 10.1148/rycan.240120
Paolo Farace
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引用次数: 0
Infrared Fluorescence-guided Surgery for Tumor and Metastatic Lymph Node Detection in Head and Neck Cancer. 红外荧光引导手术用于检测头颈癌的肿瘤和转移淋巴结
IF 5.6 Q1 ONCOLOGY Pub Date : 2024-07-01 DOI: 10.1148/rycan.230178
Haley W White, Abdullah Bin Naveed, Benjamin R Campbell, Yu-Jin Lee, Fred M Baik, Michael Topf, Eben L Rosenthal, Marisa E Hom

In patients with head and neck cancer (HNC), surgical removal of cancerous tissue presents the best overall survival rate. However, failure to obtain negative margins during resection has remained a steady concern over the past 3 decades. The need for improved tumor removal and margin assessment presents an ongoing concern for the field. While near-infrared agents have long been used in imaging, investigation of these agents for use in HNC imaging has dramatically expanded in the past decade. Targeted tracers for use in primary and metastatic lymph node detection are of particular interest, with panitumumab-IRDye800 as a major candidate in current studies. This review aims to provide an overview of intraoperative near-infrared fluorescence-guided surgery techniques used in the clinical detection of malignant tissue and sentinel lymph nodes in HNC, highlighting current applications, limitations, and future directions for use of this technology within the field. Keywords: Molecular Imaging-Cancer, Fluorescence © RSNA, 2024.

在头颈癌(HNC)患者中,手术切除癌组织的总生存率最高。然而,在过去的 30 年中,切除过程中未能获得阴性边缘一直是一个令人担忧的问题。改进肿瘤切除和边缘评估的需求一直是该领域关注的问题。虽然近红外制剂在成像中的应用由来已久,但在过去十年中,用于 HNC 成像的近红外制剂的研究急剧增加。用于原发和转移淋巴结检测的靶向示踪剂尤其引人关注,帕尼单抗-IRDye800 是目前研究中的主要候选药物。本综述旨在概述用于临床检测HNC恶性组织和前哨淋巴结的术中近红外荧光引导手术技术,重点介绍该技术在该领域的当前应用、局限性和未来发展方向。关键词分子成像-癌症、荧光 © RSNA, 2024.
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Radiology. Imaging cancer
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