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Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer
Ovarian cancer is the leading cause of gynecologic cancer death among women. Regardless of the development made in the past two decades in the surgery and chemotherapy of ovarian cancer, most of the advanced-stage patients are with recurrent cancer and die. The conventional treatment for ovarian can...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795433/ https://www.ncbi.nlm.nih.gov/pubmed/35087101 http://dx.doi.org/10.1038/s41597-022-01127-6 |
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author | Wang, Ching-Wei Chang, Cheng-Chang Khalil, Muhammad Adil Lin, Yi-Jia Liou, Yi-An Hsu, Po-Chao Lee, Yu-Ching Wang, Chih-Hung Chao, Tai-Kuang |
author_facet | Wang, Ching-Wei Chang, Cheng-Chang Khalil, Muhammad Adil Lin, Yi-Jia Liou, Yi-An Hsu, Po-Chao Lee, Yu-Ching Wang, Chih-Hung Chao, Tai-Kuang |
author_sort | Wang, Ching-Wei |
collection | PubMed |
description | Ovarian cancer is the leading cause of gynecologic cancer death among women. Regardless of the development made in the past two decades in the surgery and chemotherapy of ovarian cancer, most of the advanced-stage patients are with recurrent cancer and die. The conventional treatment for ovarian cancer is to remove cancerous tissues using surgery followed by chemotherapy, however, patients with such treatment remain at great risk for tumor recurrence and progressive resistance. Nowadays, new treatment with molecular-targeted agents have become accessible. Bevacizumab as a monotherapy in combination with chemotherapy has been recently approved by FDA for the treatment of epithelial ovarian cancer (EOC). Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors’ best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for EOC and peritoneal serous papillary carcinoma (PSPC). This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with EOC and PSPC to bevacizumab. |
format | Online Article Text |
id | pubmed-8795433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87954332022-02-07 Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer Wang, Ching-Wei Chang, Cheng-Chang Khalil, Muhammad Adil Lin, Yi-Jia Liou, Yi-An Hsu, Po-Chao Lee, Yu-Ching Wang, Chih-Hung Chao, Tai-Kuang Sci Data Data Descriptor Ovarian cancer is the leading cause of gynecologic cancer death among women. Regardless of the development made in the past two decades in the surgery and chemotherapy of ovarian cancer, most of the advanced-stage patients are with recurrent cancer and die. The conventional treatment for ovarian cancer is to remove cancerous tissues using surgery followed by chemotherapy, however, patients with such treatment remain at great risk for tumor recurrence and progressive resistance. Nowadays, new treatment with molecular-targeted agents have become accessible. Bevacizumab as a monotherapy in combination with chemotherapy has been recently approved by FDA for the treatment of epithelial ovarian cancer (EOC). Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors’ best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for EOC and peritoneal serous papillary carcinoma (PSPC). This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with EOC and PSPC to bevacizumab. Nature Publishing Group UK 2022-01-27 /pmc/articles/PMC8795433/ /pubmed/35087101 http://dx.doi.org/10.1038/s41597-022-01127-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Wang, Ching-Wei Chang, Cheng-Chang Khalil, Muhammad Adil Lin, Yi-Jia Liou, Yi-An Hsu, Po-Chao Lee, Yu-Ching Wang, Chih-Hung Chao, Tai-Kuang Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer |
title | Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer |
title_full | Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer |
title_fullStr | Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer |
title_full_unstemmed | Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer |
title_short | Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer |
title_sort | histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795433/ https://www.ncbi.nlm.nih.gov/pubmed/35087101 http://dx.doi.org/10.1038/s41597-022-01127-6 |
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