Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides

Arkadiusz Gertych , Żaneta Świderska-Chadaj , Zhaoxuan Ma , Nathan Ing , Tomasz Markiewicz , Szczepan Cierniak , Hootan Salemi , Samuel Guzman , Ann E. Walts , Beatrice S. Knudsen

Abstract

During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.

Author Arkadiusz Gertych - [Cedars-Sinai Medical Center]
Arkadiusz Gertych,,
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, Żaneta Świderska-Chadaj (FoEE / ITEEMIS)
Żaneta Świderska-Chadaj,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Zhaoxuan Ma - [Cedars-Sinai Medical Center]
Zhaoxuan Ma,,
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, Nathan Ing - [Cedars-Sinai Medical Center]
Nathan Ing,,
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, Tomasz Markiewicz (FoEE / ITEEMIS)
Tomasz Markiewicz,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Szczepan Cierniak - [Centralny Szpital Kliniczny Wojskowej Akademii Medycznej z Poliklinika]
Szczepan Cierniak,,
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, Hootan Salemi - [Cedars-Sinai Medical Center]
Hootan Salemi,,
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, Samuel Guzman - [Cedars-Sinai Medical Center]
Samuel Guzman,,
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, Ann E. Walts - [Cedars-Sinai Medical Center]
Ann E. Walts,,
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, Beatrice S. Knudsen - [Cedars-Sinai Medical Center]
Beatrice S. Knudsen,,
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Journal seriesScientific Reports, ISSN 2045-2322, (N/A 100 pkt)
Issue year2019
Vol9
ASJC Classification1000 Multidisciplinary
DOIDOI:10.1038/s41598-018-37638-9
Languageen angielski
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 19-12-2019, ArticleFromJournal
Publication indicators Scopus Citations = 6; WoS Citations = 3; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.401; WoS Impact Factor: 2018 = 4.011 (2) - 2018=4.525 (5)
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