Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone

Abstract 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 p...

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Autores principales:	Anna Ray Laury, Sami Blom, Tuomas Ropponen, Anni Virtanen, Olli Mikael Carpén
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dec4332aac824453a0d7dbb9e180088a
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spelling	oai:doaj.org-article:dec4332aac824453a0d7dbb9e180088a2021-12-02T18:51:35ZArtificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone10.1038/s41598-021-98480-02045-2322https://doaj.org/article/dec4332aac824453a0d7dbb9e180088a2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98480-0https://doaj.org/toc/2045-2322Abstract 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.Anna Ray LaurySami BlomTuomas RopponenAnni VirtanenOlli Mikael CarpénNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anna Ray Laury Sami Blom Tuomas Ropponen Anni Virtanen Olli Mikael Carpén Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone
description	Abstract 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	article
author	Anna Ray Laury Sami Blom Tuomas Ropponen Anni Virtanen Olli Mikael Carpén
author_facet	Anna Ray Laury Sami Blom Tuomas Ropponen Anni Virtanen Olli Mikael Carpén
author_sort	Anna Ray Laury
title	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_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_sort	artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dec4332aac824453a0d7dbb9e180088a
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Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone

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