Standardized classification of lung adenocarcinoma subtypes and improvement of grading assessment through deep learning

Lami, Kris, Ota, Noriaki, Yamaoka, Shinsuke, Bychkov, Andrey, Matsumoto, Keitaro, Uegami, Wataru, Munkhdelger, Jijgee, Seki, Kurumi, Sukhbaatar, Odsuren, Attanoos, Richard, Berezowska, Sabina, Brcic, Luka, Cavazza, Alberto, English, John C., Fabro, Alexandre Todorovic, Ishida, Kaori, Kashima, Yukio, Kitamura, Yuka, Larsen, Brandon T., Marchevsky, Alberto M., Miyazaki, Takuro, Morimoto, Shimpei, Ozasa, Mutsumi, Roden, Anja C., Schneider, Frank, Smith, Maxwell L., Tabata, Kazuhiro, Takano, Angela M., Tanaka, Tomonori, Tsuchiya, Tomoshi, Nagayasu, Takeshi, Sakanashi, Hidenori and Fukuoka, Junya 2023. Standardized classification of lung adenocarcinoma subtypes and improvement of grading assessment through deep learning. The American Journal of Pathology 12 , pp. 2066-2079. 10.1016/j.ajpath.2023.07.002 Item availability restricted.

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Abstract

The histopathological distinction of lung adenocarcinoma (LADC) subtypes is subject to high inter-observer variability, which can compromise the optimal assessment of the patient prognosis. Therefore, this study developed convolutional neural networks (CNNs) capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathological images were obtained from seventeen expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1-scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained CNNs improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	0002-9440
Date of First Compliant Deposit:	8 August 2023
Date of Acceptance:	12 July 2023
Last Modified:	23 Nov 2023 08:22
URI:	https://orca.cardiff.ac.uk/id/eprint/161515

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