日期 影响因子
日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:
–

Quantitative Ultrasound Texture Analysis of Breast Tumor Responses to Chemotherapy: Comparison of a Cart-Based and a Wireless Ultrasound Scanner

乳腺肿瘤化疗反应的定量超声纹理分析：基于推车式和无线超声扫描仪的比较

期刊:Journal of Imaging
影响因子:3.3
doi:10.3390/jimaging12030129
Alberico, David; Pena, Maria Lourdes Anzola; Osapoetra, Laurentius O; Sannachi, Lakshmanan; Yip, Joyce; Gandhi, Sonal; Wright, Frances; Oelze, Michael; Czarnota, Gregory J
肿瘤
肿瘤免疫
发表日期：2026
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Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results

定量超声成像预测乳腺癌新辅助化疗疗效并指导个体化治疗：随机II期临床试验结果

期刊:npj Precision Oncology
影响因子:8
doi:10.1038/s41698-025-01134-x
Moore-Palhares, Daniel; Alberico, David; Chan, Adrian Wai; DiCenzo, Daniel; Sannachi, Lakshmanan; Dasgupta, Archya; Yip, Joyce; Pena, Maria Lourdes Anzola; Gandhi, Sonal; Pezo, Rossanna; Eisen, Andrea; Jerzak, Katarzyna J; Gonzalez, Carlos A Carmona; Warner, Ellen; Wright, Frances C; Look-Hong, Nicole; Roberts, Amanda; Sadeghi-Naini, Ali; Curpen, Belinda; Skarpathiotakis, Mia; Betel, Carrie; Kolios, Michael C; Trudeau, Maureen; Czarnota, Gregory J
肿瘤
肿瘤免疫
乳腺癌
发表日期：2025
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Texture Analysis of Histology Images for Characterizing Ultrasound-Stimulated Microbubble Radiation Enhancement Treatment Response

利用组织学图像纹理分析表征超声刺激微泡放射增强治疗反应

期刊:Cells
影响因子:5.2
doi:10.3390/cells14242023
Sannachi, Lakshmanan; Mohabir, Serena; McNabb, Evan; Sharma, Deepa; Giles, Anoja; Yang, Wenyi; Leong, Kai Xuan; Stanisz, Martin; Czarnota, Gregory J
发表日期：2025
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ASMase Activation in Ultrasound-Stimulated Radiation Enhancement Using MRI-Guided Focused Ultrasound

利用磁共振引导聚焦超声增强超声刺激放射治疗中的ASMase激活

期刊:Cells
影响因子:5.2
doi:10.3390/cells14201618
Petchiny, Tera N; Sharma, Deepa; Giles, Anoja; Leong, Kai Xuan; Yang, Wenyi; Sannachi, Lakshmanan; Alberico, David; Czarnota, Gregory J
Mas
发表日期：2025
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Pre-Treatment Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using Intratumoral and Peritumoral Radiomics from T2-Weighted and Contrast-Enhanced T1-Weighted MRI

利用T2加权和对比增强T1加权MRI的肿瘤内和肿瘤周围放射组学数据，对乳腺癌新辅助化疗的反应进行治疗前预测

期刊:Cancers
影响因子:4.4
doi:10.3390/cancers17091520
Jang, Deok Hyun; Kolios, Christopher; Osapoetra, Laurentius O; Sannachi, Lakshmanan; Curpen, Belinda; Pejović-Milić, Ana; Czarnota, Gregory J
肿瘤
肿瘤免疫
乳腺癌
发表日期：2025
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A Priori Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using CT Radiomics

利用CT放射组学对乳腺癌新辅助化疗反应进行先验预测

期刊:Cancers
影响因子:4.4
doi:10.3390/cancers17162706
Jang, Deok Hyun; Osapoetra, Laurentius O; Sannachi, Lakshmanan; Curpen, Belinda; Pejović-Milić, Ana; Czarnota, Gregory J
肿瘤
肿瘤免疫
乳腺癌
发表日期：2025
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Hybrid Feature Selection for Predicting Chemotherapy Response in Locally Advanced Breast Cancer Using Clinical and CT Radiomics Features: Integration of Matrix Rank and Genetic Algorithm

基于临床和CT放射组学特征的混合特征选择方法预测局部晚期乳腺癌化疗反应：矩阵排序与遗传算法的融合

期刊:Cancers
影响因子:4.4
doi:10.3390/cancers17172738
Moslemi, Amir; Osapoetra, Laurentius Oscar; Safakish, Aryan; Sannachi, Lakshmanan; Alberico, David; Czarnota, Gregory J
肿瘤
肿瘤免疫
乳腺癌
发表日期：2025
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Radiomics Analysis of QUS Spectral Parametric Images for Predicting the Risk of Breast Cancer Recurrence

利用QUS光谱参数图像进行放射组学分析预测乳腺癌复发风险

期刊:Cancers
影响因子:4.4
doi:10.3390/cancers17233810
Osapoetra, Laurentius Oscar; Dinniwell, Graham; Anzola Pena, Maria Lourdes; Alberico, David; Sannachi, Lakshmanan; Czarnota, Gregory J
肿瘤
肿瘤免疫
乳腺癌
发表日期：2025
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Validation of a Quantitative Ultrasound Texture Analysis Model for Early Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: A Prospective Serial Imaging Study

定量超声纹理分析模型在乳腺癌新辅助化疗早期疗效预测中的验证：一项前瞻性连续影像学研究

期刊:Cancers
影响因子:4.4
doi:10.3390/cancers17152594
Moore-Palhares, Daniel; Sannachi, Lakshmanan; Chan, Adrian Wai; Dasgupta, Archya; DiCenzo, Daniel; Gandhi, Sonal; Pezo, Rossanna; Eisen, Andrea; Warner, Ellen; Wright, Frances; Look Hong, Nicole; Sadeghi-Naini, Ali; Skarpathiotakis, Mia; Curpen, Belinda; Betel, Carrie; Kolios, Michael C; Trudeau, Maureen; Czarnota, Gregory J
肿瘤
肿瘤免疫
乳腺癌
发表日期：2025
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A Priori Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Deep Features from Pre-Treatment MRI and CT

利用治疗前MRI和CT的深度特征对乳腺癌新辅助化疗反应进行先验预测

期刊:Cancers
影响因子:4.4
doi:10.3390/cancers17203394
Jang, Deok Hyun; Osapoetra, Laurentius O; Sannachi, Lakshmanan; Curpen, Belinda; Pejović-Milić, Ana; Czarnota, Gregory J
肿瘤
肿瘤免疫
乳腺癌
发表日期：2025
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