Cargando…
Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning
The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10345095/ https://www.ncbi.nlm.nih.gov/pubmed/37443117 http://dx.doi.org/10.1038/s41523-023-00562-x |
_version_ | 1785073009416994816 |
---|---|
author | Ding, Yan Yang, Fan Han, Mengxue Li, Chunhui Wang, Yanan Xu, Xin Zhao, Min Zhao, Meng Yue, Meng Deng, Huiyan Yang, Huichai Yao, Jianhua Liu, Yueping |
author_facet | Ding, Yan Yang, Fan Han, Mengxue Li, Chunhui Wang, Yanan Xu, Xin Zhao, Min Zhao, Meng Yue, Meng Deng, Huiyan Yang, Huichai Yao, Jianhua Liu, Yueping |
author_sort | Ding, Yan |
collection | PubMed |
description | The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high. |
format | Online Article Text |
id | pubmed-10345095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103450952023-07-15 Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning Ding, Yan Yang, Fan Han, Mengxue Li, Chunhui Wang, Yanan Xu, Xin Zhao, Min Zhao, Meng Yue, Meng Deng, Huiyan Yang, Huichai Yao, Jianhua Liu, Yueping NPJ Breast Cancer Article The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high. Nature Publishing Group UK 2023-07-13 /pmc/articles/PMC10345095/ /pubmed/37443117 http://dx.doi.org/10.1038/s41523-023-00562-x Text en © The Author(s) 2023 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/) . |
spellingShingle | Article Ding, Yan Yang, Fan Han, Mengxue Li, Chunhui Wang, Yanan Xu, Xin Zhao, Min Zhao, Meng Yue, Meng Deng, Huiyan Yang, Huichai Yao, Jianhua Liu, Yueping Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning |
title | Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning |
title_full | Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning |
title_fullStr | Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning |
title_full_unstemmed | Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning |
title_short | Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning |
title_sort | multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10345095/ https://www.ncbi.nlm.nih.gov/pubmed/37443117 http://dx.doi.org/10.1038/s41523-023-00562-x |
work_keys_str_mv | AT dingyan multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT yangfan multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT hanmengxue multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT lichunhui multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT wangyanan multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT xuxin multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT zhaomin multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT zhaomeng multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT yuemeng multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT denghuiyan multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT yanghuichai multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT yaojianhua multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning AT liuyueping multicenterstudyonpredictingbreastcancerlymphnodestatusfromcoreneedlebiopsyspecimensusingmultimodalandmultiinstancedeeplearning |