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

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http://hdl.handle.net/10138/336881

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Laury , A R , Blom , S , Ropponen , T , Virtanen , A & Carpen , O M 2021 , ' Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone ' , Scientific Reports , vol. 11 , no. 1 , 19165 . https://doi.org/10.1038/s41598-021-98480-0

Title: Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone
Author: Laury, Anna Ray; Blom, Sami; Ropponen, Tuomas; Virtanen, Anni; Carpen, Olli Mikael
Contributor organization: Research Program in Systems Oncology
Research Programs Unit
HUSLAB
HUS Diagnostic Center
Department of Pathology
Precision Cancer Pathology
Olli Mikael Carpen / Principal Investigator
Digital Precision Cancer Medicine (iCAN)
Date: 2021-09-27
Language: eng
Number of pages: 9
Belongs to series: Scientific Reports
ISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-021-98480-0
URI: http://hdl.handle.net/10138/336881
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 = 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.
Subject: LONG-TERM SURVIVORS
OVARIAN-CANCER
CLINICOPATHOLOGICAL FEATURES
MUTATION-STATUS
WOMEN
PATTERNS
BRCA1
3111 Biomedicine
3122 Cancers
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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