@inproceedings{52aaa8c0e9594563a4ae979900e0cb2a,
title = "Radiomics for predicting response to neoadjuvant chemotherapy treatment in breast cancer",
abstract = "Women who are diagnosed with breast cancer are referred to Neoadjuvant Chemotherapy Treatment (NACT) before surgery when treatment guidelines indicate that. Achieving complete response in this treatment is correlated with improved overall survival compared with those experiencing a partial or no response at all. In this paper, we explore multi modal clinical and radiomics metrics including quantitative features from medical imaging, to assess in advance complete response to NACT. Our dataset consists of a cohort from Institut Curie with 1383 patients; from which 528 patients have mammogram imaging. We analyze the data via image processing, machine learning and deep learning algorithms to increase the set of discriminating features and create effective models. Our results show ability to classify the data in this problem settings, using the clinical data. We then show the possible improvement we may achieve in combining clinical and mammogram data measured by the AUC, sensitivity and specificity. We show that for our cohort the overall model achieves sensitivity 0.954 while keeping good specificity of 0.222. This means that almost all patients that achieved pathologic complete response will also be correctly classified by our model. At the same time, for 22% of the patients, the model could correctly predict in advance that they won't achieve pathologic complete response, enabling them to reassess in advance this treatment. We also describe our system architecture that includes the Biomedical Framework, a platform to create configurable reusable pipelines and expose them as micro-services on-premise or in-thecloud.",
keywords = "Artificial Intelligence, Big Data, Breast Cancer, Deep Learning, Machine Learning, Neoadjuvant Chemotherapy Treatment, Neoadjuvant Treatment, Radiomics",
author = "Simona Rabinovici-Cohen and Tal Tlusty and Ami Abutbul and Kari Antila and Xos{\'e} Fernandez and {Grandal Rejo}, Beatriz and Efrat Hexter and {Hijano Cubelos}, Oliver and Abed Khateeb and Juha Pajula and Shaked Perek",
note = "Funding Information: We thank Johan Archinard from Institut Curie for his dedicated continuous support with the IT infrastructure for this pilot. We thank Prof. Fabien Reyal, Dr. Caroline Malhaire and Dr. Anne Sophie Hamy-Petit from Institut Curie for defining the clinical use case, share their experience and help understanding the data. Research reported in this publication was partially supported by European Union{\textquoteright}s Horizon 2020 research and innovation program under grant agreement No 780495. The authors are solely responsible for the content of this paper. It does not represent the opinion of the European Union, and the European Union is not responsible for any use that might be made of data appearing therein. Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Medical Imaging 2020 : Imaging Informatics for Healthcare, Research, and Applications ; Conference date: 16-02-2020 Through 17-02-2020",
year = "2020",
doi = "10.1117/12.2551374",
language = "English",
isbn = "978-1-5106-3403-9",
series = "Progress in Biomedical Optics and Imaging",
publisher = "International Society for Optics and Photonics SPIE",
number = "55",
editor = "Po-Hao Chen and Deserno, {Thomas M.}",
booktitle = "Medical Imaging 2020",
address = "United States",
}
