Analyse mathématique prédictive de l'efficacité de la chimiothérapie néoadjuvante chez les patientes ayant un cancer du sein

R. Rouzier; R. Incitti

doi:10.1007/s10269-006-0376-4

Analyse mathématique prédictive de l'efficacité de la chimiothérapie néoadjuvante chez les patientes ayant un cancer du sein

Translated title of the contribution: Mathematical analysis of predictivity of chemosensitivity in the neoadjuvant setting in breast cancer patients

R. Rouzier^*, R. Incitti

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

The concept of primary chemotherapy for patients with operable breast cancer is attractive because it provides the chemosensitivity of the tumour in vivo. Mathematical models, such as logistic regression, give the opportunity to determine chemosensitivity before any treatment by combining clinical and pathological data. Other models, such as machine learning (recursive partitoning, artificial neural network) provides concepts and can be improved by the integration of biological translational data.

Translated title of the contribution	Mathematical analysis of predictivity of chemosensitivity in the neoadjuvant setting in breast cancer patients
Original language	French
Pages (from-to)	227-233
Number of pages	7
Journal	Oncologie
Volume	8
Issue number	3
DOIs	https://doi.org/10.1007/s10269-006-0376-4
State	Published - Mar 2006
Externally published	Yes

Keywords

Artificial intelligence
Breast cancer
Chemosensitivity
Mathematical model
Neoadjuvant chemotherapy
Nomogram

ASJC Scopus subject areas

Oncology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/s10269-006-0376-4

Cite this

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title = "Analyse math{\'e}matique pr{\'e}dictive de l'efficacit{\'e} de la chimioth{\'e}rapie n{\'e}oadjuvante chez les patientes ayant un cancer du sein",

abstract = "The concept of primary chemotherapy for patients with operable breast cancer is attractive because it provides the chemosensitivity of the tumour in vivo. Mathematical models, such as logistic regression, give the opportunity to determine chemosensitivity before any treatment by combining clinical and pathological data. Other models, such as machine learning (recursive partitoning, artificial neural network) provides concepts and can be improved by the integration of biological translational data.",

keywords = "Artificial intelligence, Breast cancer, Chemosensitivity, Mathematical model, Neoadjuvant chemotherapy, Nomogram",

author = "R. Rouzier and R. Incitti",

year = "2006",

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doi = "10.1007/s10269-006-0376-4",

language = "French",

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AU - Incitti, R.

PY - 2006/3

Y1 - 2006/3

N2 - The concept of primary chemotherapy for patients with operable breast cancer is attractive because it provides the chemosensitivity of the tumour in vivo. Mathematical models, such as logistic regression, give the opportunity to determine chemosensitivity before any treatment by combining clinical and pathological data. Other models, such as machine learning (recursive partitoning, artificial neural network) provides concepts and can be improved by the integration of biological translational data.

AB - The concept of primary chemotherapy for patients with operable breast cancer is attractive because it provides the chemosensitivity of the tumour in vivo. Mathematical models, such as logistic regression, give the opportunity to determine chemosensitivity before any treatment by combining clinical and pathological data. Other models, such as machine learning (recursive partitoning, artificial neural network) provides concepts and can be improved by the integration of biological translational data.

KW - Artificial intelligence

KW - Breast cancer

KW - Chemosensitivity

KW - Mathematical model

KW - Neoadjuvant chemotherapy

KW - Nomogram

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DO - 10.1007/s10269-006-0376-4

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Analyse mathématique prédictive de l'efficacité de la chimiothérapie néoadjuvante chez les patientes ayant un cancer du sein

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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