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Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone
High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8476598/ https://www.ncbi.nlm.nih.gov/pubmed/34580357 http://dx.doi.org/10.1038/s41598-021-98480-0 |
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author | Laury, Anna Ray Blom, Sami Ropponen, Tuomas Virtanen, Anni Carpén, Olli Mikael |
author_facet | Laury, Anna Ray Blom, Sami Ropponen, Tuomas Virtanen, Anni Carpén, Olli Mikael |
author_sort | Laury, Anna Ray |
collection | PubMed |
description | High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic or predictive information, which is in sharp contrast to almost all other carcinoma types. Deep-learning based image analysis has recently been able to predict outcome and/or identify morphology-based representations of underlying molecular alterations in other tumor types, such as colorectal carcinoma, lung carcinoma, breast carcinoma, and melanoma. Using a carefully stratified HGSC patient cohort consisting of women (n = 30) with similar presentations who experienced very different treatment responses (platinum free intervals of either ≤ 6 months or ≥ 18 months), we used whole slide images (WSI, n = 205) to train a convolutional neural network. The neural network was trained, in three steps, to identify morphologic regions (digital biomarkers) that are highly associating with one or the other treatment response group. We tested the classifier using a separate 22 slide test set, and 18/22 slides were correctly classified. We show that a neural network based approach can discriminate extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%). These proof-of-concept results are novel, because for the first time, prospective prognostic information is identified specifically within HGSC tumor morphology. |
format | Online Article Text |
id | pubmed-8476598 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84765982021-09-29 Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone Laury, Anna Ray Blom, Sami Ropponen, Tuomas Virtanen, Anni Carpén, Olli Mikael Sci Rep Article High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic or predictive information, which is in sharp contrast to almost all other carcinoma types. Deep-learning based image analysis has recently been able to predict outcome and/or identify morphology-based representations of underlying molecular alterations in other tumor types, such as colorectal carcinoma, lung carcinoma, breast carcinoma, and melanoma. Using a carefully stratified HGSC patient cohort consisting of women (n = 30) with similar presentations who experienced very different treatment responses (platinum free intervals of either ≤ 6 months or ≥ 18 months), we used whole slide images (WSI, n = 205) to train a convolutional neural network. The neural network was trained, in three steps, to identify morphologic regions (digital biomarkers) that are highly associating with one or the other treatment response group. We tested the classifier using a separate 22 slide test set, and 18/22 slides were correctly classified. We show that a neural network based approach can discriminate extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%). These proof-of-concept results are novel, because for the first time, prospective prognostic information is identified specifically within HGSC tumor morphology. Nature Publishing Group UK 2021-09-27 /pmc/articles/PMC8476598/ /pubmed/34580357 http://dx.doi.org/10.1038/s41598-021-98480-0 Text en © The Author(s) 2021 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Laury, Anna Ray Blom, Sami Ropponen, Tuomas Virtanen, Anni Carpén, Olli Mikael Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone |
title | Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone |
title_full | Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone |
title_fullStr | Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone |
title_full_unstemmed | Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone |
title_short | Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone |
title_sort | artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8476598/ https://www.ncbi.nlm.nih.gov/pubmed/34580357 http://dx.doi.org/10.1038/s41598-021-98480-0 |
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