Literature DB >> 27317735

High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer.

Emilio Alba, Ana Lluch, Nuria Ribelles, Antonio Anton-Torres, Pedro Sanchez-Rovira, Joan Albanell, Lourdes Calvo, Jose Antonio Lopez García-Asenjo, Jose Palacios, Jose Ignacio Chacon, Amparo Ruiz, Juan De la Haba-Rodriguez, Miguel A Segui-Palmer, Beatriz Cirauqui, Mireia Margeli, Arrate Plazaola, Agusti Barnadas, Maribel Casas, Rosalia Caballero, Eva Carrasco, Federico Rojo.   

Abstract

Entities:  

Year:  2016        PMID: 27317735      PMCID: PMC4912359          DOI: 10.1634/theoncologist.2015-0312erratum

Source DB:  PubMed          Journal:  Oncologist        ISSN: 1083-7159


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2016;21:150–155; first published on January 19, 2016; http://dx.doi:10.1634/theoncologist.2015-0312 The online version of this article has been amended to correct errors that appear in Table 1 on page 152. A row was added to the “Treatment” section to provide missing MDH treatment data. In the “Histologic grade” section, the percentage noted for “GX” in the “pCR = no” column was incorrect; the correct percentage is 73.3. An errant comma in the “G3” heading was removed. For clarity, the heading of the last row of the table was changed to read “Ki67, median (range), %”.
  6 in total

1.  Clinical and pathological response to neoadjuvant chemotherapy with different chemotherapy regimens predicts the outcome of locally advanced breast cancer.

Authors:  Shicong Tang; Ke Wang; Kai Zheng; Jiadong Liu; Hengyu Zhang; Mingjian Tan; Hongwan Li; Huimeng Li; Xin Tan; Dequan Liu; Rong Guo
Journal:  Gland Surg       Date:  2020-10

2.  Thymosin beta 10 is a key regulator of tumorigenesis and metastasis and a novel serum marker in breast cancer.

Authors:  Xin Zhang; Dong Ren; Ling Guo; Lan Wang; Shu Wu; Chuyong Lin; Liping Ye; Jinrong Zhu; Jun Li; Libing Song; Huanxin Lin; Zhenyu He
Journal:  Breast Cancer Res       Date:  2017-02-08       Impact factor: 6.466

3.  Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.

Authors:  Jian Chen; Li Hao; Xiaojun Qian; Lin Lin; Yueyin Pan; Xinghua Han
Journal:  Front Immunol       Date:  2022-07-22       Impact factor: 8.786

Review 4.  Predictive and Prognostic Roles of Pathological Indicators for Patients with Breast Cancer on Neoadjuvant Chemotherapy.

Authors:  Xinyan Li; Mozhi Wang; Mengshen Wang; Xueting Yu; Jingyi Guo; Tie Sun; Litong Yao; Qiang Zhang; Yingying Xu
Journal:  J Breast Cancer       Date:  2019-11-04       Impact factor: 3.588

5.  Neoadjuvant Chemotherapy of Triple-Negative Breast Cancer: Evaluation of Early Clinical Response, Pathological Complete Response Rates, and Addition of Platinum Salts Benefit Based on Real-World Evidence.

Authors:  Milos Holanek; Iveta Selingerova; Ondrej Bilek; Tomas Kazda; Pavel Fabian; Lenka Foretova; Maria Zvarikova; Radka Obermannova; Ivana Kolouskova; Oldrich Coufal; Katarina Petrakova; Marek Svoboda; Alexandr Poprach
Journal:  Cancers (Basel)       Date:  2021-03-30       Impact factor: 6.639

6.  Association of Neo-Family History Score with pathological complete response, safety, and survival outcomes in patients with breast cancer receiving neoadjuvant platinum-based chemotherapy: An exploratory analysis of two prospective trials.

Authors:  Yaqian Xu; Yanping Lin; Yaohui Wang; Liheng Zhou; Shuguang Xu; Yifan Wu; Jing Peng; Jie Zhang; Wenjin Yin; Jinsong Lu
Journal:  EClinicalMedicine       Date:  2021-07-17
  6 in total

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