Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer.
BACKGROUND:Personalized treatment for cancer patients is a hot topic of debate, particularly the decision to initiate chemotherapy in patients with Estrogen receptor (ER)-positive, HER2-negative tumors in the early stages of breast cancer (BC). Owing to significant advancements in information techno...
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doaj-08bb012903e747ee8246ebe56f1bd4072020-11-25T01:36:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01137e020010010.1371/journal.pone.0200100Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer.Yun Yeong KimSe Jeong OhYong Soon ChunWoon Kee LeeHeung Kyu ParkBACKGROUND:Personalized treatment for cancer patients is a hot topic of debate, particularly the decision to initiate chemotherapy in patients with Estrogen receptor (ER)-positive, HER2-negative tumors in the early stages of breast cancer (BC). Owing to significant advancements in information technology (IT) and genomics, clinicians are increasingly attaining therapeutic goals rapidly and safely by effectively differentiating patient subsets that require chemotherapy. IBM Watson for Oncology (WFO) is a cognitive computing system employed by clinicians to provide evidence-based treatment options for cancer. WFO aids in clinical diagnosis, with claims that it may be superior in performance to human clinicians. The current study was based on the hypothesis that WFO alone cannot effectively determine whether or not chemotherapy is essential for the subset of ER-positive, HER2-negative BC patients. PATIENTS AND METHODS:From December 2015 to July 2017, 95 patients with ER-positive, HER2- negative BC subjected to treatment were retrospectively examined using WFO, and outputs compared to real clinical practice. Treatment options were suggested by WFO, and WFO recommendations calculated both with and without data from the gene expression assay (GEA). RESULTS:WFO without GEA was unable to determine the groups of patients that did not require chemotherapy. Concordant therapeutic recommendations between real clinical practice and WFO without GEA were obtained for 23.2% of the patient group. On the other hand, the results of WFO with GEA showed good clinical applicability. Sensitivity, specificity, positive predictive and negative predictive values of WFO with GEA were 100%, 80%, 61% and 100%, respectively. CONCLUSIONS:Our collective findings indicate that WFO without the gene expression assay has limited clinical utility.http://europepmc.org/articles/PMC6034851?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yun Yeong Kim Se Jeong Oh Yong Soon Chun Woon Kee Lee Heung Kyu Park |
spellingShingle |
Yun Yeong Kim Se Jeong Oh Yong Soon Chun Woon Kee Lee Heung Kyu Park Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. PLoS ONE |
author_facet |
Yun Yeong Kim Se Jeong Oh Yong Soon Chun Woon Kee Lee Heung Kyu Park |
author_sort |
Yun Yeong Kim |
title |
Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. |
title_short |
Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. |
title_full |
Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. |
title_fullStr |
Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. |
title_full_unstemmed |
Gene expression assay and Watson for Oncology for optimization of treatment in ER-positive, HER2-negative breast cancer. |
title_sort |
gene expression assay and watson for oncology for optimization of treatment in er-positive, her2-negative breast cancer. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2018-01-01 |
description |
BACKGROUND:Personalized treatment for cancer patients is a hot topic of debate, particularly the decision to initiate chemotherapy in patients with Estrogen receptor (ER)-positive, HER2-negative tumors in the early stages of breast cancer (BC). Owing to significant advancements in information technology (IT) and genomics, clinicians are increasingly attaining therapeutic goals rapidly and safely by effectively differentiating patient subsets that require chemotherapy. IBM Watson for Oncology (WFO) is a cognitive computing system employed by clinicians to provide evidence-based treatment options for cancer. WFO aids in clinical diagnosis, with claims that it may be superior in performance to human clinicians. The current study was based on the hypothesis that WFO alone cannot effectively determine whether or not chemotherapy is essential for the subset of ER-positive, HER2-negative BC patients. PATIENTS AND METHODS:From December 2015 to July 2017, 95 patients with ER-positive, HER2- negative BC subjected to treatment were retrospectively examined using WFO, and outputs compared to real clinical practice. Treatment options were suggested by WFO, and WFO recommendations calculated both with and without data from the gene expression assay (GEA). RESULTS:WFO without GEA was unable to determine the groups of patients that did not require chemotherapy. Concordant therapeutic recommendations between real clinical practice and WFO without GEA were obtained for 23.2% of the patient group. On the other hand, the results of WFO with GEA showed good clinical applicability. Sensitivity, specificity, positive predictive and negative predictive values of WFO with GEA were 100%, 80%, 61% and 100%, respectively. CONCLUSIONS:Our collective findings indicate that WFO without the gene expression assay has limited clinical utility. |
url |
http://europepmc.org/articles/PMC6034851?pdf=render |
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