Optimal Tumor Reduction Rate and Modalities for Predicting pCR in Women With Breast Cancer.


Journal

Anticancer research
ISSN: 1791-7530
Titre abrégé: Anticancer Res
Pays: Greece
ID NLM: 8102988

Informations de publication

Date de publication:
Apr 2020
Historique:
received: 10 02 2020
revised: 04 03 2020
accepted: 05 03 2020
entrez: 3 4 2020
pubmed: 3 4 2020
medline: 21 4 2020
Statut: ppublish

Résumé

To predict pCR during neoadjuvant chemotherapy is still difficult. The aim of this study was to evaluate the optimal tumor reduction rate and modalities for predicting pCR after two cycles of docetaxel. We analyzed 52 patients with HER2-positive or triple-negative breast cancer. The tumor reduction rate was evaluated after two 3-week cycles of docetaxel (plus trastuzumab for HER2-positive cancer patients). Patients without progression completed two additional cycles of docetaxel and four cycles of an anthracycline-containing regimen. Twenty-eight patients achieved pCR. The optimal tumor reduction rates for predicting pCR were 23, 39, 32, and 40% for US, caliper, MMG, and MRI measurements, respectively. The AUC was highest for caliper measurements. The optimal modality for predicting pCR differed among subtypes. Although tumor reduction rate after two cycles of chemotherapy is highly predictive of pCR, the optimal cutoff value differed among the modalities and breast cancer subtype.

Sections du résumé

BACKGROUND/AIM OBJECTIVE
To predict pCR during neoadjuvant chemotherapy is still difficult. The aim of this study was to evaluate the optimal tumor reduction rate and modalities for predicting pCR after two cycles of docetaxel.
PATIENTS AND METHODS METHODS
We analyzed 52 patients with HER2-positive or triple-negative breast cancer. The tumor reduction rate was evaluated after two 3-week cycles of docetaxel (plus trastuzumab for HER2-positive cancer patients). Patients without progression completed two additional cycles of docetaxel and four cycles of an anthracycline-containing regimen.
RESULTS RESULTS
Twenty-eight patients achieved pCR. The optimal tumor reduction rates for predicting pCR were 23, 39, 32, and 40% for US, caliper, MMG, and MRI measurements, respectively. The AUC was highest for caliper measurements. The optimal modality for predicting pCR differed among subtypes.
CONCLUSION CONCLUSIONS
Although tumor reduction rate after two cycles of chemotherapy is highly predictive of pCR, the optimal cutoff value differed among the modalities and breast cancer subtype.

Identifiants

pubmed: 32234930
pii: 40/4/2303
doi: 10.21873/anticanres.14196
doi:

Substances chimiques

Anthracyclines 0
Docetaxel 15H5577CQD
Receptor, ErbB-2 EC 2.7.10.1
Trastuzumab P188ANX8CK

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2303-2309

Informations de copyright

Copyright© 2020, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Auteurs

Sayaka Kuba (S)

Department of Surgery, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan skuba@nagasaki-u.ac.jp.

Shigeto Maeda (S)

Department of Surgery, National Hospital Organization Nagasaki Medical Center, Nagasaki, Japan.

Megumi Matsumoto (M)

Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan.

Kosho Yamanouchi (K)

Department of Surgery, National Hospital Organization Nagasaki Medical Center, Nagasaki, Japan.

Toru Iwata (T)

Department of Surgery, Nagasaki Rosai Hospital, Nagasaki, Japan.

Michi Morita (M)

Department of Surgery, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan.

Chika Sakimura (C)

Department of Surgery, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan.

Ryota Otsubo (R)

Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan.

Hiroshi Yano (H)

Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan.

Shuntaro Sato (S)

Clinical Research Center of Nagasaki University Hospital, Nagasaki, Japan.

Kengo Kanetaka (K)

Department of Surgery, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan.

Takeshi Nagayasu (T)

Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan.

Susumu Eguchi (S)

Department of Surgery, Nagasaki University Graduate School of Biomedical Science, Nagasaki, Japan.

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Classifications MeSH