Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters (vol 44, pg 21, 2019)

Lee Hyunjong

首页> 外文期刊>Clinical nuclear medicine >Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters (vol 44, pg 21, 2019)

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Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters (vol 44, pg 21, 2019)

机译：预测乳腺癌患者对Neoadjuvant化疗的反应：使用临床病理因素和FDG PET / CT纹理参数组合统计建模（Vol 44，PG 21,2019）

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《Clinical nuclear medicine》 |2021年第6期|共1页
作者
Lee Hyunjong;
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作者单位

Seoul Natl Univ Dept Mol Med &

Biopharmaceut Sci Grad Sch Convergence Sci &

Technol Seoul South;

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正文语种 eng
中图分类放射医学;
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1. A Predictive Model for Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy Using Machine Learning [J] . Isaac Kim, Kwanbum Lee, Seung Ah Lee, 乳腺癌（英文） . 2021,第004期
2. HER2 and topoisomerase Ⅱα : possible predictors of response to neoadjuvant chemotherapy for breast cancer patients [J] . ZHU Li, LI Ya-fen, CHEN Wei-guo, 中华医学杂志（英文版） . 2008,第020期
3. Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters [J] . Lee Hyunjong, Lee Dong-eun, Park Sohyun, Clinical nuclear medicine . 2019,第1期

机译：使用临床病理因子和FDG PET / CT纹理参数预测对乳腺癌患者乳腺癌联合统计建模患者的新辅助化疗的反应
4. 18FDG-PET/CT for predicting the outcome in ER+/HER2- breast cancer patients: comparison of clinicopathological parameters and PET image-derived indices including tumor texture analysis [J] . David Groheux, Antoine Martineau, Luis Teixeira, Breast Cancer Research . 2017,第1期

机译：18FDG-PET / CT预测ER + / HER2-乳腺癌患者的预后：比较临床病理参数和PET图像衍生指标，包括肿瘤纹理分析
5. The role of basal 18F-FDG PET/CT maximum standard uptake value and maximum standard uptake change in predicting pathological response in breast cancer patients receiving neoadjuvant chemotherapy [J] . Akdeniz Nadiye, Komek Halil, Kucukoner Mehmet, Nuclear Medicine Communications . 2021,第3期

机译：基础18F-FDG PET / CT最大标准摄取值和最大标准摄取变化预测接受Neoadjuvant化疗的乳腺癌患者病理反应的最大标准摄取变化
6. Preliminary Experiences of DBT (digital breast tomosynthesis) and Hybrid 18F-FDG-PET/MR (PETMR) for Neoadjuvant Chemotherapy (NAC) Cases in Breast Cancer [C] . Nachiko Uchiyama MD, Hiroaki Kurihara MD, Takayuki Kinoshita MD, International Workshop on Breast Imaging . 2018

机译：DBT（数字乳腺断层合成）和混合18F-FDG-PET / MR（PETMR）在乳腺癌新辅助化疗（NAC）病例中的初步经验
7. Combining biomarkers and clinicopathologic factors for prediction of response to adjuvant chemotherapy for breast cancer: Cox model and Support Vector Machine (SVM) methods. [D] . Liu, Xudong. 2010

机译：结合生物标志物和临床病理因素预测乳腺癌对辅助化疗的反应：Cox模型和支持向量机（SVM）方法。
8. 18FDG-PET/CT for predicting the outcome in ER+/HER2- breast cancer patients: comparison of clinicopathological parameters and PET image-derived indices including tumor texture analysis [O] . David Groheux, Antoine Martineau, Luis Teixeira, 2017

机译：18FDG-PET / CT预测ER + / HER2-乳腺癌患者的预后：比较临床病理参数和PET图像衍生指标包括肿瘤纹理分析
9. FDG-PET/CT for predicting the outcome in ER+/HER2- breast cancer patients: comparison of clinicopathological parameters and PET image-derived indices including tumor texture analysis [O] . 2017

机译：FDG-PET / CT预测ER + / HER2-乳腺癌患者的预后：比较临床病理参数和PET图像衍生指标，包括肿瘤纹理分析
10. P53 Mutation Analysis to Predict Tumor Response in Patients Undergoing Neoadjuvant Treatment for Locally Advanced Breast Cancer [R] . Carey, L. A. , Dorsey, K. C. , Dressler, L. , 2006

机译：p53突变分析预测局部晚期乳腺癌新辅助治疗患者的肿瘤反应

Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters (vol 44, pg 21, 2019)

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