Literature DB >> 28325260

Imaging performance in guiding response to neoadjuvant therapy according to breast cancer subtypes: A systematic literature review.

Melanie A Lindenberg1, Anna Miquel-Cases2, Valesca P Retèl3, Gabe S Sonke4, Jelle Wesseling5, Marcel P M Stokkel6, Wim H van Harten7.   

Abstract

Monitoring therapeutic response to neoadjuvant chemotherapy(NAC) is likely to improve NAC effectiveness in breast cancer(BC). Imaging performance seems to vary per tumour subtype(by ER and HER2 status), therefore we performed a systematic review on subtype specific imaging performance in monitoring NAC in BC. Studies examining imaging performance in predicting pathologic complete response(pCR) during NAC in BC subtypes were selected. Per study, negative- and positive predictive value, sensitivity(se) and specificity(sp), AUC and accuracy were derived. Fifteen/106 articles were included. Inter-study variability was revealed in: monitoring interval, response and pCR definitions. In ER-positive/HER2-negative BC, 181F FDG-PET/CT showed se/sp of 38%-89%/74%-100%, MRI showed se/sp of 35%-37%/87%-89%. In triple negative BC, 181F FDG-PET/CT showed se/sp of 0%-79%/95%-100%. 181F FDG-PET/CT showed in ER-positive/HER2-positive BC se/sp of 59%/80% and in ER-negative/HER2-positive 27%/88%. Evidence on imaging performance in monitoring NAC according BC subtypes is lacking. Consensus should be reached in: definitions of pCR, response and monitoring interval before starting well-designed studies.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Breast cancer; ER; HER2; Imaging; Neoadjuvant therapy; Therapeutic response

Mesh:

Substances:

Year:  2017        PMID: 28325260     DOI: 10.1016/j.critrevonc.2017.02.014

Source DB:  PubMed          Journal:  Crit Rev Oncol Hematol        ISSN: 1040-8428            Impact factor:   6.312


  10 in total

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Authors:  Gregory Boverman; Cynthia E L Davis; Shireen D Geimer; Paul M Meaney
Journal:  IEEE J Electromagn RF Microw Med Biol       Date:  2017-12-27

Review 2.  MRI Performance in Detecting pCR After Neoadjuvant Chemotherapy by Molecular Subtype of Breast Cancer.

Authors:  Nancy Yu; Vivian W Y Leung; Sarkis Meterissian
Journal:  World J Surg       Date:  2019-09       Impact factor: 3.352

3.  Bioinformatic analysis of differential expression and core GENEs in breast cancer.

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Journal:  Int J Clin Exp Pathol       Date:  2018-03-01

Review 4.  Aptamer selection and applications for breast cancer diagnostics and therapy.

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5.  Direct comparison of PET/CT and MRI to predict the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis.

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Journal:  Sci Rep       Date:  2017-08-16       Impact factor: 4.379

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Authors:  Isabelle Veys; Catalin-Florin Pop; Romain Barbieux; Michel Moreau; Danielle Noterman; Filip De Neubourg; Jean-Marie Nogaret; Gabriel Liberale; Denis Larsimont; Pierre Bourgeois
Journal:  PLoS One       Date:  2018-05-25       Impact factor: 3.240

7.  Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

Authors:  Carmen Herrero Vicent; Xavier Tudela; Paula Moreno Ruiz; Víctor Pedralva; Ana Jiménez Pastor; Daniel Ahicart; Silvia Rubio Novella; Isabel Meneu; Ángela Montes Albuixech; Miguel Ángel Santamaria; María Fonfria; Almudena Fuster-Matanzo; Santiago Olmos Antón; Eduardo Martínez de Dueñas
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Authors:  Juliana M Mello; Flavia Sarvacinski; Flavia C Schaefer; Daniel S Ercolani; Nathalia R Lobato; Yasmine C Martins; Guilherme Zwetsch; Fernando P Bittelbrunn; Charles F Ferreira; Andrea P Damin
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9.  A plasma-derived extracellular vesicle mRNA classifier for the detection of breast cancer.

Authors:  Geng-Xi Cai; Li Lin; Xiang-Ming Zhai; Zhi-Wei Guo; Ying-Song Wu; Guo-Lin Ye; Qing Liu; Lu-Shi Chen; Guan-Yu Xing; Qiao-Hong Zhao; Li-Ling Tang; Shun-He Mai; Bo-Jian Ye
Journal:  Gland Surg       Date:  2021-06

10.  Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response.

Authors:  Lal Hussain; Pauline Huang; Tony Nguyen; Kashif J Lone; Amjad Ali; Muhammad Salman Khan; Haifang Li; Doug Young Suh; Tim Q Duong
Journal:  Biomed Eng Online       Date:  2021-06-28       Impact factor: 2.819

  10 in total

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