Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
Abstract The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that com...
Enregistré dans:
Auteurs principaux: | Sunghoon Joo, Eun Sook Ko, Soonhwan Kwon, Eunjoo Jeon, Hyungsik Jung, Ji-Yeon Kim, Myung Jin Chung, Young-Hyuck Im |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/89bbfdeddf364266aeeb4cea1e90b29a |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
A Study on Clinical and Pathological Responses to Neoadjuvant Chemotherapy in Breast Carcinoma
par: Kunnuru SKR, et autres
Publié: (2020) -
Molecular Biomarkers Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer Patients: Review
par: Ana Julia Aguiar de Freitas, et autres
Publié: (2021) -
Elevated Level of Nerve Growth Factor (NGF) in Serum-Derived Exosomes Predicts Poor Survival in Patients with Breast Cancer Undergoing Neoadjuvant Chemotherapy
par: Hae Hyun Jung, et autres
Publié: (2021) -
Criteria for identifying residual tumours after neoadjuvant chemotherapy of breast cancers: a magnetic resonance imaging study
par: Yunju Kim, et autres
Publié: (2021) -
Pathological Complete Response to Neoadjuvant Chemotherapy in a Patient with HER2-Positive Squamous Cell Carcinoma of the Breast
par: Yuki Usui, et autres
Publié: (2021)